diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/3DMGAME-The Evil Within Update 2 (v1 03) And Crack-3DM How to Install and Play the Latest Version.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/3DMGAME-The Evil Within Update 2 (v1 03) And Crack-3DM How to Install and Play the Latest Version.md
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-<h1>3DMGAME-The Evil Within Update 2 (v1 03) And Crack-3DM</h1>
-<p>If you are a fan of horror games, you might have heard of The Evil Within, a survival horror game developed by Tango Gameworks and published by Bethesda Softworks in 2014. The game follows the story of Detective Sebastian Castellanos, who is trapped in a nightmarish world after witnessing a gruesome mass murder at a mental hospital. The game features a third-person perspective, stealth elements, limited resources, and various enemies and traps that will test your skills and nerves.</p>
-<p>But if you want to enjoy the game to its fullest potential, you might want to check out the latest update and crack from 3DMGAME, a Chinese group of hackers and modders who specialize in cracking games and releasing updates. In this article, we will tell you everything you need to know about 3DMGAME-The Evil Within Update 2 (v1 03) And Crack-3DM, including how to download and install it, what's new in it, and some FAQs.</p>
-<h2>3DMGAME-The Evil Within Update 2 (v1 03) And Crack-3DM</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt; <a href="https://byltly.com/2uKwpN">https://byltly.com/2uKwpN</a></b></p><br /><br />
-<h2>Introduction</h2>
-<h3>What is The Evil Within?</h3>
-<p>The Evil Within is a survival horror game that was released in October 2014 for Windows, PlayStation 3, PlayStation 4, Xbox 360, and Xbox One. The game was directed by Shinji Mikami, the creator of the Resident Evil series, and was his first project since leaving Capcom in 2010. The game received generally positive reviews from critics and players, who praised its atmosphere, story, gameplay, and graphics, but also criticized its technical issues, difficulty spikes, and lack of originality.</p>
-<h3>What is 3DMGAME?</h3>
-<p>3DMGAME is a Chinese group of hackers and modders who are known for cracking games and releasing updates for them. They have cracked many popular games such as Grand Theft Auto V, FIFA 15, Dragon Age: Inquisition, Far Cry 4, Assassin's Creed Unity, and more. They also provide tutorials, guides, tools, mods, cheats, trainers, and patches for various games on their website.</p>
-<h3>What is Update 2 (v1 03) And Crack-3DM?</h3>
-<p>Update 2 (v1 03) And Crack-3DM is the latest update and crack for The Evil Within that was released by 3DMGAME on November 27th, 2014. It is a standalone update that does not require any previous updates or cracks to work. It fixes some bugs and glitches in the game, improves the gameplay, graphics, and audio quality, adds new difficulty settings for Casual and Survival modes, and allows you to play the game without any DRM or online activation.</p>
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-<h2>How to Download and Install Update 2 (v1 03) And Crack-3DM</h2>
-<h3>Requirements</h3>
-<p>Before you download and install Update 2 (v1 03) And Crack-3DM, you need to make sure that you have the following requirements:</p>
-<ul>
-<li>A PC with Windows Vista/7/8/8.1 (64-bit)</li>
-<li>A minimum of 4 GB of RAM</li>
-<li>A minimum of 50 GB of free disk space</li>
-<li>A DirectX 11 compatible graphics card with at least 1 GB of VRAM</li>
-<li>A DirectX compatible sound card</li>
-<li>A keyboard and mouse or a compatible gamepad</li>
-<li>A copy of The Evil Within installed on your PC</li>
-</ul>
-<h3>Steps</h3>
-<p>Once you have met the requirements, you can follow these steps to download and install Update 2 (v1 03) And Crack-3DM:</p>
-<ol>
-<li>Download the update and crack files from <a href="http://www.3dmgame.com/games/theevilwithin/">the official website of 3DMGAME</a>. You will need a torrent client such as uTorrent or BitTorrent to download them.</li>
-<li>Extract the files to your game directory using a program such as WinRAR or 7-Zip. You can find your game directory by right-clicking on the shortcut of The Evil Within on your desktop or in your Start menu and selecting Properties. Then click on Open File Location.</li>
-<li>Run the game as administrator by right-clicking on the executable file (TheEvilWithin.exe) in your game directory and selecting Run as administrator.</li>
-<li>Enjoy the new features and fixes in Update 2 (v1 03) And Crack-3DM.</li>
-</ol>
-<h2>What's New in Update 2 (v1 03) And Crack-3DM</h2>
-<h3>Gameplay Improvements</h3>
-<h4>Added difficulty settings for Casual and Survival modes</h4>
-<p>Update 2 (v1 03) And Crack-3DM adds two new difficulty settings for Casual and Survival modes: Easy Mode and Nightmare Mode. Easy Mode reduces the damage taken from enemies and traps, increases the amount of ammo and health items available, lowers the enemy AI level, disables some enemy attacks, removes some enemies from certain areas, allows you to use autosave checkpoints more frequently, gives you more time to escape from grab attacks or stealth kills, lets you carry more matches and syringes at once and enables aim assist. Nightmare Mode increases the damage taken from enemies and traps, decreases the amount of ammo and health items available, raises the enemy AI level, enables all enemy attacks, adds more enemies to certain areas, limits you to manual saving only, gives you less time to escape from grab attacks or stealth kills, lets you carry fewer matches and syringes at once and disables aim assist.</p>
-<h4>Fixed some bugs and glitches</h4>
-<p>Update 2 (v1 03) And Crack-3DM fixes some bugs and glitches that were present in the original version of the game or previous updates. Some of these fixes include:</p>
-<ul>
-<li>Fixed an issue where Sebastian would get stuck in certain objects or walls.</li>
-<li>Fixed an issue where Sebastian would fall through the floor or out of bounds.</li>
-<li>Fixed an issue where Sebastian would die instantly from certain attacks or traps.</li>
-<li>Fixed an issue where Sebastian would lose health or stamina without any apparent reason.</li>
-<li>Fixed an issue where Sebastian would not be able to interact with certain objects or items.</li>
-<li>Fixed an issue where Sebastian would not be able to use certain weapons or abilities.</li>
-<li>Fixed an issue where Sebastian would not be able to reload his weapons properly.</li>
-<li>Fixed an issue where Sebastian would not be able to aim his weapons accurately.</li>
-<li>Fixed an issue where Sebastian would not be able to see his crosshair or HUD elements.</li>
-<li>Fixed an issue where Sebastian would not be able to hear certain sounds or voices.</li>
-<li>Fixed an issue where Sebastian would not be able to save or load his progress correctly.</li>
-<li>Fixed an issue where Sebastian would not be able to change his settings or options properly.</li>
- <li>Fixed an issue where Sebastian would experience crashes or freezes during gameplay or cutscenes.</li>
- </ul>
- <h4>Enhanced performance and stability</h4>
- <p>Update 2 (v1 03) And Crack-3DM enhances the performance and stability of the game by optimizing the code and resources used by the game engine. This results in faster loading times, smoother frame rates, less stuttering, less lagging, less memory usage, and less CPU usage. update also fixes some compatibility issues with different hardware and software configurations.</p>
-<h3>Graphics Enhancements</h3>
-<h4>Improved lighting and shadows</h4>
-<p>Update 2 (v1 03) And Crack-3DM improves the lighting and shadows in the game by adjusting the brightness, contrast, color, and quality of the light sources and the shadow casters. This results in more realistic and immersive visuals, especially in dark and gloomy environments. The update also adds some dynamic lighting effects, such as flickering lights, flashing lights, and light shafts.</p>
-<h4>Added support for higher resolutions and frame rates</h4>
-<p>Update 2 (v1 03) And Crack-3DM adds support for higher resolutions and frame rates in the game by allowing you to change the resolution and refresh rate in the settings menu. You can choose from a variety of resolutions, ranging from 800x600 to 3840x2160 (4K), and refresh rates, ranging from 30 Hz to 120 Hz. The update also removes the 30 FPS cap that was present in the original version of the game, allowing you to play the game at higher frame rates if your hardware can handle it.</p>
-<h4>Optimized textures and models</h4>
-<p>Update 2 (v1 03) And Crack-3DM optimizes the textures and models in the game by reducing their size and complexity without compromising their quality. This results in less loading time, less disk space usage, less memory usage, and less CPU usage. The update also improves the texture filtering and anti-aliasing techniques used by the game engine, resulting in sharper and smoother graphics.</p>
-<h3>Audio Improvements</h3>
-<h4>Fixed some audio issues and sync problems</h4>
-<p>Update 2 (v1 03) And Crack-3DM fixes some audio issues and sync problems that were present in the original version of the game or previous updates. Some of these fixes include:</p>
-<ul>
-<li>Fixed an issue where some sounds or voices would not play or would play incorrectly.</li>
-<li>Fixed an issue where some sounds or voices would be too loud or too quiet.</li>
-<li>Fixed an issue where some sounds or voices would be distorted or corrupted.</li>
-<li>Fixed an issue where some sounds or voices would be delayed or out of sync.</li>
-<li>Fixed an issue where some sounds or voices would overlap or repeat.</li>
-<li>Fixed an issue where some sounds or voices would be missing or cut off.</li>
-</ul>
-<h4>Added support for surround sound and headphones</h4>
-<p>Update 2 (v1 03) And Crack-3DM adds support for surround sound and headphones in the game by allowing you to change the audio output device and mode in the settings menu. You can choose from a variety of audio output devices, such as speakers, headphones, earphones, or external sound cards, and audio output modes, such as stereo, quadraphonic, 5.1 surround sound, or 7.1 surround sound. The update also enhances the spatial audio effects used by the game engine, resulting in more immersive and realistic soundscapes.</p>
-<h4>Improved voice acting and sound effects</h4>
-<p>Update 2 (v1 03) And Crack-3DM improves the voice acting and sound effects in the game by adding more variety, emotion, and clarity to them. The update also adds some new voice lines and sound effects that were not present in the original version of the game or previous updates. Some of these new voice lines and sound effects include:</p>
-<ul>
-<li>New dialogue for Sebastian and other characters during gameplay and cutscenes.</li>
-<li>New grunts, screams, moans, laughs, cries, whispers, and other vocalizations for Sebastian and other characters during gameplay and cutscenes.</li>
-<li>New sounds for weapons, items, enemies, traps, environments, actions, events, and other elements during gameplay and cutscenes.</li>
- </ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Update 2 (v1 03) And Crack-3DM is a great update and crack for The Evil Within that enhances the game in many ways. It fixes some bugs and glitches, improves the gameplay, graphics, and audio quality, adds new difficulty settings for Casual and Survival modes, and allows you to play the game without any DRM or online activation. If you are a fan of horror games, you should definitely download and install Update 2 (v1 03) And Crack-3DM and enjoy The Evil Within to its fullest potential.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Update 2 (v1 03) And Crack-3DM:</p>
- <table>
-<tr><td><b>Q:</b></td><td><b>A:</b></td></tr>
-<tr><td>Is Update 2 (v1 03) And Crack-3DM safe to download and install?</td><td>Yes, Update 2 (v1 03) And Crack-3DM is safe to download and install. It does not contain any viruses, malware, spyware, or other harmful software. However, you should always scan any files you download from the internet with a reliable antivirus program before opening them.</td></tr>
- <tr><td>Does Update 2 (v1 03) And Crack-3DM work with any version of The Evil Within?</td><td>No, Update 2 (v1 03) And Crack-3DM only works with the PC version of The Evil Within. It does not work with the PlayStation 3, PlayStation 4, Xbox 360, or Xbox One versions of The Evil Within.</td></tr>
- <tr><td>Does Update 2 (v1 03) And Crack-3DM work with any language of The Evil Within?</td><td>Yes, Update 2 (v1 03) And Crack-3DM works with any language of The Evil Within. It supports English, French, Italian, German, Spanish, Russian, Polish, Japanese, Korean, Chinese Simplified, and Chinese Traditional languages.</td></tr>
- <tr><td>Does Update 2 (v1 03) And Crack-3DM work with any DLCs or expansions of The Evil Within?</td><td>No, Update 2 (v1 03) And Crack-3DM does not work with any DLCs or expansions of The Evil Within. It only works with the base game of The Evil Within. If you want to play any DLCs or expansions of The Evil Within, you will need to buy them separately and install them separately.</td></tr>
- <tr><td>Does Update 2 (v1 03) And Crack-3DM affect my save files or achievements of The Evil Within?</td><td>No, Update 2 (v1 03) And Crack-3DM does not affect your save files or achievements of The Evil Within. You can continue your progress from where you left off and unlock new achievements as you play. However, you should always backup your save files before installing any updates or cracks just in case something goes wrong.</td></tr>
- </table>
- </p> 0a6ba089eb<br />
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Carenado Stationair Full ((FULL)) (FSX).zip.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Carenado Stationair Full ((FULL)) (FSX).zip.md
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+++ /dev/null
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-<h1>Carenado Stationair: A High-Quality Add-on for FSX/P3D</h1>
-<p>If you are a fan of flight simulation games, you might be interested in Carenado Stationair, a high-quality add-on for Microsoft Flight Simulator X (FSX) and Lockheed Martin Prepar3D (P3D). Carenado Stationair is a detailed and realistic representation of the Cessna CT206H Stationair, a single-engine, six-seat, fixed-wing aircraft that is popular for general aviation and utility purposes.</p>
-<p>Carenado Stationair comes with two versions: the original HD Series and the G1000 Extension Pack. The HD Series features HD quality textures, 3D gauges, original stereo sounds, customizable panel, realistic behavior and performance, and five liveries. The G1000 Extension Pack adds a Carenado G1000 glass cockpit with advanced features such as flight plan creation, TAWS and TCAS alerts, inset map, and two additional liveries.</p>
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-<p>Carenado Stationair is compatible with FSX SP1 and SP2, FSX Acceleration Pack, FSX Steam Edition, and P3D v1 to v4. It requires Windows XP with SP3, Vista or 7 (32 or 64 bits), Pentium V/3GHz or similar processor, and 2GB RAM (4GB recommended). It also requires the CT206H Stationair HD Series for FSX/P3D to run the G1000 Extension Pack.</p>
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-<p>Happy flying!</p><p>Cessna Stationair History</p>
-<p>The Cessna Stationair has a long and rich history that dates back to the early 1960s. The Stationair family is derived from the Cessna 210, a retractable-gear aircraft that was popular for its speed and performance. Cessna decided to create a fixed-gear version of the 210 that would offer more utility and versatility for different missions and markets.</p>
-<p></p>
-<p>The first model of the Stationair family was the Cessna 205, introduced in 1962 as a 1963 model year. The 205 was essentially a 210 with fixed landing gear and a different door arrangement. It had a pilot side door and a small rear door on the left side for the passengers. It was powered by a 260-hp Continental IO-470-S engine and had a distinctive engine cowling bulge that was inherited from the 210. The 205 was produced for only two years, with 576 units built.</p>
-<p>In 1964, Cessna introduced the Cessna 206, which replaced the 205 in production. The 206 had a more powerful 285-hp Continental IO-520-A engine and a redesigned wing with bigger flaps. It also had a new door configuration, with a large clamshell rear door on the right side that allowed easy loading of cargo and passengers. The 206 was marketed as the U206 Super Skywagon, with "U" standing for "utility". It was intended as a flying pickup truck that could handle various tasks such as bush flying, skydiving, aerial photography and more.</p>
-<p>The same year, Cessna also introduced the Cessna P206 Super Skylane, which was a more passenger-oriented version of the 206. The P206 had the same engine as the U206, but retained the door arrangement of the 205, with a pilot side door and a small rear door on the left side. The P206 was aimed at personal and business use, with more comfort and luxury features than the U206.</p>
-<p>Both the U206 and the P206 had turbocharged variants, designated as TU206 and TP206 respectively. The turbocharged models had a Continental TSIO-520 engine that produced either 285 hp or 300 hp depending on the model year. The turbocharged models offered better performance at high altitudes and hot temperatures.</p>
-<p>The U206 and P206 were produced until 1986, when Cessna stopped making single-engine piston aircraft due to liability concerns. During this period, Cessna made several improvements and modifications to the Stationair family, such as adding fuel injection, changing propellers, increasing gross weight, adding avionics and more. The total production of the U206 and P206 was over 6,500 aircraft.</p>
-<p>In 1998, Cessna resumed production of single-engine piston aircraft, including the Stationair. The new model was designated as the Cessna 206H Stationair, with "H" standing for "high". The 206H had a fuel-injected Lycoming IO-540-AC1A engine that produced 300 hp. It also had a new wing design with tapered tips, improved landing gear, updated avionics and more. The 206H also had an optional G1000 glass cockpit extension pack that added a Garmin G1000 integrated flight deck system.</p>
-<p>The turbocharged version of the 206H was called the Cessna T206H Turbo Stationair HD (High Definition), with "HD" indicating enhanced features such as synthetic vision technology, terrain awareness system, traffic collision avoidance system and more. The T206H had a turbocharged Lycoming TIO-540-AJ1A engine that also produced 300 hp.</p>
-<p>The Cessna Stationair is still in production today, offering a versatile and reliable aircraft that can serve various purposes and markets. It has proven to be a popular choice for pilots who need a spacious cabin, a powerful engine, a rugged construction and a high useful load.</p> 81aa517590<br />
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cheto 8 Ball Pool Ipa PATCHED Download.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cheto 8 Ball Pool Ipa PATCHED Download.md
deleted file mode 100644
index bbde6eedf4751b881f72b5566c972db5ee85f702..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cheto 8 Ball Pool Ipa PATCHED Download.md	
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-<p>8 Ball Pool is one of the most popular and addictive online pool games that you can play on your iPhone, iPad, and iPod. However, if you want to have an edge over your opponents and win more matches, you may want to use a cheat tool that can enhance your gameplay and give you unlimited guidelines, auto aim, anti ban, and more.</p>
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- <p>If you are looking for a fun-filled action comedy movie to watch, you might want to check out <strong>Bade Miyan Chhote Miyan</strong>. This 1998 Hindi film stars <strong>Amitabh Bachchan</strong> and <strong>Govinda</strong> in dual roles as police officers and thieves who look alike. The movie is full of hilarious situations, witty dialogues, and thrilling action sequences. It also features <strong>Raveena Tandon</strong>, <strong>Ramya Krishnan</strong>, <strong>Anupam Kher</strong>, <strong>Paresh Rawal</strong>, and <strong>Satish Kaushik</strong> in supporting roles. In this article, we will tell you more about this movie and how you can download it in Hindi dubbed.</p>
- <h2>Introduction</h2>
- <h3>What is Bade Miyan Chhote Miyan?</h3>
- <p><em>Bade Miyan Chhote Miyan</em> (transl. Big Mister and Little Mister) is a 1998 Indian Hindi-language action comedy film directed by <strong>David Dhawan</strong>. Produced by <strong>Vashu Bhagnani</strong>'s Pooja Entertainment, the film is inspired by William Shakespeare's <em>The Comedy of Errors</em> and the 1995 American film <em>Bad Boys</em> by Michael Bay. [1] It was the first Hindi film to be shot in the Ramoji Film City. [2]</p>
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- <h3>Why is it a popular movie?</h3>
- <p><em>Bade Miyan Chhote Miyan</em> was one of the highest grossing Hindi films of 1998. It was theatrically released worldwide on 16 October 1998 and emerged as a major commercial success with a total gross collection of ₹ 35.21 crore, despite clashing with Karan Johar's <em>Kuch Kuch Hota Hai</em> which also saw Kher in an important role. [3] The film was praised for its comedy, action, music, and performances of Bachchan and Govinda. It also had a special appearance by <strong>Madhuri Dixit</strong> in a song. The film has a cult following among the fans of Dhawan, Bachchan, and Govinda.</p>
- <h3>How to download it in Hindi dubbed?</h3>
- <p>If you want to watch this movie in Hindi dubbed, you can find it online on various platforms. Some of the websites that offer this movie in Hindi dubbed are:</p>
-<ul>
-<li>Eros Now: You can watch this movie on Eros Now with Prime Video Channels. You can also download it for offline viewing.</li>
-<li>Moviespur: You can download this movie in HD quality from Moviespur for free.</li>
-<li>Filmywap: You can download this movie in various formats from Filmywap for free.</li>
-<li>Movierulz: You can download this movie from Movierulz for free.</li>
-</ul>
-<p>However, we advise you to watch this movie legally from authorized sources and avoid piracy.</p>
- <h2>Plot Summary</h2>
- <h3>The main characters</h3>
- <p>The film revolves around four characters who look alike:</p>
-<ul>
-<li><strong>Bade Miyan (Amitabh Bachchan)</strong>: A notorious thief who robs rich people with his partner Chote Miyan.</li>
-<li><strong>Chote Miyan (Govinda)</strong>: A petty thief who works with Bade Miyan and often gets into trouble.</li>
-<li><strong>Arjun Singh (Amitabh Bachchan)</strong>: A honest police inspector who works with his friend Pyare Mohan.</li>
-<li><strong>Pyare Mohan (Govinda)</strong>: A loyal police inspector who works with Arjun Singh and loves his sister Seema.</li>
-</ul>
- <h3>The story of confusion and comedy</h3>
- <p>The story begins when Arjun Singh and Pyare Mohan are assigned to catch Zorawar Siddiqui, a smuggler of diamonds and arms who works under the cover of being a statue maker. Zorawar kills Madhu, an eyewitness of his crime, but her friend Neha sees her murder. She calls the police, and Arjun comes there. He takes her to Pyare's house for protection.</p>
- <p>Meanwhile, Bade Miyan and Chote Miyan arrive in town and start robbing people. They also impersonate Arjun and Pyare to escape from the police. This leads to a series of confusions and misunderstandings as every crime they commit is blamed on Arjun and Pyare. Their seniors suspend them from duty and their reputation is tarnished.</p>
- <p>To make matters worse, even Seema and Neha mistake Bade and Chote for Arjun and Pyare. They fall in love with them without knowing their true identities. Bade and Chote also take advantage of this situation and enjoy their company.</p>
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- <h3>The climax and resolution</h3>
- <p>The climax happens when Zorawar kidnaps Seema to blackmail Arjun and Pyare. He also finds out that Bade and Chote look like them and decides to use them for his benefit. He sends them to rob a bank while he plans to escape with Seema.</p>
- <p>However, Bade and Chote realize their mistake and decide to help Arjun and Pyare. They arrive at Zorawar's hideout and stall him while Arjun and Pyare come with the police force. A fight ensues between Zorawar's men and the police officers. Bade, Chote, Arjun, and Pyare work together to defeat Zorawar and rescue Seema.</p>
- <p>The film ends with Bade and Chote confessing their crimes and apologizing to Seema, Neha, Arjun, Pyare, Shyamlal (the police commissioner), Sharafat Ali (a friend of Zorawar), Sharad (Zorawar's henchman), Sundar (Zorawar's driver), Makkan (a watchman), Madhu's father, etc. They are forgiven by everyone except Sharafat Ali who still wants revenge on them.</p>
- <h2>Cast and Crew</h2>
- <h3>The lead actors and their roles</h3>
- <table>
-<tr>
-<th>Name</th>
-<th>Role</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Amitabh Bachchan</td>
-<td>Dual Role (Insp. Arjun Singh & Bade Miyan)</td>
-<td>A veteran actor who played both the honest cop and the notorious thief with equal ease.</td>
-</tr>
-<tr>
-<td>Govinda</td>
-<td>Dual Role (Insp. Pyare Mohan & Chote Miyan)</td>
-<td>A versatile actor who played both the loyal cop and the petty thief with equal charm.</td>
-</tr>
-<tr>
-<td>Raveena Tandon</td>
-<td>Seema Singh</td>
-<td>A beautiful actress who played Arjun's sister and Pyare's love interest.</td>
-</tr>
-<tr>
-<td>Ramya Krishnan</td>
-<td>Neha</td>
-<td>A talented actress who played Madhu's friend and Arjun's love interest.</td>
-</tr>
-<tr></table>
- <h3>The supporting actors and their roles</h3>
- <table><tr><th>Name <th>Role <th>Description <tr><td>Anupam Kher <td>Police Commissioner Shyamlal <td>A veteran actor who played the boss of Arjun and Pyare. <tr><td>Paresh Rawal <td>Zorawar Bhai <td>A versatile actor who played the main antagonist of the film. <tr><td>Satish Kaushik <td>Sharafat Ali <td>A comic actor who played a friend of Zorawar who gets cheated by Bade and Chote. <tr><td>Sharat Saxena Here is the remaining part of the article with HTML formatting: <h2>Box Office and Reception</h2>
- <h3>The budget and the collection</h3>
- <p>The film was made on a budget of ₹ 8.9 crore. It was released on 16 October 1998 on the occasion of Dussehra. It faced a tough competition from <em>Kuch Kuch Hota Hai</em>, which was also released on the same day. However, <em>Bade Miyan Chhote Miyan</em> managed to hold its own and became a huge hit at the box office. It collected ₹ 35.21 crore in India and ₹ 4.5 crore overseas, for a worldwide total of ₹ 39.71 crore. It was the second highest grossing Hindi film of 1998, after <em>Kuch Kuch Hota Hai</em>. [4] It was also one of the highest grossing Hindi films of the decade.</p>
- <h3>The critical and audience reviews</h3>
- <p>The film received positive reviews from both critics and audiences. It was praised for its comedy, action, music, and performances of Bachchan and Govinda. Some of the critics' comments are:</p>
-<ul>
-<li>"<em>Bade Miyan Chhote Miyan</em> is a laugh riot from start to end. The film has some of the most hilarious scenes ever seen in Hindi cinema. The chemistry between Amitabh Bachchan and Govinda is superb. They complement each other perfectly and create magic on screen." - Taran Adarsh, Bollywood Hungama [5]</li>
-<li>"<em>Bade Miyan Chhote Miyan</em> is a fun-filled entertainer that delivers what it promises. It is a perfect example of how to make a masala film with style and substance. The film has everything - comedy, action, romance, drama, and music. The film belongs to Amitabh Bachchan and Govinda who are in top form and make you laugh till your sides hurt." - Rajeev Masand, Outlook [6]</li>
-<li>"<em>Bade Miyan Chhote Miyan</em> is a delightful comedy that showcases the talent and versatility of Amitabh Bachchan and Govinda. They play their dual roles with ease and conviction. The film has some of the best comic scenes ever seen in Hindi cinema. The film is a must watch for all comedy lovers." - Anupama Chopra, India Today [7]</li>
-</ul>
- <h3>The awards and nominations</h3>
- <p>The film won one award and received one nomination at various award ceremonies. The details are:</p>
-<table>
-<tr>
-<th>Award</th>
-<th>Category</th>
-<th>Recipient(s)</th>
-<th>Result</th>
-</tr>
-<tr>
-<td>Zee Cine Awards 1999</td>
-<td>Best Actor in a Comic Role</td>
-<td>Govinda</td>
-<td>Won</td>
-</tr>
-<tr>
-<td>44th Filmfare Awards</td>
-<td>Best Actor</td>
-<td>Govinda</td>
-<td>Nominated</td>
-</tr>
-<tr></table>
- <h2>Conclusion</h2>
- <p><em>Bade Miyan Chhote Miyan</em> is a classic action comedy film that entertains you with its humor, action, music, and performances. It is one of the best films of David Dhawan, Amitabh Bachchan, and Govinda. It is a film that you can watch anytime and enjoy with your family and friends.</p>
- <p>If you want to watch this movie in Hindi dubbed, you can download it from various websites online. However, we advise you to watch it legally from authorized sources and avoid piracy.</p>
- <p>We hope you enjoyed reading this article and learned more about this movie. If you have any questions or feedback, please let us know in the comments below.</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <p>Here are some of the common questions that people ask about this movie:</p>
- <ol>
-<li><strong>Q: Who played the role of Madhuri Dixit's husband in the song "Makhna"?</strong></li>
-<li>A: The role of Madhuri Dixit's husband in the song "Makhna" was played by actor <strong>Mahavir Shah</strong>, who also played Zorawar's client in the film.</li>
-<li><strong>Q: What was the name of Zorawar's driver who helped Bade and Chote escape from the police?</strong></li>
-<li>A: The name of Zorawar's driver who helped Bade and Chote escape from the police was <strong>Sundar</strong>, played by actor <strong>Avtar Gill</strong>.</li>
-<li><strong>Q: What was the name of the museum where Bade and Chote stole a diamond necklace?</strong></li>
-<li>A: The name of the museum where Bade and Chote stole a diamond necklace was <strong>Raj Mahal Museum</strong>. The museum security officer was played by actor <strong>Govardhan Asrani</strong>.</li>
-<li><strong>Q: What was the name of Sharafat Ali's wife who slapped him for cheating on her?</strong></li>
-<li>A: The name of Sharafat Ali's wife who slapped him for cheating on her was <strong>Munni Bai</strong>, played by actress <strong>Kishori Shahane</strong>.</li>
-<li><strong>Q: What was the name of Arjun and Seema's mother who wanted Arjun to get married?</strong></li>
-<li>A: The name of Arjun and Seema's mother who wanted Arjun to get married was <strong>Laxmi Devi Singh</strong>, played by actress <strong>Sushma Seth</strong>.</li>
-<ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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-<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Brawlhalla 2014 The Free 2D Platform Fighting Game You Need to Try.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Brawlhalla 2014 The Free 2D Platform Fighting Game You Need to Try.md
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- <p>Once you have downloaded Brawlhalla, you can launch it from your platform's menu or library. You will need an internet connection to play online modes, but you can also play offline modes without it.</p>
- <h2>Brawlhalla Game Overview</h2>
- <p>Brawlhalla is a game that offers a lot of variety and fun for players of all skill levels. Here are some of the main features and modes that you can enjoy in Brawlhalla:</p>
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- <ul>
-<li><strong>Free to Play:</strong> Brawlhalla is completely free to play. You don't need to pay anything to access all the game modes, characters, stages, and features. You can also earn gold by playing matches or completing missions, which you can use to unlock more characters or buy cosmetic items.</li>
-<li><strong>Cross-Play:</strong> Brawlhalla supports full cross-play for all platforms. This means that you can play with or against anyone who has Brawlhalla on any device, regardless of where they downloaded it from. You can also link your Brawlhalla account to multiple platforms and keep your progress and items synced.</li>
-<li><strong>Customization:</strong> Brawlhalla allows you to customize your characters and gameplay to your liking. You can choose from hundreds of skins, colors, taunts, KO effects, sidekicks, and more to personalize your appearance. You can also adjust the game settings, such as the gravity, damage, speed, and items, to create your own custom game modes.</li>
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- <ul>
-<li><strong>Casual:</strong> Casual mode is where you can play for fun and practice your skills. You can join a free-for-all match with up to 8 players, or a 2v2 or 4v4 team match with your friends or random partners. You can also play experimental mode, which tests new features and changes before they are implemented in the game.</li>
-<li><strong>Ranked:</strong> Ranked mode is where you can compete for glory and rewards. You can play solo or with a partner in 1v1 or 2v2 matches, and climb the global leaderboard based on your elo rating. You can also earn season rewards, such as avatars, borders, and glory points, which you can use to buy exclusive items.</li>
-<li><strong>Custom:</strong> Custom mode is where you can create or join private rooms with your own rules and settings. You can invite your friends or anyone who has the room code, and play any game mode you want. You can also browse public rooms created by other players, and join them if they have space.</li>
-<li><strong>Brawl of the Week:</strong> Brawl of the Week is a special mode that changes every week and offers a unique challenge or twist to the gameplay. For example, you might have to dodge bombs, collect crystals, or fight in a snowball fight. You can earn bonus gold and experience by playing this mode.</li>
-</ul>
- <h2>Brawlhalla Characters and Crossovers</h2>
- <p>One of the most appealing aspects of Brawlhalla is the variety and diversity of its characters. There are over 50 Legends to choose from, each with their own backstory, personality, stats, weapons, and signature moves. You can find characters from different genres, such as fantasy, sci-fi, history, mythology, and more. You can also unlock new characters by using gold or mammoth coins, which are the premium currency of the game.</p>
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- <p>Besides the original Legends, Brawlhalla also features crossover events with popular franchises and celebrities. These events add new skins for existing characters that resemble iconic characters from other media. For example, you can play as Lara Croft from Tomb Raider, Finn and Jake from Adventure Time, The Rock and John Cena from WWE, Steven Universe, Ben 10, Shovel Knight, Rayman, Hellboy, and more. These events also add new stages, music, items, and game modes related to the crossover theme.</p>
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- <p>Brawlhalla is a game that you can enjoy both online and offline. If you have an internet connection, you can play online modes with other players from around the world. You can also chat with them using text or voice chat, add them as friends, or report them if they are abusive or cheating. You can also join clans with other players who share your interests or goals.</p>
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- <p>As you can see, Brawlhalla and Super Smash Bros. have some common elements, but also some distinct features that make them different. Some might prefer Brawlhalla for its free and cross-play nature, while others might prefer Super Smash Bros. for its iconic and diverse roster. Ultimately, it depends on your personal preference and taste.</p>
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- <p>Brawlhalla is a game that is constantly evolving and improving. The developers are always working on adding new content, features, bug fixes, and improvements to the game. Here are some of the future updates and plans for Brawlhalla that you can look forward to:</p>
- <ul>
-<li><strong>New Legends:</strong> Brawlhalla will continue to introduce new characters to the game, with different stats, weapons, and skills. The latest Legend to join the game is <strong>Mako</strong>, the Shark Hunter, who uses a greatsword and a katar. The next Legend to join the game is <strong>Magyar</strong>, the Ghost Armor, who will use a hammer and a sword.</li>
-<li><strong>New Crossovers:</strong> Brawlhalla will also continue to collaborate with other franchises and celebrities to bring new crossover events to the game. The latest crossover event to join the game is <strong>The Walking Dead</strong>, which features Rick Grimes, Daryl Dixon, and Michonne as skins for Barraza, Ember, and Koji respectively. The next crossover event to join the game is <strong>Teenage Mutant Ninja Turtles</strong>, which will feature Leonardo, Raphael, Donatello, and Michelangelo as skins for Val, Ragnir, Brynn, and Caspian respectively.</li>
-<li><strong>New Modes:</strong> Brawlhalla will also introduce new game modes to the game, with different rules and objectives. The latest mode to join the game is <strong>Crew Battles</strong>, which is a 3v3 or 4v4 team mode where each player has one stock and one Legend. The next mode to join the game is <strong>Morph</strong>, which is a 1v1 or 2v2 mode where each player can switch between two Legends during the match.</li>
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- <p>Brawlhalla is a free-to-play platform fighter game that supports up to 8 players online or locally, with full cross-play for all platforms. You can choose from over 50 unique characters, each with their own stats, weapons, and skills. You can also enjoy various game modes, such as casual free-for-alls, ranked matches, custom rooms, and brawl of the week. Brawlhalla also features crossover events with popular franchises and celebrities, such as Tomb Raider, Adventure Time, WWE, Steven Universe, Ben 10, Shovel Knight, Rayman, Hellboy, The Walking Dead, and Teenage Mutant Ninja Turtles.</p>
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- <p>Here are some of the frequently asked questions and answers about Brawlhalla:</p>
- <ol>
-<li><strong>Q: How do I get better at Brawlhalla?</strong></li>
-<li>A: There is no easy or quick way to get better at Brawlhalla. You need to practice your skills, learn from your mistakes, watch tutorials and guides, study other players' strategies and techniques, and experiment with different characters, weapons, and modes. You can also join clans, play with friends, or seek feedback from other players to improve your game.</li>
-<li><strong>Q: How do I get more gold and mammoth coins in Brawlhalla?</strong></li>
-<li>A: You can get more gold by playing matches, completing missions, logging in daily, and participating in events. You can use gold to unlock more characters or buy cosmetic items. You can get more mammoth coins by spending real money, or by earning them through giveaways, contests, or promotions. You can use mammoth coins to buy exclusive items, such as skins, colors, taunts, KO effects, sidekicks, and more.</li>
-<li><strong>Q: How do I link my Brawlhalla account to multiple platforms?</strong></li>
-<li>A: You can link your Brawlhalla account to multiple platforms by following these steps:</li>
-<ul>
-<li>Go to <a href="">Brawlhalla.com/account</a> and log in with your Ubisoft account.</li>
-<li>Click on the "Link Accounts" button and choose the platform you want to link.</li>
-<li>Follow the instructions on the screen to complete the linking process.</li>
-<li>Repeat the steps for any other platform you want to link.</li>
-</ul>
-<li>Note that you can only link one account per platform, and you cannot unlink an account once it is linked.</li>
-<li><strong>Q: How do I report a player who is cheating or abusive in Brawlhalla?</strong></li>
-<li>A: You can report a player who is cheating or abusive in Brawlhalla by following these steps:</li>
-<ul>
-<li>Go to the post-game lobby after the match is over.</li>
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-<li>Note that you should only report players who are violating the <a href="">Code of Conduct</a>, and not for personal reasons or disagreements.</li>
-<li><strong>Q: How do I join or create a clan in Brawlhalla?</strong></li>
-<li>A: You can join or create a clan in Brawlhalla by following these steps:</li>
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diff --git a/spaces/1phancelerku/anime-remove-background/Archive Org APK Terbaru 2017 The Ultimate Guide to the Free Software Collection on the Internet Archive.md b/spaces/1phancelerku/anime-remove-background/Archive Org APK Terbaru 2017 The Ultimate Guide to the Free Software Collection on the Internet Archive.md
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--- a/spaces/1phancelerku/anime-remove-background/Archive Org APK Terbaru 2017 The Ultimate Guide to the Free Software Collection on the Internet Archive.md	
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diff --git a/spaces/1phancelerku/anime-remove-background/Boost Your Maths Skills with NumBots APK - The Game that Makes You a Maths Rock Star.md b/spaces/1phancelerku/anime-remove-background/Boost Your Maths Skills with NumBots APK - The Game that Makes You a Maths Rock Star.md
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--- a/spaces/1phancelerku/anime-remove-background/Boost Your Maths Skills with NumBots APK - The Game that Makes You a Maths Rock Star.md	
+++ /dev/null
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-<p>Do you want to help your child learn math skills in a fun and engaging way? If so, you might want to check out NumBots APK, a game that combines math practice with robot adventure. NumBots APK is a game that teaches children how to add and subtract, while also developing their understanding, recall, and fluency in mental arithmetic. It is designed by teachers and math experts, based on the latest research and curriculum standards. In this article, we will tell you everything you need to know about NumBots APK, including how to download and install it on your Android device, how to play it and learn math skills, what features and benefits it offers, what are its pros and cons, and our conclusion and recommendations.</p>
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-<p>Downloading and installing NumBots APK on your Android device is very easy. Just follow these simple steps:</p>
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-<ol>
-<li>Go to the Google Play Store or <a href="(^1^)">APKCombo website</a>.</li>
-<li>Search for NumBots and tap on the install button.</li>
-<li>Allow the app to access your device's storage and permissions.</li>
-<li>Launch the app and sign in with your account or create a new one.</li>
-</ol>
-<p>Congratulations! You are now ready to play NumBots APK on your Android device.</p>
- <h2>How to Play NumBots APK and Learn Math Skills</h2>
-<p>Playing NumBots APK is very simple. Just follow these easy steps:</p>
-<ol>
-<li>Choose your level and mode from Story or Challenge.</li>
-<li>Solve the math problems by dragging the correct answer to the equation.</li>
-<li>Collect coins and stars as you progress through the levels.</li>
-<li <li>Unlock new robots and upgrade them with your coins.</li>
-</ol>
-<p>That's it! You are now playing NumBots APK and learning math skills at the same time.</p>
- <h2>Features and Benefits of NumBots APK</h2>
-<p>NumBots APK is not just a game, but also a learning platform that offers many features and benefits for children. Here are some of them:</p>
-<ul>
-<li><strong>Engaging and interactive gameplay</strong> that motivates children to learn math. NumBots APK uses gamification elements such as levels, coins, stars, robots, and rewards to make math practice fun and exciting. Children can also compete with their friends and other players online to see who can solve the most problems in the shortest time.</li>
-<li><strong>Adaptive and personalized learning</strong> that adjusts to each child's ability and pace. NumBots APK uses artificial intelligence and algorithms to tailor the difficulty and content of the math problems to each child's level and progress. This way, children can learn at their own speed and challenge themselves without getting frustrated or bored.</li>
-<li><strong>Curriculum-aligned and evidence-based content</strong> that covers addition and subtraction skills. NumBots APK is based on the national curriculum standards and the latest research on math education. It covers the essential skills of addition and subtraction, from single-digit to multi-digit numbers, from counting to mental strategies, from concrete to abstract representations. It also helps children develop their number sense, place value, and problem-solving skills.</li>
-<li><strong>Feedback and progress reports</strong> that help children and parents track their learning outcomes. NumBots APK provides instant feedback and hints to help children correct their mistakes and improve their accuracy. It also generates detailed reports that show children's strengths, weaknesses, achievements, and areas for improvement. Parents can access these reports through the app or the website to monitor their children's performance and progress.</li>
-</ul>
- <h2>Pros and Cons of NumBots APK</h2>
-<p>Like any other app, NumBots APK has its pros and cons. Here are some of them:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td><ul>
-<li>Fun and educational game that helps children develop math fluency and confidence.</li>
-<li>Free to download and play with optional in-app purchases for extra features.</li>
-<li>Compatible with most Android devices and easy to install and use.</li>
-</ul></td>
-<td><ul>
-<li>Requires internet connection and account registration to play.</li>
-<li>May contain ads and third-party links that could distract children from learning.</li>
-</ul></td>
-</tr>
-</table>
- <h2>Conclusion and Recommendations</h2>
-<p>In conclusion, NumBots APK is a highly engaging platform for learning to add and subtract, created by teachers and math experts. It is suitable for children from kindergarten upwards. It offers a variety of features and benefits that make math learning fun and effective. It has some minor drawbacks that can be easily overcome with parental guidance and supervision.</p>
- <p>We recommend NumBots APK to parents who want to help their children improve their math skills in a fun and interactive way. We also suggest that parents monitor their children's screen time and online activity while using the app.</p>
- <h3>Frequently Asked Questions</h3>
-<ol>
-<li><strong>What is NumBots APK?</strong></li>
-<p>NumBots APK is a game that teaches children how to add and subtract, while also developing their understanding, recall, and fluency in mental arithmetic.</p>
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-<p>You can download NumBots APK from the Google Play Store or the APKCombo website.</p>
- <li><strong>How can I play NumBots APK?</strong></li>
-<p>You can play NumBots APK by choosing your level and mode from Story or Challenge, solving the math problems by dragging the correct answer to the equation, collecting coins and stars as you progress through the levels, and unlocking new robots and upgrading them with your coins.</p>
- <li><strong>What are the benefits of NumBots APK?</strong></li>
-<p>NumBots APK offers many benefits for children, such as engaging and interactive gameplay, adaptive and personalized learning, curriculum-aligned and evidence-based content, feedback and progress reports, etc.</p>
- <li><strong>What are the drawbacks of NumBots APK?</strong></li>
-<p>NumBots APK has some drawbacks, such as requiring internet connection and account registration to play, containing ads and third-party links that could distract children from learning, etc.</p>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Burak Bulutun Tm Albmleri ve Teklileri MP3 Olarak ndirin.md b/spaces/1phancelerku/anime-remove-background/Burak Bulutun Tm Albmleri ve Teklileri MP3 Olarak ndirin.md
deleted file mode 100644
index 7cf797d244d7468ca529258a6397493a709bb103..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Burak Bulutun Tm Albmleri ve Teklileri MP3 Olarak ndirin.md	
+++ /dev/null
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-<h1>Burak Bulut Mp3 Indir: How to Download and Enjoy His Music</h1>
-<p>If you are a fan of Turkish pop music, you have probably heard of Burak Bulut, one of the most talented and successful singers in the country. But if you are not familiar with him, you might be wondering who he is, why you should listen to his music, and how you can download his mp3 files. In this article, we will answer all these questions and more, so you can enjoy his music anytime and anywhere.</p>
- <h2>Who is Burak Bulut?</h2>
-<p>Burak Bulut is a Turkish singer, songwriter, and producer who was born in Istanbul in 1990. He started his musical career at a young age, playing guitar and singing in various bands. He also studied music production at a university and worked as a sound engineer for several artists.</p>
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- <h3>A brief biography of the Turkish singer</h3>
-<p>Burak Bulut's breakthrough came in 2017, when he released his first solo single, "Yana Yana", which became a hit on YouTube and Spotify. Since then, he has released several singles and albums, such as "Cano", "Aşkın Suçu Yok", "Sen Sevda Mısın", and "Yaralı". He has also collaborated with other famous Turkish singers, such as Kurtuluş Kuş, Ebru Yaşar, Tuğba Yurt, and Emrah Karaduman.</p>
- <h3>His musical style and influences</h3>
-<p>Burak Bulut's musical style is mainly pop, with elements of rock, folk, electronic, and rap. He sings in Turkish, but sometimes uses English or Arabic words in his lyrics. He is influenced by various genres and artists, such as pop rock, indie rock, alternative rock, R&B, hip hop, reggae, and dancehall. Some of his favorite artists are Coldplay, U2, Ed Sheeran, Bruno Mars, Drake, Rihanna, Bob Marley, and Sean Paul.</p>
- <h2>Why should you listen to Burak Bulut?</h2>
-<p>Burak Bulut is not only a talented singer and songwriter, but also a versatile and creative artist who can appeal to different tastes and moods. His music is catchy, energetic, emotional, romantic, and fun. He can make you dance, sing along, cry, or smile with his songs. He can also inspire you with his positive messages and stories.</p>
- <h3>His popular songs and collaborations</h3>
-<p>Some of Burak Bulut's most popular songs are:</p>
-<ul>
-<li>"Yana Yana": A catchy pop song with a reggae vibe that talks about being close to someone you love.</li>
-<li>"Cano": A romantic ballad with a folk touch that expresses the deep feelings for a lover.</li>
-<li>"Aşkın Suçu Yok": A powerful rock song with rap verses that challenges the stereotypes and prejudices about love.</li>
-<li>"Sen Sevda Mısın": A duet with Kurtuluş Kuş that is a sweet and playful love song.</li>
-<li>"Yaralı": A collaboration with Emrah Karaduman that is a dancehall track with Arabic influences that talks about being hurt by a lover.</li>
-</ul>
- <h3>His awards and achievements</h3>
-<p>Burak Bulut has received several awards and nominations for his music, such as:</p> <p>Burak Bulut has received several awards and nominations for his music, such as:</p>
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-<table>
-<tr>
-<th>Award</th>
-<th>Category</th>
-<th>Year</th>
-<th>Result</th>
-</tr>
-<tr>
-<td>Golden Butterfly Awards</td>
-<td>Best Newcomer Singer</td>
-<td>2018</td>
-<td>Won</td>
-</tr>
-<tr>
-<td>Turkish Music Awards</td>
-<td>Best Pop Male Singer</td>
-<td>2019</td>
-<td>Nominated</td>
-</tr>
-<tr>
-<td>Turkish Music Awards</td>
-<td>Best Pop Song (for "Yana Yana")</td>
-<td>2019</td>
-<td>Nominated</td>
-</tr>
-<tr>
-<td>Turkish Music Awards</td>
-<td>Best Pop Album (for "Aşkın Suçu Yok")</td>
-<td>2020</td>
-<td>Nominated</td>
-</tr>
-<tr>
-<td>Turkish Music Awards</td>
-<td>Best Pop Collaboration (with Kurtuluş Kuş for "Sen Sevda Mısın")</td>
-<td>2020</td>
-<td>Nominated</td>
-</tr>
- <h2>How to download Burak Bulut mp3 files?</h2>
- <p>If you want to download Burak Bulut mp3 files, you should be aware of the legal and ethical issues involved. Downloading music without paying for it or without the permission of the artist is considered piracy and can have serious consequences. Therefore, you should always respect the rights of the creators and support them by buying their music or streaming it from authorized platforms.</p>
- <h3>The legal and ethical way to download his music</h3>
- <p>The legal and ethical way to download Burak Bulut's music is to purchase it from online stores or platforms that offer digital downloads, such as iTunes, Amazon, Google Play, or Qobuz. These sites allow you to buy individual songs or albums and download them to your device in various formats, such as mp3, wav, flac, or aac. You can also choose the quality and bitrate of the files according to your preferences. By buying his music, you are supporting him financially and helping him continue his artistic career.</p>
- <h3>The best sites and apps to download Burak Bulut mp3 files</h3>
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- <li><a href="(^4^)">Qobuz(^4^)</a>: This is a high-quality music streaming and downloading service that offers a wide range of genres and artists, including Burak Bulut. You can download his songs or albums in various formats and qualities, such as mp3, flac, or 24-bit. You can also access exclusive editorial content and recommendations. Qobuz requires a subscription fee, but you can try it for free for 30 days.</li>
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- <p>Now that you know how to download Burak Bulut mp3 files, you might be wondering how to enjoy his music. Here are some tips and suggestions for you:</p>
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- <p>The best way to listen to Burak Bulut's songs is to use a good pair of headphones or speakers that can deliver high-quality sound and bass. You can also use an equalizer app or feature to adjust the sound settings according to your preferences. You can also listen to his songs in different environments and moods, such as in the car, at the gym, at the beach, or at home. You can also sing along or dance to his songs if you feel like it.</p>
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- <p>Burak Bulut's music is versatile and can be mixed with different playlists and genres. You can create your own playlists based on your favorite songs or themes, such as love, party, chill, or workout. You can also listen to curated playlists that feature his songs or similar artists, such as Turkish Pop Hits, Burak Bulut Essentials, or Pop Rock Mix. You can also mix his music with other genres that complement his style, such as rock, folk, electronic, or rap.</p>
- <h2>Conclusion</h2>
- <p>Burak Bulut is one of the most talented and successful Turkish pop singers in the country. He has a unique and diverse musical style that can appeal to different tastes and moods. He has also released several popular songs and albums that have earned him many awards and nominations. If you want to download and enjoy his music, you should follow the legal and ethical way and use the best sites and apps that offer high-quality mp3 files. You should also listen to his songs with good headphones or speakers and mix them with different playlists and genres.</p>
- <h3>A summary of the main points of the article</h3>
- <p>In this article, we have covered the following topics:</p>
-<ul>
-<li>Who is Burak Bulut?</li>
-<li>Why should you listen to Burak Bulut?</li>
-<li>How to download Burak Bulut mp3 files?</li>
-<li>How to enjoy Burak Bulut's music?</li>
-</ul>
- <h3>A call to action for the readers</h3>
- <p>If you are interested in Burak Bulut's music, you should check out his official website, YouTube channel, Instagram account, and Spotify profile. You can also follow him on Twitter and Facebook for the latest news and updates. You can also share your thoughts and opinions about his music in the comments section below or on social media using the hashtag #BurakBulut.</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some of the most frequently asked questions about Burak Bulut and his music:</p>
- <h4>Q: How old is Burak Bulut?</h4>
-<p>A: Burak Bulut is 33 years old as of 2023. He was born on June 6, 1990.</p>
- <h4>Q: Is Burak Bulut married?</h4>
-<p>A: No, Burak Bulut is not married. He is currently single and focused on his musical career.</p>
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-<p>A: Burak Bulut's net worth is estimated at around $1 million as of 2023. He earns most of his income from his music sales, streams, concerts, and endorsements.</p>
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-<p>A: Burak Bulut's zodiac sign is Gemini. He is said to be adaptable, communicative, creative, and curious.</p>
- <h4>Q: What are some of Burak Bulut's hobbies and interests?</h4>
-<p>A: Besides music, Burak Bulut enjoys playing guitar, reading books, watching movies, traveling, and spending time with his friends and family.</p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Free Download The Grinch PC Game - Explore Four Seussian Environments and Three Minigames.md b/spaces/1phancelerku/anime-remove-background/Free Download The Grinch PC Game - Explore Four Seussian Environments and Three Minigames.md
deleted file mode 100644
index 6cda27efbe39bd68502b7d6f1855b45248fc9cfa..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Free Download The Grinch PC Game - Explore Four Seussian Environments and Three Minigames.md	
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-<h1>The Grinch Video Game PC Download: How to Play the Classic Game Based on the Movie</h1>
- <p>If you are a fan of Dr. Seuss' classic tale <em>How the Grinch Stole Christmas!</em>, you may be interested in playing <strong>The Grinch</strong>, a video game adaptation of the 2000 live-action movie starring Jim Carrey. The game was released for various platforms, including PC, in 2000 and 2001. In this game, you can control the Grinch and his loyal dog Max as they try to ruin Christmas for the Whos of Whoville by completing various missions and minigames.</p>
-<h2>the grinch video game pc download</h2><br /><p><b><b>DOWNLOAD</b>  <a href="https://jinyurl.com/2uNSIP">https://jinyurl.com/2uNSIP</a></b></p><br /><br />
- <p>In this article, we will show you how to download and play The Grinch video game for PC, as well as how to enjoy its features and gameplay. Whether you want to relive your childhood memories or discover a new way to experience the story, The Grinch video game for PC is a fun and festive option for you.</p>
- <h2>How to Download The Grinch Video Game for PC</h2>
- <p>There are two main ways to download and play The Grinch video game for PC: either from Archive.org or from RetroGames.cc. We will explain both options below and help you choose the best one for you.</p>
- <h3>Option 1: Download from Archive.org</h3>
- <p>Archive.org is a website that preserves and provides access to various digital media, including software, games, books, music, and more. You can find the archive disk files for The Grinch PC game on Archive.org and download them for free. Here are the steps to do so:</p>
- <ol>
-<li>Go to <a href="">the archive page</a> for The Grinch PC game on Archive.org.</li>
-<li>Click on the "DOWNLOAD OPTIONS" section on the right side of the page.</li>
-<li>Select the "ISO IMAGE" option and click on the "grinch.iso" file to download it.</li>
-<li>Once the download is complete, you will need a software that can extract and mount ISO files, such as WinRAR or Daemon Tools.</li>
-<li>Extract the ISO file to a folder on your PC using WinRAR or a similar software.</li>
-<li>Mount the ISO file using Daemon Tools or a similar software. This will create a virtual drive on your PC that contains the game files.</li>
-<li>Open the virtual drive and run the "Setup.exe" file to install the game on your PC.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Once the installation is done, you can launch the game from your desktop or start menu.</li>
-</ol>
- <p>Note that The Grinch PC game was designed for older versions of Windows, such as Windows 98 or Windows XP. Therefore, you may encounter some compatibility issues when running it on newer versions of Windows, such as Windows 10. To fix these issues, you can try the following solutions:</p>
- <ul>
-<li>Run the game in compatibility mode. To do this, right-click on the game's shortcut or executable file, select "Properties", go to the "Compatibility" tab, check the box that says "Run this program in compatibility mode for:", and choose an older version of Windows from the drop-down menu. Click "Apply" and then "OK".</li>
-<li>Run the game as an administrator. To do this, right-click on the game's shortcut or executable file, select "Run as administrator", and confirm your choice.</li>
-<li>Update your graphics card drivers. To do this, go to your graphics card manufacturer's website, find the latest drivers for your model, download and install them on your PC.</li>
-</ul>
- <p>The system requirements for The Grinch PC game are as follows:</p>
- <table>
-<tr><th>Minimum Requirements</th><th>Recommended Requirements</th></tr>
-<tr><td>CPU: Pentium II 266 MHz or equivalent</td><td>CPU: Pentium III 450 MHz or equivalent</td></tr>
-<tr><td>RAM: 64 MB</td><td>RAM: 128 MB</td></tr>
-<tr><td>OS: Windows 98/ME/2000/XP</td><td>OS: Windows 98/ME/2000/XP</td></tr>
-<tr><td>Video Card: 8 MB DirectX 8 compatible video card</td><td>Video Card: 16 MB DirectX 8 compatible video card</td></tr>
-<tr><td>Sound Card: DirectX 8 compatible sound card</td><td>Sound Card: DirectX 8 compatible sound card</td></tr>
-<tr><td>Free Disk Space: 650 MB</td><td>Free Disk Space: 650 MB</td></tr>
-<tr><td>CD-ROM: 4X CD-ROM drive</td><td>CD-ROM: 4X CD-ROM drive</td></tr>
-</table>
- <h3>Option 2: Download from RetroGames.cc</h3>
- <p>RetroGames.cc is a website that allows you to play retro games online in your browser without downloading or installing anything. You can find The Grinch video game for PC on RetroGames.cc and play it for free. Here are the steps to do so:</p>
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- <ol>
-<li>Go to <a href="">the game page</a> for The Grinch video game for PC on RetroGames.cc.</li>
-<li>Click on the "Play Game" button to launch the game in a new window.</li>
-<li>Wait for the game to load and start. You may need to enable Flash Player or allow pop-ups for the game to work properly.</li>
-<li>Use your keyboard or mouse to control the game. You can also adjust the settings and controls by clicking on the icons on the bottom of the window.</li>
-<li>Enjoy playing The Grinch video game online in your browser.</li>
-</ol>
- <p>Note that playing The Grinch video game online may not offer the same quality and performance as playing it offline. You may experience some lag, glitches, or errors while playing online. To avoid these issues, you can try the following solutions:</p>
- <ul>
-<li>Use a fast and stable internet connection. To check your internet speed, you can use a tool like Speedtest.net.</li>
-<li>Use a compatible and updated browser. The recommended browsers for RetroGames.cc are Chrome, Firefox, Edge, and Safari.</li>
-<li>Disable any extensions or plugins that may interfere with the game. For example, ad blockers, VPNs, or antivirus software.</li>
-<li>Clear your browser cache and cookies. To do this, go to your browser settings and find the option to clear browsing data.</li>
-</ul>
- <p>The advantages of playing The Grinch video game online are:</p>
- <ul>
-<li>You don't need to download or install anything on your PC.</li>
-<li>You can play the game anytime and anywhere as long as you have an internet connection and a browser.</li>
-<li>You can save your progress online and resume playing later.</li>
-</ul>
- <p>The disadvantages of playing The Grinch video game online are:</p>
- <ul>
-<li>You may not get the full features and quality of the game as offline.</li>
-<li>You may encounter some technical issues or errors while playing online.</li>
-<li>You may need to enable Flash Player or allow pop-ups for the game to work properly.</li>
-</ul>
- <h2>How to Enjoy The Grinch Video Game for PC</h2>
- <p>Now that you have downloaded or played The Grinch video game for PC, you may wonder how to enjoy it to the fullest. Here are some tips and tricks to help you have fun with the game:</p>
- <h3>Explore the Four Seussian Environments and Complete 24 Missions</h3>
- <p>The Grinch video game for PC features four different environments that are based on the movie: Whoville, Whoforest, Wholake, and Whomountains. Each environment has six missions that you need to complete in order to advance the story and unlock new gadgets and abilities. You can switch between controlling the Grinch and Max the Dog at any time by pressing the Tab key on your keyboard.</p>
- <p>The main goal of each mission is to sabotage the Whos' Christmas preparations by using various gadgets and abilities that you acquire throughout the game. Some of these gadgets and abilities are: rotten egg launcher, slime shooter, rocket spring shoes, octopus climbing device, bad breath, binoculars, stealth mode, helicopter ears, and more. You can access your gadgets and abilities by pressing the Spacebar key on your keyboard.</p>
- <p>Besides completing the missions, you can also explore the environments and collect various items that will help you progress in the game. Some of these items are: blueprint pieces, which allow you to create new gadgets; presents, which you can destroy with your gadgets or abilities; and bonus books, which unlock bonus levels and minigames. You can check your inventory and status by pressing the Esc key on your keyboard.</p>
- <h3>Watch the Cutscenes and Listen to the Narrator</h3>
- <p>The Grinch video game for PC follows the plot and style of the movie <em>How the Grinch Stole Christmas!</em>, which is based on the book by Dr. Seuss. The game features many cutscenes that show the events and characters from the movie, such as the Grinch's childhood, his encounter with Cindy Lou Who, his plan to steal Christmas, and his change of heart. The game also includes the movie trailer as a bonus feature.</p>
- <p>One of the most enjoyable aspects of The Grinch video game for PC is the narration by George Lowe, who does uncredited work as the voice of the narrator. Lowe is best known for his role as Space Ghost in the animated series <em>Space Ghost Coast to Coast</em>. Lowe adds humor and charm to the game by commenting on the Grinch's actions, making jokes and puns, and breaking the fourth wall. He also interacts with the Grinch and Max, who sometimes respond to him or ignore him.</p>
- <p>You can watch the cutscenes and listen to the narrator by playing the game normally or by accessing them from the game menu. To access the game menu, press the Esc key on your keyboard and select the "Extras" option. From there, you can choose to watch the cutscenes or the movie trailer.</p>
- <h3>Review the Game and Share Your Opinion</h3>
- <p>The Grinch video game for PC received mixed reviews from critics and players when it was released. Some praised it for its faithful adaptation of the movie, its colorful graphics, its humorous narration, and its varied gameplay. Others criticized it for its repetitive missions, its clunky controls, its low difficulty, and its technical issues.</p>
- <p>Some examples of reviews for The Grinch video game for PC are:</p>
- <blockquote>
-<p>"The Grinch is a decent platformer that fans of the movie should enjoy." - IGN</p>
-</blockquote>
- <blockquote>
-<p>"The Grinch is a game that only die-hard fans of either platform games or Jim Carrey's Grinch movie could love." - GameSpot</p>
-</blockquote>
- <blockquote>
-<p>"The Grinch is a fun game for kids and adults alike. It has a lot of humor, charm, and creativity. It is not very challenging, but it is very entertaining." - User review on Metacritic</p>
-</blockquote>
- <p>If you have played The Grinch video game for PC, you may want to share your own opinion about it with other fans. You can do so by rating and reviewing the game on various platforms and websites, such as Archive.org, RetroGames.cc, Amazon, Metacritic, or any other site that allows user feedback. You can also join online forums or communities that discuss The Grinch video game or related topics.</p>
- <p>By sharing your opinion, you can help other people decide whether to play The Grinch video game for PC or not. You can also express your appreciation or criticism of the game and its developers. You can also compare your experience with other players and see how they enjoyed or disliked the game.</p>
- <h2>Conclusion</h2>
- <p>The Grinch video game for PC is a classic game that is based on the 2000 movie <em>How the Grinch Stole Christmas!</em>, which is based on the book by Dr. Seuss. The game allows you to control the Grinch and his dog Max as they try to ruin Christmas for the Whos of Whoville by completing various missions and minigames in four different environments.</p>
- <p>You can download and play The Grinch video game for PC either from Archive.org or from RetroGames.cc. Both options are free and easy to use, but they may have some compatibility or technical issues depending on your PC and internet connection. You can also play The Grinch video game on other platforms, such as PlayStation, Dreamcast, or Game Boy Color.</p>
- <p>You can enjoy The Grinch video game for PC by exploring its features and gameplay, such as its gadgets and abilities, its cutscenes and narration, and its bonus levels and minigames. You can also review the game and share your opinion with other fans on various platforms and websites.</p>
- <p>We hope this article has helped you learn more about The Grinch video game for PC and how to play it. If you are looking for a fun and festive way to celebrate Christmas, you may want to give this game a try. You may find yourself laughing along with the narrator, sympathizing with the Grinch, or cheering for the Whos.</p>
- <p>Merry Christmas and Happy New Year!</p>
- <h2>FAQs</h2>
- <ul>
-<li><strong>Q: Is The Grinch video game suitable for children?</strong></li>
-<li>A: Yes, The Grinch video game is rated E for Everyone by ESRB and is suitable for children of all ages. The game is based on a family-friendly movie and has no violence, profanity, or inappropriate content.</li>
-<li><strong>Q: Can I play The Grinch video game on other platforms besides PC?</strong></li>
-<li>A: Yes, The Grinch video game was also released for PlayStation, Dreamcast, and Game Boy Color. However, these versions may be harder to find or more expensive than the PC version. You may also need an emulator or a compatible device to play them.</li>
-<li><strong>Q: How long does it take to finish The Grinch video game?</strong></li>
-<li>A: The length of The Grinch video game depends on how much you explore, collect, and replay. According to HowLongToBeat.com, the average time to complete the main story is about 6 hours, while completing everything can take up to 12 hours.</li>
-<li><strong>Q: Who developed and published The Grinch video game?</strong></li>
-<li>A: The Grinch video game was developed by Artificial Mind & Movement (PS & DC), Konami Computer Entertainment Nagoya (GBC), and Konami (PC). It was published by Konami for all platforms.</li>
-<li><strong>Q: Where can I find more information about The Grinch video game?</strong></li>
-<li>A: You can find more information about The Grinch video game on various websites, such as Wikipedia, TV Tropes, Amazon, or IGN. You can also watch gameplay videos on YouTube   or read user reviews on Metacritic.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/portaspeech/fs.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/portaspeech/fs.py
deleted file mode 100644
index 477cc6501bc700367eb43f17be4a7bf5822b6a1e..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/portaspeech/fs.py
+++ /dev/null
@@ -1,188 +0,0 @@
-from utils.hparams import hparams
-import torch
-from torch import nn
-import torch.nn.functional as F
-from modules.commons.conv import TextConvEncoder, ConvBlocks
-from modules.commons.common_layers import Embedding
-from modules.fastspeech.tts_modules import LayerNorm, PitchPredictor, LengthRegulator
-from modules.commons.rel_transformer import RelTransformerEncoder, BERTRelTransformerEncoder
-from modules.commons.align_ops import clip_mel2token_to_multiple, expand_states
-from utils.pitch_utils import denorm_f0, f0_to_coarse
-
-FS_ENCODERS = {
-    'rel_fft': lambda hp, dict: RelTransformerEncoder(
-        len(dict), hp['hidden_size'], hp['hidden_size'],
-        hp['ffn_hidden_size'], hp['num_heads'], hp['enc_layers'],
-        hp['enc_ffn_kernel_size'], hp['dropout'], prenet=hp['enc_prenet'], pre_ln=hp['enc_pre_ln']),
-}
-
-FS_DECODERS = {
-    'conv': lambda hp: ConvBlocks(hp['hidden_size'], hp['hidden_size'], hp['dec_dilations'],
-                                  hp['dec_kernel_size'], layers_in_block=hp['layers_in_block'],
-                                  norm_type=hp['enc_dec_norm'], dropout=hp['dropout'],
-                                  post_net_kernel=hp.get('dec_post_net_kernel', 3)),
-}
-
-class DurationPredictor(torch.nn.Module):
-    def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0):
-        super(DurationPredictor, self).__init__()
-        self.offset = offset
-        self.conv = torch.nn.ModuleList()
-        self.kernel_size = kernel_size
-        for idx in range(n_layers):
-            in_chans = idim if idx == 0 else n_chans
-            self.conv += [torch.nn.Sequential(
-                torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2),
-                torch.nn.ReLU(),
-                LayerNorm(n_chans, dim=1),
-                torch.nn.Dropout(dropout_rate)
-            )]
-        self.linear = nn.Sequential(torch.nn.Linear(n_chans, 1), nn.Softplus())
-
-    def forward(self, x, x_padding=None):
-        x = x.transpose(1, -1)  # (B, idim, Tmax)
-        for f in self.conv:
-            x = f(x)  # (B, C, Tmax)
-            if x_padding is not None:
-                x = x * (1 - x_padding.float())[:, None, :]
-
-        x = self.linear(x.transpose(1, -1))  # [B, T, C]
-        x = x * (1 - x_padding.float())[:, :, None]  # (B, T, C)
-        x = x[..., 0]  # (B, Tmax)
-        return x
-
-class FastSpeech(nn.Module):
-    def __init__(self, dict_size, out_dims=None):
-        super().__init__()
-        self.enc_layers = hparams['enc_layers']
-        self.dec_layers = hparams['dec_layers']
-        self.hidden_size = hparams['hidden_size']
-        if hparams.get("use_bert") is True:
-            self.ph_encoder = BERTRelTransformerEncoder(dict_size, hparams['hidden_size'], hparams['hidden_size'],
-                hparams['ffn_hidden_size'], hparams['num_heads'], hparams['enc_layers'],
-                hparams['enc_ffn_kernel_size'], hparams['dropout'], prenet=hparams['enc_prenet'], pre_ln=hparams['enc_pre_ln'])
-        else:
-            self.ph_encoder = FS_ENCODERS[hparams['encoder_type']](hparams, dict_size)
-        self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
-        self.out_dims = hparams['audio_num_mel_bins'] if out_dims is None else out_dims
-        self.mel_out = nn.Linear(self.hidden_size, self.out_dims, bias=True)
-        if hparams['use_spk_id']:
-            self.spk_id_proj = Embedding(hparams['num_spk'], self.hidden_size)
-        if hparams['use_spk_embed']:
-            self.spk_embed_proj = nn.Linear(256, self.hidden_size, bias=True)
-        predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
-        self.dur_predictor = DurationPredictor(
-            self.hidden_size,
-            n_chans=predictor_hidden,
-            n_layers=hparams['dur_predictor_layers'],
-            dropout_rate=hparams['predictor_dropout'],
-            kernel_size=hparams['dur_predictor_kernel'])
-        self.length_regulator = LengthRegulator()
-        if hparams['use_pitch_embed']:
-            self.pitch_embed = Embedding(300, self.hidden_size, 0)
-            self.pitch_predictor = PitchPredictor(
-                self.hidden_size, n_chans=predictor_hidden,
-                n_layers=5, dropout_rate=0.1, odim=2,
-                kernel_size=hparams['predictor_kernel'])
-        if hparams['dec_inp_add_noise']:
-            self.z_channels = hparams['z_channels']
-            self.dec_inp_noise_proj = nn.Linear(self.hidden_size + self.z_channels, self.hidden_size)
-
-    def forward(self, txt_tokens, mel2ph=None, spk_embed=None, spk_id=None,
-                f0=None, uv=None, infer=False, **kwargs):
-        ret = {}
-        src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-        style_embed = self.forward_style_embed(spk_embed, spk_id)
-
-        use_bert = hparams.get("use_bert") is True
-        if use_bert:
-            encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=kwargs['ph2word'],
-                ret=ret) * src_nonpadding + style_embed
-        else:
-            encoder_out = self.encoder(txt_tokens) * src_nonpadding + style_embed
-
-        # add dur
-        dur_inp = (encoder_out + style_embed) * src_nonpadding
-        mel2ph = self.forward_dur(dur_inp, mel2ph, txt_tokens, ret)
-        tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
-        decoder_inp = expand_states(encoder_out, mel2ph)
-
-        # add pitch embed
-        if hparams['use_pitch_embed']:
-            pitch_inp = (decoder_inp + style_embed) * tgt_nonpadding
-            decoder_inp = decoder_inp + self.forward_pitch(pitch_inp, f0, uv, mel2ph, ret, encoder_out)
-
-        # decoder input
-        ret['decoder_inp'] = decoder_inp = (decoder_inp + style_embed) * tgt_nonpadding
-        if hparams['dec_inp_add_noise']:
-            B, T, _ = decoder_inp.shape
-            z = kwargs.get('adv_z', torch.randn([B, T, self.z_channels])).to(decoder_inp.device)
-            ret['adv_z'] = z
-            decoder_inp = torch.cat([decoder_inp, z], -1)
-            decoder_inp = self.dec_inp_noise_proj(decoder_inp) * tgt_nonpadding
-        ret['mel_out'] = self.forward_decoder(decoder_inp, tgt_nonpadding, ret, infer=infer, **kwargs)
-        return ret
-
-    def forward_style_embed(self, spk_embed=None, spk_id=None):
-        # add spk embed
-        style_embed = 0
-        if hparams['use_spk_embed']:
-            style_embed = style_embed + self.spk_embed_proj(spk_embed)[:, None, :]
-        if hparams['use_spk_id']:
-            style_embed = style_embed + self.spk_id_proj(spk_id)[:, None, :]
-        return style_embed
-
-    def forward_dur(self, dur_input, mel2ph, txt_tokens, ret):
-        """
-
-        :param dur_input: [B, T_txt, H]
-        :param mel2ph: [B, T_mel]
-        :param txt_tokens: [B, T_txt]
-        :param ret:
-        :return:
-        """
-        src_padding = txt_tokens == 0
-        if hparams['predictor_grad'] != 1:
-            dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
-        dur = self.dur_predictor(dur_input, src_padding)
-        ret['dur'] = dur
-        if mel2ph is None:
-            mel2ph = self.length_regulator(dur, src_padding).detach()
-        ret['mel2ph'] = mel2ph = clip_mel2token_to_multiple(mel2ph, hparams['frames_multiple'])
-        return mel2ph
-
-    def forward_pitch(self, decoder_inp, f0, uv, mel2ph, ret, encoder_out=None):
-        if hparams['pitch_type'] == 'frame':
-            pitch_pred_inp = decoder_inp
-            pitch_padding = mel2ph == 0
-        else:
-            pitch_pred_inp = encoder_out
-            pitch_padding = encoder_out.abs().sum(-1) == 0
-            uv = None
-        if hparams['predictor_grad'] != 1:
-            pitch_pred_inp = pitch_pred_inp.detach() + \
-                             hparams['predictor_grad'] * (pitch_pred_inp - pitch_pred_inp.detach())
-        ret['pitch_pred'] = pitch_pred = self.pitch_predictor(pitch_pred_inp)
-        use_uv = hparams['pitch_type'] == 'frame' and hparams['use_uv']
-        if f0 is None:
-            f0 = pitch_pred[:, :, 0]
-            if use_uv:
-                uv = pitch_pred[:, :, 1] > 0
-        f0_denorm = denorm_f0(f0, uv if use_uv else None, pitch_padding=pitch_padding)
-        pitch = f0_to_coarse(f0_denorm)  # start from 0 [B, T_txt]
-        ret['f0_denorm'] = f0_denorm
-        ret['f0_denorm_pred'] = denorm_f0(
-            pitch_pred[:, :, 0], (pitch_pred[:, :, 1] > 0) if use_uv else None,
-            pitch_padding=pitch_padding)
-        if hparams['pitch_type'] == 'ph':
-            pitch = torch.gather(F.pad(pitch, [1, 0]), 1, mel2ph)
-            ret['f0_denorm'] = torch.gather(F.pad(ret['f0_denorm'], [1, 0]), 1, mel2ph)
-            ret['f0_denorm_pred'] = torch.gather(F.pad(ret['f0_denorm_pred'], [1, 0]), 1, mel2ph)
-        pitch_embed = self.pitch_embed(pitch)
-        return pitch_embed
-
-    def forward_decoder(self, decoder_inp, tgt_nonpadding, ret, infer, **kwargs):
-        x = decoder_inp  # [B, T, H]
-        x = self.decoder(x)
-        x = self.mel_out(x)
-        return x * tgt_nonpadding
diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/crash.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/crash.py
deleted file mode 100644
index 98a06e20bc793687ec259e23c8b9e503887b34f5..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/crash.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import sys
-
-class ExceptionHook:
-    instance = None
-    def __call__(self, *args, **kwargs):
-        if self.instance is None:
-            from IPython.core import ultratb
-            self.instance = ultratb.FormattedTB(mode='Plain',
-                 color_scheme='Linux', call_pdb=1)
-        return self.instance(*args, **kwargs)
-
-sys.excepthook = ExceptionHook()
diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual_dis.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual_dis.py
deleted file mode 100644
index be47add559612aaf8c667afd554d88e23fd8fd56..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual_dis.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import sys
-
-sys.path.insert(0, '.')  # nopep8
-from ldm.modules.losses_audio.vqperceptual import *
-from ldm.modules.discriminator.multi_window_disc import Discriminator
-
-class LPAPSWithDiscriminator(nn.Module):# 相比于contperceptual.py添加了MultiWindowDiscriminator
-    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_loss="hinge"):
-
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        self.kl_weight = kl_weight
-        self.pixel_weight = pixelloss_weight
-        self.perceptual_loss = LPAPS().eval()
-        self.perceptual_weight = perceptual_weight
-        # output log variance
-        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"LPAPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-
-        disc_win_num = 3
-        mel_disc_hidden_size = 128
-        self.discriminator_multi = Discriminator(time_lengths=[32, 64, 128][:disc_win_num],
-                freq_length=80, hidden_size=mel_disc_hidden_size, kernel=(3, 3),
-                cond_size=0, norm_type="in", reduction="stack")
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", weights=None):
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
-        weighted_nll_loss = nll_loss
-        if weights is not None:
-            weighted_nll_loss = weights*nll_loss
-        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
-        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        kl_loss = posteriors.kl()
-        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-
-            logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().squeeze(1).transpose(1, 2))
-
-            g_loss = -torch.mean(logits_fake)
-            g_loss_multi = -torch.mean(logits_fake_multi['y'])
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-                d_weight_multi = self.calculate_adaptive_weight(nll_loss, g_loss_multi, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = d_weight_multi = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss + d_weight_multi * disc_factor * g_loss_multi
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/logvar".format(split): self.logvar.detach(),
-                   "{}/kl_loss".format(split): kl_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   "{}/g_loss_multi".format(split): g_loss_multi.detach().mean(),
-                   }
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            logits_real_multi = self.discriminator_multi(inputs.contiguous().detach().squeeze(1).transpose(1, 2))
-            logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().detach().squeeze(1).transpose(1, 2))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-            d_loss_multi = disc_factor * self.disc_loss(logits_real_multi['y'], logits_fake_multi['y'])
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/disc_loss_multi".format(split): d_loss_multi.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss+d_loss_multi, log
-
diff --git a/spaces/ASJMO/freegpt/Dockerfile b/spaces/ASJMO/freegpt/Dockerfile
deleted file mode 100644
index 7ac29c145f7d05ea9b1344e50e634629c9d88984..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python:3.10-slim-buster  
-  
-WORKDIR /app  
-  
-COPY requirements.txt requirements.txt  
-  
-RUN python -m venv venv  
-ENV PATH="/app/venv/bin:$PATH"  
-  
-RUN apt-get update && \  
-    apt-get install -y --no-install-recommends build-essential libffi-dev cmake libcurl4-openssl-dev && \  
-    pip3 install --no-cache-dir -r requirements.txt  
-  
-COPY . .  
-  
-RUN chmod -R 777 translations  
-  
-CMD ["python3", "./run.py"]  
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/_base_/default_runtime.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/_base_/default_runtime.py
deleted file mode 100644
index 2f0db2e3d303e6d245c36610aab5b6fcb3193480..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/_base_/default_runtime.py
+++ /dev/null
@@ -1,34 +0,0 @@
-default_scope = 'mmyolo'
-
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=50),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(type='CheckpointHook', interval=1),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    visualization=dict(type='mmdet.DetVisualizationHook'))
-
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'),
-)
-
-vis_backends = [dict(type='LocalVisBackend')]
-visualizer = dict(
-    type='mmdet.DetLocalVisualizer',
-    vis_backends=vis_backends,
-    name='visualizer')
-log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
-
-log_level = 'INFO'
-load_from = None
-resume = False
-
-# file_client_args = dict(
-#         backend='petrel',
-#         path_mapping=dict({
-#             './data/': 's3://openmmlab/datasets/detection/',
-#             'data/': 's3://openmmlab/datasets/detection/'
-#         }))
-file_client_args = dict(backend='disk')
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/transforms.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample
diff --git a/spaces/AdithyaSNair/Diabetes_analysis/README.md b/spaces/AdithyaSNair/Diabetes_analysis/README.md
deleted file mode 100644
index 35fdec3dcda963df0caf832c67a1b83dc663294c..0000000000000000000000000000000000000000
--- a/spaces/AdithyaSNair/Diabetes_analysis/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Diabetes Analysis
-emoji: 🦀
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/intouching/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/intouching/Factory.js
deleted file mode 100644
index adbe4aae65efc9f4ecfcc899a1b1b505f2100cfc..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/intouching/Factory.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import InTouching from './InTouching.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('inTouching', function (gameObject, config) {
-    return new InTouching(gameObject, config);
-});
-
-SetValue(window, 'RexPlugins.UI.InTouching', InTouching);
-
-export default InTouching;
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_consistency_to_diffusers.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_consistency_to_diffusers.py
deleted file mode 100644
index 5a6158bb9867e5b4d5546a0b7e820ee94243f183..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_consistency_to_diffusers.py
+++ /dev/null
@@ -1,313 +0,0 @@
-import argparse
-import os
-
-import torch
-
-from diffusers import (
-    CMStochasticIterativeScheduler,
-    ConsistencyModelPipeline,
-    UNet2DModel,
-)
-
-
-TEST_UNET_CONFIG = {
-    "sample_size": 32,
-    "in_channels": 3,
-    "out_channels": 3,
-    "layers_per_block": 2,
-    "num_class_embeds": 1000,
-    "block_out_channels": [32, 64],
-    "attention_head_dim": 8,
-    "down_block_types": [
-        "ResnetDownsampleBlock2D",
-        "AttnDownBlock2D",
-    ],
-    "up_block_types": [
-        "AttnUpBlock2D",
-        "ResnetUpsampleBlock2D",
-    ],
-    "resnet_time_scale_shift": "scale_shift",
-    "upsample_type": "resnet",
-    "downsample_type": "resnet",
-}
-
-IMAGENET_64_UNET_CONFIG = {
-    "sample_size": 64,
-    "in_channels": 3,
-    "out_channels": 3,
-    "layers_per_block": 3,
-    "num_class_embeds": 1000,
-    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
-    "attention_head_dim": 64,
-    "down_block_types": [
-        "ResnetDownsampleBlock2D",
-        "AttnDownBlock2D",
-        "AttnDownBlock2D",
-        "AttnDownBlock2D",
-    ],
-    "up_block_types": [
-        "AttnUpBlock2D",
-        "AttnUpBlock2D",
-        "AttnUpBlock2D",
-        "ResnetUpsampleBlock2D",
-    ],
-    "resnet_time_scale_shift": "scale_shift",
-    "upsample_type": "resnet",
-    "downsample_type": "resnet",
-}
-
-LSUN_256_UNET_CONFIG = {
-    "sample_size": 256,
-    "in_channels": 3,
-    "out_channels": 3,
-    "layers_per_block": 2,
-    "num_class_embeds": None,
-    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
-    "attention_head_dim": 64,
-    "down_block_types": [
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-        "ResnetDownsampleBlock2D",
-        "AttnDownBlock2D",
-        "AttnDownBlock2D",
-        "AttnDownBlock2D",
-    ],
-    "up_block_types": [
-        "AttnUpBlock2D",
-        "AttnUpBlock2D",
-        "AttnUpBlock2D",
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-        "ResnetUpsampleBlock2D",
-    ],
-    "resnet_time_scale_shift": "default",
-    "upsample_type": "resnet",
-    "downsample_type": "resnet",
-}
-
-CD_SCHEDULER_CONFIG = {
-    "num_train_timesteps": 40,
-    "sigma_min": 0.002,
-    "sigma_max": 80.0,
-}
-
-CT_IMAGENET_64_SCHEDULER_CONFIG = {
-    "num_train_timesteps": 201,
-    "sigma_min": 0.002,
-    "sigma_max": 80.0,
-}
-
-CT_LSUN_256_SCHEDULER_CONFIG = {
-    "num_train_timesteps": 151,
-    "sigma_min": 0.002,
-    "sigma_max": 80.0,
-}
-
-
-def str2bool(v):
-    """
-    https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
-    """
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ("yes", "true", "t", "y", "1"):
-        return True
-    elif v.lower() in ("no", "false", "f", "n", "0"):
-        return False
-    else:
-        raise argparse.ArgumentTypeError("boolean value expected")
-
-
-def convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=False):
-    new_checkpoint[f"{new_prefix}.norm1.weight"] = checkpoint[f"{old_prefix}.in_layers.0.weight"]
-    new_checkpoint[f"{new_prefix}.norm1.bias"] = checkpoint[f"{old_prefix}.in_layers.0.bias"]
-    new_checkpoint[f"{new_prefix}.conv1.weight"] = checkpoint[f"{old_prefix}.in_layers.2.weight"]
-    new_checkpoint[f"{new_prefix}.conv1.bias"] = checkpoint[f"{old_prefix}.in_layers.2.bias"]
-    new_checkpoint[f"{new_prefix}.time_emb_proj.weight"] = checkpoint[f"{old_prefix}.emb_layers.1.weight"]
-    new_checkpoint[f"{new_prefix}.time_emb_proj.bias"] = checkpoint[f"{old_prefix}.emb_layers.1.bias"]
-    new_checkpoint[f"{new_prefix}.norm2.weight"] = checkpoint[f"{old_prefix}.out_layers.0.weight"]
-    new_checkpoint[f"{new_prefix}.norm2.bias"] = checkpoint[f"{old_prefix}.out_layers.0.bias"]
-    new_checkpoint[f"{new_prefix}.conv2.weight"] = checkpoint[f"{old_prefix}.out_layers.3.weight"]
-    new_checkpoint[f"{new_prefix}.conv2.bias"] = checkpoint[f"{old_prefix}.out_layers.3.bias"]
-
-    if has_skip:
-        new_checkpoint[f"{new_prefix}.conv_shortcut.weight"] = checkpoint[f"{old_prefix}.skip_connection.weight"]
-        new_checkpoint[f"{new_prefix}.conv_shortcut.bias"] = checkpoint[f"{old_prefix}.skip_connection.bias"]
-
-    return new_checkpoint
-
-
-def convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_dim=None):
-    weight_q, weight_k, weight_v = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3, dim=0)
-    bias_q, bias_k, bias_v = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3, dim=0)
-
-    new_checkpoint[f"{new_prefix}.group_norm.weight"] = checkpoint[f"{old_prefix}.norm.weight"]
-    new_checkpoint[f"{new_prefix}.group_norm.bias"] = checkpoint[f"{old_prefix}.norm.bias"]
-
-    new_checkpoint[f"{new_prefix}.to_q.weight"] = weight_q.squeeze(-1).squeeze(-1)
-    new_checkpoint[f"{new_prefix}.to_q.bias"] = bias_q.squeeze(-1).squeeze(-1)
-    new_checkpoint[f"{new_prefix}.to_k.weight"] = weight_k.squeeze(-1).squeeze(-1)
-    new_checkpoint[f"{new_prefix}.to_k.bias"] = bias_k.squeeze(-1).squeeze(-1)
-    new_checkpoint[f"{new_prefix}.to_v.weight"] = weight_v.squeeze(-1).squeeze(-1)
-    new_checkpoint[f"{new_prefix}.to_v.bias"] = bias_v.squeeze(-1).squeeze(-1)
-
-    new_checkpoint[f"{new_prefix}.to_out.0.weight"] = (
-        checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1).squeeze(-1)
-    )
-    new_checkpoint[f"{new_prefix}.to_out.0.bias"] = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1).squeeze(-1)
-
-    return new_checkpoint
-
-
-def con_pt_to_diffuser(checkpoint_path: str, unet_config):
-    checkpoint = torch.load(checkpoint_path, map_location="cpu")
-    new_checkpoint = {}
-
-    new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"]
-    new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"]
-    new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"]
-    new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"]
-
-    if unet_config["num_class_embeds"] is not None:
-        new_checkpoint["class_embedding.weight"] = checkpoint["label_emb.weight"]
-
-    new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"]
-    new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"]
-
-    down_block_types = unet_config["down_block_types"]
-    layers_per_block = unet_config["layers_per_block"]
-    attention_head_dim = unet_config["attention_head_dim"]
-    channels_list = unet_config["block_out_channels"]
-    current_layer = 1
-    prev_channels = channels_list[0]
-
-    for i, layer_type in enumerate(down_block_types):
-        current_channels = channels_list[i]
-        downsample_block_has_skip = current_channels != prev_channels
-        if layer_type == "ResnetDownsampleBlock2D":
-            for j in range(layers_per_block):
-                new_prefix = f"down_blocks.{i}.resnets.{j}"
-                old_prefix = f"input_blocks.{current_layer}.0"
-                has_skip = True if j == 0 and downsample_block_has_skip else False
-                new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip)
-                current_layer += 1
-
-        elif layer_type == "AttnDownBlock2D":
-            for j in range(layers_per_block):
-                new_prefix = f"down_blocks.{i}.resnets.{j}"
-                old_prefix = f"input_blocks.{current_layer}.0"
-                has_skip = True if j == 0 and downsample_block_has_skip else False
-                new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip)
-                new_prefix = f"down_blocks.{i}.attentions.{j}"
-                old_prefix = f"input_blocks.{current_layer}.1"
-                new_checkpoint = convert_attention(
-                    checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim
-                )
-                current_layer += 1
-
-        if i != len(down_block_types) - 1:
-            new_prefix = f"down_blocks.{i}.downsamplers.0"
-            old_prefix = f"input_blocks.{current_layer}.0"
-            new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)
-            current_layer += 1
-
-        prev_channels = current_channels
-
-    # hardcoded the mid-block for now
-    new_prefix = "mid_block.resnets.0"
-    old_prefix = "middle_block.0"
-    new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)
-    new_prefix = "mid_block.attentions.0"
-    old_prefix = "middle_block.1"
-    new_checkpoint = convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim)
-    new_prefix = "mid_block.resnets.1"
-    old_prefix = "middle_block.2"
-    new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)
-
-    current_layer = 0
-    up_block_types = unet_config["up_block_types"]
-
-    for i, layer_type in enumerate(up_block_types):
-        if layer_type == "ResnetUpsampleBlock2D":
-            for j in range(layers_per_block + 1):
-                new_prefix = f"up_blocks.{i}.resnets.{j}"
-                old_prefix = f"output_blocks.{current_layer}.0"
-                new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True)
-                current_layer += 1
-
-            if i != len(up_block_types) - 1:
-                new_prefix = f"up_blocks.{i}.upsamplers.0"
-                old_prefix = f"output_blocks.{current_layer-1}.1"
-                new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)
-        elif layer_type == "AttnUpBlock2D":
-            for j in range(layers_per_block + 1):
-                new_prefix = f"up_blocks.{i}.resnets.{j}"
-                old_prefix = f"output_blocks.{current_layer}.0"
-                new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True)
-                new_prefix = f"up_blocks.{i}.attentions.{j}"
-                old_prefix = f"output_blocks.{current_layer}.1"
-                new_checkpoint = convert_attention(
-                    checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim
-                )
-                current_layer += 1
-
-            if i != len(up_block_types) - 1:
-                new_prefix = f"up_blocks.{i}.upsamplers.0"
-                old_prefix = f"output_blocks.{current_layer-1}.2"
-                new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix)
-
-    new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"]
-    new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"]
-    new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"]
-    new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"]
-
-    return new_checkpoint
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
-    parser.add_argument(
-        "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
-    )
-    parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")
-
-    args = parser.parse_args()
-    args.class_cond = str2bool(args.class_cond)
-
-    ckpt_name = os.path.basename(args.unet_path)
-    print(f"Checkpoint: {ckpt_name}")
-
-    # Get U-Net config
-    if "imagenet64" in ckpt_name:
-        unet_config = IMAGENET_64_UNET_CONFIG
-    elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
-        unet_config = LSUN_256_UNET_CONFIG
-    elif "test" in ckpt_name:
-        unet_config = TEST_UNET_CONFIG
-    else:
-        raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")
-
-    if not args.class_cond:
-        unet_config["num_class_embeds"] = None
-
-    converted_unet_ckpt = con_pt_to_diffuser(args.unet_path, unet_config)
-
-    image_unet = UNet2DModel(**unet_config)
-    image_unet.load_state_dict(converted_unet_ckpt)
-
-    # Get scheduler config
-    if "cd" in ckpt_name or "test" in ckpt_name:
-        scheduler_config = CD_SCHEDULER_CONFIG
-    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
-        scheduler_config = CT_IMAGENET_64_SCHEDULER_CONFIG
-    elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
-        scheduler_config = CT_LSUN_256_SCHEDULER_CONFIG
-    else:
-        raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")
-
-    cm_scheduler = CMStochasticIterativeScheduler(**scheduler_config)
-
-    consistency_model = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
-    consistency_model.save_pretrained(args.dump_path)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_vae_pt_to_diffusers.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_vae_pt_to_diffusers.py
deleted file mode 100644
index a8ba48bc001c86186d3aad9ee3bcd208ceca5ea3..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_vae_pt_to_diffusers.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import argparse
-import io
-
-import requests
-import torch
-from omegaconf import OmegaConf
-
-from diffusers import AutoencoderKL
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
-    assign_to_checkpoint,
-    conv_attn_to_linear,
-    create_vae_diffusers_config,
-    renew_vae_attention_paths,
-    renew_vae_resnet_paths,
-)
-
-
-def custom_convert_ldm_vae_checkpoint(checkpoint, config):
-    vae_state_dict = checkpoint
-
-    new_checkpoint = {}
-
-    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
-    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
-    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
-    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
-    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
-    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
-
-    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
-    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
-    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
-    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
-    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
-    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
-
-    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
-    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
-    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
-    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
-
-    # Retrieves the keys for the encoder down blocks only
-    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
-    down_blocks = {
-        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
-    }
-
-    # Retrieves the keys for the decoder up blocks only
-    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
-    up_blocks = {
-        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
-    }
-
-    for i in range(num_down_blocks):
-        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
-
-        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.weight"
-            )
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.bias"
-            )
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-    conv_attn_to_linear(new_checkpoint)
-
-    for i in range(num_up_blocks):
-        block_id = num_up_blocks - 1 - i
-        resnets = [
-            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
-        ]
-
-        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.weight"
-            ]
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.bias"
-            ]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-    conv_attn_to_linear(new_checkpoint)
-    return new_checkpoint
-
-
-def vae_pt_to_vae_diffuser(
-    checkpoint_path: str,
-    output_path: str,
-):
-    # Only support V1
-    r = requests.get(
-        " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
-    )
-    io_obj = io.BytesIO(r.content)
-
-    original_config = OmegaConf.load(io_obj)
-    image_size = 512
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    if checkpoint_path.endswith("safetensors"):
-        from safetensors import safe_open
-
-        checkpoint = {}
-        with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
-            for key in f.keys():
-                checkpoint[key] = f.get_tensor(key)
-    else:
-        checkpoint = torch.load(checkpoint_path, map_location=device)["state_dict"]
-
-    # Convert the VAE model.
-    vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
-    converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint, vae_config)
-
-    vae = AutoencoderKL(**vae_config)
-    vae.load_state_dict(converted_vae_checkpoint)
-    vae.save_pretrained(output_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
-
-    args = parser.parse_args()
-
-    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker.py
deleted file mode 100644
index 38c7b22d08d43ade5fe7979f5514ec973109fd82..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import torch
-import torch.nn as nn
-from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
-
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-def cosine_distance(image_embeds, text_embeds):
-    normalized_image_embeds = nn.functional.normalize(image_embeds)
-    normalized_text_embeds = nn.functional.normalize(text_embeds)
-    return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
-
-
-class StableDiffusionSafetyChecker(PreTrainedModel):
-    config_class = CLIPConfig
-
-    _no_split_modules = ["CLIPEncoderLayer"]
-
-    def __init__(self, config: CLIPConfig):
-        super().__init__(config)
-
-        self.vision_model = CLIPVisionModel(config.vision_config)
-        self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
-
-        self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
-        self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
-
-        self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
-        self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
-
-    @torch.no_grad()
-    def forward(self, clip_input, images):
-        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
-        image_embeds = self.visual_projection(pooled_output)
-
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
-
-        result = []
-        batch_size = image_embeds.shape[0]
-        for i in range(batch_size):
-            result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
-
-            # increase this value to create a stronger `nfsw` filter
-            # at the cost of increasing the possibility of filtering benign images
-            adjustment = 0.0
-
-            for concept_idx in range(len(special_cos_dist[0])):
-                concept_cos = special_cos_dist[i][concept_idx]
-                concept_threshold = self.special_care_embeds_weights[concept_idx].item()
-                result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["special_scores"][concept_idx] > 0:
-                    result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
-                    adjustment = 0.01
-
-            for concept_idx in range(len(cos_dist[0])):
-                concept_cos = cos_dist[i][concept_idx]
-                concept_threshold = self.concept_embeds_weights[concept_idx].item()
-                result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["concept_scores"][concept_idx] > 0:
-                    result_img["bad_concepts"].append(concept_idx)
-
-            result.append(result_img)
-
-        has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
-
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                if torch.is_tensor(images) or torch.is_tensor(images[0]):
-                    images[idx] = torch.zeros_like(images[idx])  # black image
-                else:
-                    images[idx] = np.zeros(images[idx].shape)  # black image
-
-        if any(has_nsfw_concepts):
-            logger.warning(
-                "Potential NSFW content was detected in one or more images. A black image will be returned instead."
-                " Try again with a different prompt and/or seed."
-            )
-
-        return images, has_nsfw_concepts
-
-    @torch.no_grad()
-    def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
-        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
-        image_embeds = self.visual_projection(pooled_output)
-
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds)
-
-        # increase this value to create a stronger `nsfw` filter
-        # at the cost of increasing the possibility of filtering benign images
-        adjustment = 0.0
-
-        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
-        # special_scores = special_scores.round(decimals=3)
-        special_care = torch.any(special_scores > 0, dim=1)
-        special_adjustment = special_care * 0.01
-        special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
-
-        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
-        # concept_scores = concept_scores.round(decimals=3)
-        has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
-
-        images[has_nsfw_concepts] = 0.0  # black image
-
-        return images, has_nsfw_concepts
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py b/spaces/Andy1621/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
deleted file mode 100644
index 7866acebea689e7a863a836c326b1407de733fe8..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
+++ /dev/null
@@ -1,14 +0,0 @@
-_base_ = [
-    '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
-    '../_base_/default_runtime.py'
-]
-model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-# actual epoch = 3 * 3 = 9
-lr_config = dict(policy='step', step=[3])
-# runtime settings
-runner = dict(
-    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
deleted file mode 100644
index f34373d9ebab5ef6f4c01e3eab8a97c288495be0..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './encnet_r50-d8_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py
deleted file mode 100644
index b1adfbab882d9825a3f348ed99e401d1f164cd11..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/optimize.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/optimize.py
deleted file mode 100644
index acebf21248cd1bf2a769296e4cbe4c6645d46ef8..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superboogav2/optimize.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""
-This module implements a hyperparameter optimization routine for the embedding application. It utilizes TPE optimization from Optuna.
-
-Each run, the optimizer will set the default values inside the hyperparameters. At the end, it will output the best ones it has found.
-"""
-import re
-import json
-import optuna
-import gradio as gr
-import numpy as np
-import logging
-import hashlib
-logging.getLogger('optuna').setLevel(logging.WARNING)
-
-import extensions.superboogav2.parameters as parameters
-
-from pathlib import Path
-
-from .benchmark import benchmark
-from .parameters import Parameters
-from modules.logging_colors import logger
-
-
-# Format the parameters into markdown format.
-def _markdown_hyperparams():
-    res = []
-    for param_name, param_value in Parameters.getInstance().hyperparameters.items():
-        # Escape any markdown syntax
-        param_name = re.sub(r"([_*\[\]()~`>#+-.!])", r"\\\1", param_name)
-        param_value_default = re.sub(r"([_*\[\]()~`>#+-.!])", r"\\\1", str(param_value['default'])) if param_value['default'] else ' '
-        
-        res.append('* {}: **{}**'.format(param_name, param_value_default))
-
-    return '\n'.join(res)
-
-
-# Convert numpy types to python types.
-def _convert_np_types(params):
-    for key in params:
-        if type(params[key]) == np.bool_:
-            params[key] = bool(params[key])
-        elif type(params[key]) == np.int64:
-            params[key] = int(params[key])
-        elif type(params[key]) == np.float64:
-            params[key] = float(params[key])
-    return params
-
-
-# Set the default values for the hyperparameters.
-def _set_hyperparameters(params):
-    for param_name, param_value in params.items():
-        if param_name in Parameters.getInstance().hyperparameters: 
-            Parameters.getInstance().hyperparameters[param_name]['default'] = param_value
-
-
-# Check if the parameter is for optimization.
-def _is_optimization_param(val):
-    is_opt = val.get('should_optimize', False) # Either does not exist or is false
-    return is_opt
-
-
-# Create a hashable representation of the parameters
-def _get_params_hash(params):
-    params_str = json.dumps(params, sort_keys=True)
-    return hashlib.sha256(params_str.encode()).hexdigest()
-
-
-def optimize(collector, progress=gr.Progress()):
-    # Inform the user that something is happening.
-    progress(0, desc=f'Setting Up...')
-
-    # Track the current step
-    current_step = 0
-
-    # Track the best score
-    best_score = 0
-
-    # Dictionary for caching scores
-    scores_cache = {}
-
-    def objective_function(trial):
-        nonlocal current_step
-        nonlocal best_score
-        nonlocal scores_cache
-
-        params = {}
-        for key, val in Parameters.getInstance().hyperparameters.items():
-            if _is_optimization_param(val):
-                params[key] = trial.suggest_categorical(key, val['categories'])
-
-        _set_hyperparameters(params)
-
-        params_hash = _get_params_hash(params)
-
-        # If the score for these parameters is in the cache, return it
-        if params_hash in scores_cache:
-            return scores_cache[params_hash]
-
-        # Benchmark the current set of parameters.
-        score, max_score = benchmark(Path("extensions/superboogav2/benchmark_texts/questions.json"), collector)
-
-        # Cache the score
-        scores_cache[params_hash] = score
-
-        result = json.dumps(_convert_np_types(params), indent=4)
-        result += f'\nScore: {score}/{max_score}'
-
-        logger.debug(result)
-
-        # Increment the current step
-        current_step += 1
-
-        # Update the best score
-        best_score = max(best_score, score)
-
-        # Update the progress
-        progress(current_step / parameters.get_optimization_steps(), desc=f'Optimizing... {current_step}/{parameters.get_optimization_steps()}')
-
-        return -score
-
-    # Run the optimization.
-    study = optuna.create_study()
-    study.optimize(objective_function, n_trials=int(parameters.get_optimization_steps()))
-
-    best_params = study.best_params
-    _set_hyperparameters(best_params)
-
-    # Convert results to a markdown string.
-    str_result = f"## Best parameters:\n\n{_markdown_hyperparams()}\n\n## Score:\n\n{best_score}"
-
-    # Save to JSON file
-    with open('best_params.json', 'w') as fp:
-        json.dump(_convert_np_types(best_params), fp, indent=4)
-
-    return str_result
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/alexnet.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/alexnet.py
deleted file mode 100644
index 89e36b8c7851f895d9ae7f07149f0e707456aab0..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/alexnet.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-
-import torch.nn as nn
-
-
-class AlexNet(nn.Module):
-    """AlexNet backbone.
-
-    Args:
-        num_classes (int): number of classes for classification.
-    """
-
-    def __init__(self, num_classes=-1):
-        super(AlexNet, self).__init__()
-        self.num_classes = num_classes
-        self.features = nn.Sequential(
-            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=3, stride=2),
-            nn.Conv2d(64, 192, kernel_size=5, padding=2),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=3, stride=2),
-            nn.Conv2d(192, 384, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(384, 256, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(256, 256, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(kernel_size=3, stride=2),
-        )
-        if self.num_classes > 0:
-            self.classifier = nn.Sequential(
-                nn.Dropout(),
-                nn.Linear(256 * 6 * 6, 4096),
-                nn.ReLU(inplace=True),
-                nn.Dropout(),
-                nn.Linear(4096, 4096),
-                nn.ReLU(inplace=True),
-                nn.Linear(4096, num_classes),
-            )
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            from ..runner import load_checkpoint
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            # use default initializer
-            pass
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-
-        x = self.features(x)
-        if self.num_classes > 0:
-            x = x.view(x.size(0), 256 * 6 * 6)
-            x = self.classifier(x)
-
-        return x
diff --git a/spaces/Artrajz/vits-simple-api/bert_vits2/README.md b/spaces/Artrajz/vits-simple-api/bert_vits2/README.md
deleted file mode 100644
index 2d2c104fed4165f60ab2940f4642e36230e12e32..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/bert_vits2/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Bert-VITS2
-
-VITS2 Backbone with bert
-## 成熟的旅行者/开拓者/舰长/博士/sensei/猎魔人/喵喵露/V应该参阅代码自己学习如何训练。
-### 严禁将此项目用于一切违反《中华人民共和国宪法》，《中华人民共和国刑法》，《中华人民共和国治安管理处罚法》和《中华人民共和国民法典》之用途。
\ No newline at end of file
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/README.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/README.md
deleted file mode 100644
index 9fcd33513fb81ef3aeb4d3c8d9732324dffa2646..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-
-This directory contains code to prepare a detectron2 model for deployment.
-Currently it supports exporting a detectron2 model to Caffe2 format through ONNX.
-
-Please see [documentation](https://detectron2.readthedocs.io/tutorials/deployment.html) for its usage.
-
-
-### Acknowledgements
-
-Thanks to Mobile Vision team at Facebook for developing the Caffe2 conversion tools.
-
-Thanks to Computing Platform Department - PAI team at Alibaba Group (@bddpqq, @chenbohua3) who
-help export Detectron2 models to TorchScript.
diff --git a/spaces/BAAI/vid2vid-zero/vid2vid_zero/p2p/p2p_stable.py b/spaces/BAAI/vid2vid-zero/vid2vid_zero/p2p/p2p_stable.py
deleted file mode 100644
index 2dfc5dbdaed584d7d8e8ab0d48522c58d1976e65..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/vid2vid_zero/p2p/p2p_stable.py
+++ /dev/null
@@ -1,242 +0,0 @@
-# Copyright 2022 Google LLC
-#
-# 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 typing import Optional, Union, Tuple, List, Callable, Dict
-import torch
-import torch.nn.functional as nnf
-import numpy as np
-import abc
-from . import ptp_utils
-from . import seq_aligner
-from transformers import CLIPTextModel, CLIPTokenizer
-
-pretrained_model_path = "checkpoints/CompVis/stable-diffusion-v1-4/"
-ldm_stable = None
-tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
-
-LOW_RESOURCE = False 
-NUM_DIFFUSION_STEPS = 50
-GUIDANCE_SCALE = 7.5
-MAX_NUM_WORDS = 77
-device = torch.device('cuda')
-
-
-class LocalBlend:
-
-    def __call__(self, x_t, attention_store):
-        k = 1
-        maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
-        maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
-        maps = torch.cat(maps, dim=1)
-        maps = (maps * self.alpha_layers).sum(-1).mean(1)
-        mask = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 +1), (1, 1), padding=(k, k))
-        mask = nnf.interpolate(mask, size=(x_t.shape[2:]))
-        mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
-        mask = mask.gt(self.threshold)
-        mask = (mask[:1] + mask[1:]).float()
-        x_t = x_t[:1] + mask * (x_t - x_t[:1])
-        return x_t
-       
-    # def __init__(self, prompts: List[str], words: [List[List[str]]], threshold=.3):
-    def __init__(self, prompts: List[str], words: List[List[str]], threshold=.3):
-        alpha_layers = torch.zeros(len(prompts),  1, 1, 1, 1, MAX_NUM_WORDS)
-        for i, (prompt, words_) in enumerate(zip(prompts, words)):
-            if type(words_) is str:
-                words_ = [words_]
-            for word in words_:
-                ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
-                alpha_layers[i, :, :, :, :, ind] = 1
-        self.alpha_layers = alpha_layers.to(device)
-        self.threshold = threshold
-
-
-class AttentionControl(abc.ABC):
-    
-    def step_callback(self, x_t):
-        return x_t
-    
-    def between_steps(self):
-        return
-    
-    @property
-    def num_uncond_att_layers(self):
-        return self.num_att_layers if LOW_RESOURCE else 0
-    
-    @abc.abstractmethod
-    def forward (self, attn, is_cross: bool, place_in_unet: str):
-        raise NotImplementedError
-
-    def __call__(self, attn, is_cross: bool, place_in_unet: str):
-        if self.cur_att_layer >= self.num_uncond_att_layers:
-            if LOW_RESOURCE:
-                attn = self.forward(attn, is_cross, place_in_unet)
-            else:
-                h = attn.shape[0]
-                attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
-        self.cur_att_layer += 1
-        if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
-            self.cur_att_layer = 0
-            self.cur_step += 1
-            self.between_steps()
-        return attn
-    
-    def reset(self):
-        self.cur_step = 0
-        self.cur_att_layer = 0
-
-    def __init__(self):
-        self.cur_step = 0
-        self.num_att_layers = -1
-        self.cur_att_layer = 0
-
-class EmptyControl(AttentionControl):
-    
-    def forward (self, attn, is_cross: bool, place_in_unet: str):
-        return attn
-    
-    
-class AttentionStore(AttentionControl):
-
-    @staticmethod
-    def get_empty_store():
-        return {"down_cross": [], "mid_cross": [], "up_cross": [],
-                "down_self": [],  "mid_self": [],  "up_self": []}
-
-    def forward(self, attn, is_cross: bool, place_in_unet: str):
-        key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
-        if attn.shape[1] <= 32 ** 2:  # avoid memory overhead
-            self.step_store[key].append(attn)
-        return attn
-
-    def between_steps(self):
-        if len(self.attention_store) == 0:
-            self.attention_store = self.step_store
-        else:
-            for key in self.attention_store:
-                for i in range(len(self.attention_store[key])):
-                    self.attention_store[key][i] += self.step_store[key][i]
-        self.step_store = self.get_empty_store()
-
-    def get_average_attention(self):
-        average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
-        return average_attention
-
-
-    def reset(self):
-        super(AttentionStore, self).reset()
-        self.step_store = self.get_empty_store()
-        self.attention_store = {}
-
-    def __init__(self):
-        super(AttentionStore, self).__init__()
-        self.step_store = self.get_empty_store()
-        self.attention_store = {}
-
-        
-class AttentionControlEdit(AttentionStore, abc.ABC):
-    
-    def step_callback(self, x_t):
-        if self.local_blend is not None:
-            x_t = self.local_blend(x_t, self.attention_store)
-        return x_t
-        
-    def replace_self_attention(self, attn_base, att_replace):
-        if att_replace.shape[2] <= 16 ** 2:
-            return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
-        else:
-            return att_replace
-    
-    @abc.abstractmethod
-    def replace_cross_attention(self, attn_base, att_replace):
-        raise NotImplementedError
-    
-    def forward(self, attn, is_cross: bool, place_in_unet: str):
-        super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
-        if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
-            h = attn.shape[0] // (self.batch_size)
-            attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
-            attn_base, attn_repalce = attn[0], attn[1:]
-            if is_cross:
-                alpha_words = self.cross_replace_alpha[self.cur_step]
-                attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
-                attn[1:] = attn_repalce_new
-            else:
-                attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
-            attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
-        return attn
-    
-    def __init__(self, prompts, num_steps: int,
-                 cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
-                 self_replace_steps: Union[float, Tuple[float, float]],
-                 local_blend: Optional[LocalBlend]):
-        super(AttentionControlEdit, self).__init__()
-        self.batch_size = len(prompts)
-        self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
-        if type(self_replace_steps) is float:
-            self_replace_steps = 0, self_replace_steps
-        self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
-        self.local_blend = local_blend
-
-
-class AttentionReplace(AttentionControlEdit):
-
-    def replace_cross_attention(self, attn_base, att_replace):
-        return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
-      
-    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
-                 local_blend: Optional[LocalBlend] = None):
-        super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
-        self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
-
-
-class AttentionRefine(AttentionControlEdit):
-
-    def replace_cross_attention(self, attn_base, att_replace):
-        attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
-        attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
-        return attn_replace
-
-    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
-                 local_blend: Optional[LocalBlend] = None):
-        super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
-        self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
-        self.mapper, alphas = self.mapper.to(device), alphas.to(device)
-        self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
-
-
-class AttentionReweight(AttentionControlEdit):
-
-    def replace_cross_attention(self, attn_base, att_replace):
-        if self.prev_controller is not None:
-            attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
-        attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
-        return attn_replace
-
-    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,
-                local_blend: Optional[LocalBlend] = None, controller: Optional[AttentionControlEdit] = None):
-        super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
-        self.equalizer = equalizer.to(device)
-        self.prev_controller = controller
-
-
-def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],
-                  Tuple[float, ...]]):
-    if type(word_select) is int or type(word_select) is str:
-        word_select = (word_select,)
-    equalizer = torch.ones(len(values), 77)
-    values = torch.tensor(values, dtype=torch.float32)
-    for word in word_select:
-        inds = ptp_utils.get_word_inds(text, word, tokenizer)
-        equalizer[:, inds] = values
-    return equalizer
diff --git a/spaces/Benson/text-generation/Examples/Ciudad Congelada Apk Mod Descargar.md b/spaces/Benson/text-generation/Examples/Ciudad Congelada Apk Mod Descargar.md
deleted file mode 100644
index 993a4e4daafdc4e94dca231a43f5fbd78da4b6c9..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Ciudad Congelada Apk Mod Descargar.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-
-<h1>Ciudad congelada APK Mod Descargar: Una guía para sobrevivir a la edad de hielo</h1>
-<p>¿Te encantan los juegos de simulación de construcción de ciudades? ¿Te gusta desafiarte a ti mismo con escenarios de supervivencia? ¿Quieres experimentar un mundo helado donde tienes que gestionar tus recursos, explorar la naturaleza y guiar a tus supervivientes? Si respondiste afirmativamente a cualquiera de estas preguntas, entonces es posible que quieras echar un vistazo a <strong>Frozen City</strong>, un juego móvil inspirado en el éxito de PC y consola <strong>Frostpunk</strong>. </p>
-<h2>ciudad congelada apk mod descargar</h2><br /><p><b><b>Download Zip</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://bltlly.com/2v6Ji8">https://bltlly.com/2v6Ji8</a></b></p><br /><br />
-<p>En este artículo, le diremos todo lo que necesita saber sobre Frozen City, incluyendo qué es, cómo descargar e instalar su versión modificada, cómo jugarla y algunos consejos y trucos para ayudarlo a tener éxito. ¡Vamos a empezar! </p>
- <h2>¿Qué es la ciudad congelada? </h2>
-<h3>Un juego de simulación de construcción de ciudades en un apocalipsis de hielo y nieve</h3>
-<p>Frozen City es un juego que te reta a construir un lugar cálido para tus supervivientes en un páramo congelado post-apocalíptico. Tienes que reunir recursos, asignar trabajadores, explorar la naturaleza, mantener las necesidades básicas de tus sobrevivientes y el equilibrio entre la producción y el suministro. También tienes que tomar decisiones difíciles y sacrificios por el bien de tu nueva sociedad. </p>
-<h3>Inspirado por el éxito de PC y consola Frostpunk</h3>
-<p>Frozen City está fuertemente influenciado por Frostpunk, un juego que fue lanzado en 2018 para PC y más tarde para consolas. Frostpunk es un juego de estrategia oscuro y arenoso que te pone a cargo de la última ciudad en la Tierra, que está rodeada por un generador masivo que proporciona calor y energía. Tienes que manejar a tu gente, leyes, tecnología y recursos para sobrevivir el duro invierno. </p>
-<p>Aunque Frozen City no es tan sombrío o complejo como Frostpunk, todavía captura parte de su esencia y atmósfera. Te enfrentarás a desafíos y dilemas similares mientras intentas mantener tu ciudad viva y próspera. </p>
-<h3>Características de Frozen City</h3>
-
-<ul>
-<li>Reúne recursos y asigna sobrevivientes para trabajar en varias instalaciones</li>
-<li>Mantener las necesidades básicas de los sobrevivientes como alimentos y refugio</li>
-<li>Adéntrate en la naturaleza y descubre recursos e información sin explotar</li>
-<li>Procesar materias primas en objetos vivos</li>
-<li>Expande tu ciudad para atraer más sobrevivientes</li>
-<li>Juego duro con elementos desafiantes</li>
-<li>Delegación de tareas en profundidad</li>
-<li>Sistema de necesidades inmersivas</li>
-<li>Establecimiento de cadenas de suministro y líneas de producción</li>
-<li>Un vasto mundo para explorar</li>
-</ul>
- <h2>Cómo descargar e instalar Frozen City APK mod? </h2>
-<h3>Los beneficios de usar una versión modificada de Frozen City</h3>
-<p>Si desea mejorar su experiencia de juego con Frozen City, es posible que desee considerar la descarga e instalación de su versión modificada. Una versión modificada es una versión modificada del juego original que ofrece algunas características adicionales o ventajas que no están disponibles en la versión oficial. </p>
-<p></p>
-<p>Algunos de los beneficios de usar una versión modificada de Frozen City son:</p>
-<ul>
-<li>Puedes obtener dinero o monedas ilimitadas que puedes usar para mejorar tus edificios, comprar artículos o reclutar héroes. </li>
-<li> Puede acceder a un mod de menú que le permite personalizar la configuración de juego, como la velocidad, daños, salud, etc.</li>
-<li> Puede disfrutar de tiempos de carga más rápidos y un rendimiento más suave. </li>
-<li>Puede evitar cualquier restricción o limitación que puedan imponer los desarrolladores o editores de juegos. </li>
-</ul>
- <h3>Los pasos para descargar e instalar Frozen City APK mod</h3>
-<p>Para descargar e instalar Frozen City APK mod en su dispositivo Android, debe seguir estos pasos:</p>
-<ol>
-<li>Descargar el archivo de mod de Frozen City APK de una fuente de confianza. Puede encontrar la última versión del mod en sitios como [APKPure]( 2 ) o [APKMODY]( 1 ). </li>
-
-<li>Abra el instalador de APKMODY y seleccione Instalar APKs.</li>
-<li>Navegue a la ubicación del archivo de mod descargado Frozen City APK y selecciónelo. </li>
-<li>Seleccione Instalar en la ventana de instalación. </li>
-<li>Espere a que la instalación termine y luego inicie el juego desde el cajón de la aplicación. </li>
-</ol>
-<p>Felicidades, que ha instalado con éxito Frozen City APK mod en su dispositivo Android. Ahora puedes disfrutar del juego con dinero ilimitado, mod de menú y otras características. </p>
- <h2>¿Cómo se juega Frozen City? </h2>
-<h3>Los fundamentos del juego de Frozen City</h3>
-<p>Frozen City es un juego que requiere que uses tus habilidades estratégicas de pensamiento y gestión para sobrevivir en un mundo helado. Estos son algunos de los aspectos básicos del juego que necesitas saber:</p>
-<ul>
-<li>Se inicia el juego con un pequeño pueblo alrededor de un generador que proporciona calor y energía. Es necesario mantener el generador funcionando mediante el suministro de carbón, que se puede recoger de pilas o minas cercanas. </li>
-<li>También es necesario construir tiendas de campaña, puestos médicos, talleres, cocinas y otras instalaciones para proporcionar refugio, alimentos, salud y tecnología para sus sobrevivientes. Puede asignar trabajadores a diferentes edificios tocando en ellos y seleccionando el icono de trabajador. </li>
-<li>Puede explorar el mapa enviando exploradores a varias ubicaciones. Puede encontrar recursos, sobrevivientes, eventos y secretos en el camino. También puedes encontrar enemigos, peligros y opciones que afectarán tu juego. </li>
-<li>Puedes investigar nuevas tecnologías construyendo un taller y seleccionando el icono de investigación. Puedes desbloquear nuevos edificios, mejoras, leyes y habilidades que te ayudarán a mejorar tu ciudad y sobrevivir más tiempo. </li>
-<li>Puede acceder al menú del juego tocando el icono del engranaje en la esquina superior derecha. Puede hacer una pausa, reanudar, guardar, cargar o salir del juego desde allí. También puedes ajustar la configuración del juego, como sonido, gráficos, idioma, etc.</li>
-</ul>
-
-<p>Frozen City no es un juego fácil. Pondrá a prueba tus habilidades de toma de decisiones y gestión de recursos mientras te enfrentas a diversos desafíos y crisis. Aquí hay algunos consejos y trucos que te ayudarán a progresar y completar etapas en el juego:</p>
-<ul>
-<li>Mantenga un ojo en sus recursos. Necesita equilibrar la producción y el consumo de carbón, madera, acero, alimentos y otros recursos. Puede ver los niveles de recursos en la esquina superior izquierda de la pantalla. También puede pulsarlos para ver más detalles. </li>
-<li>Mantén un ojo en tus sobrevivientes. Necesitas cuidar de sus necesidades, tales como calor, hambre, salud y esperanza. Puede ver su estado en la esquina superior derecha de la pantalla. También puede pulsar sobre ellos para ver más detalles. </li>
-<li>Mantenga un ojo en su generador. Es necesario ajustar su nivel de potencia y el rango de acuerdo con las condiciones climáticas y el tamaño de su ciudad. Puede ver el estado del generador en la esquina inferior izquierda de la pantalla. También puede pulsar sobre él para ver más detalles. </li>
-<li>Mantenga un ojo en sus leyes. Es necesario promulgar leyes que darán forma a su sociedad y afectan a su juego. Puede ver sus leyes en la esquina inferior derecha de la pantalla. También puede pulsarlas para ver más detalles. </li>
-<li>Planifica con anticipación. Necesitas anticipar eventos futuros y prepararte en consecuencia. Por ejemplo, necesita abastecerse de recursos antes de que llegue una tormenta, o necesita investigar nuevas tecnologías antes de quedarse sin opciones. </li>
-<li>Sé flexible. Necesitas adaptarte a situaciones cambiantes y hacer concesiones entre diferentes objetivos. Por ejemplo, es posible que necesites sacrificar algo de comodidad por eficiencia, o algo de moralidad por supervivencia. </li>
-</ul>
- <h3>Cómo usar BlueStacks para disfrutar de una experiencia de juego superior</h3>
-
-<ul>
-<li> Puedes jugar Frozen City en una pantalla más grande con visibilidad y gráficos mejorados. </li>
-<li> Puede usar los controles del teclado y el ratón para <li> Puede usar los controles del teclado y el ratón para una jugabilidad más precisa y conveniente. También puede personalizar la asignación de claves y la configuración de sensibilidad según sus preferencias. </li>
-<li>Puede utilizar la función Multi-Instance para ejecutar varias instancias de Frozen City en su PC. Puedes jugar diferentes etapas, probar diferentes estrategias o incluso jugar con tus amigos. </li>
-<li>Puede usar la función Macro Recorder para automatizar tareas o acciones repetitivas en Frozen City. Puede grabar sus pulsaciones de teclas y clics del ratón y reproducirlos con un solo botón. </li>
-<li>Puedes usar la función Screen Recorder para capturar tus momentos de juego y compartirlos con otros. También puede editar, recortar o añadir efectos a sus vídeos. </li>
-</ul>
-<p>Para usar BlueStacks para jugar Frozen City en tu PC, debes seguir estos pasos:</p>
-<ol>
-<li>Descargar e instalar BlueStacks desde [aquí]. </li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google. </li>
-<li>Buscar Frozen City en la barra de búsqueda e instalarlo desde Google Play o APKPure.</li>
-<li>Lanza Frozen City desde la pantalla de inicio y disfruta del juego. </li>
-</ol>
-<p>¡Eso es todo! Has instalado y jugado correctamente Frozen City en tu PC usando BlueStacks. Ahora puedes disfrutar de una experiencia de juego superior con Frozen City.</p>
- <h2>Conclusión</h2>
-<p>Frozen City es un juego de simulación de construcción de ciudades que te pone a cargo de una ciudad en un apocalipsis congelado. Tienes que reunir recursos, construir instalaciones, explorar la naturaleza y tomar decisiones que afectarán tu supervivencia y la sociedad. También puede descargar e instalar una versión modificada de Frozen City que ofrece dinero ilimitado, mod de menú y otras características. También puedes usar BlueStacks para jugar a Frozen City en tu PC con gráficos, controles y rendimiento mejorados. </p>
-
- <h2>Preguntas frecuentes</h2>
-<h4>Q: ¿Es Frozen City libre para jugar? </h4>
-<p>A: Sí, Frozen City es gratis. Sin embargo, puede contener anuncios o compras dentro de la aplicación que requieren dinero real. </p>
- <h4>Q: ¿Está Frozen City disponible para dispositivos iOS? </h4>
-<p>A: No, Frozen City actualmente solo está disponible para dispositivos Android. No hay información oficial sobre si será lanzado para dispositivos iOS en el futuro. </p>
- <h4>Q: ¿Está Frozen City fuera de línea o en línea? </h4>
-<p>A: Frozen City es principalmente un juego fuera de línea. Sin embargo, es posible que necesite una conexión a Internet para descargar o actualizar el juego, o para acceder a algunas características o eventos. </p>
- <h4>Q: ¿Cuántas etapas hay en Frozen City? </h4>
-<p>A: Actualmente hay 10 etapas en Frozen City, cada una con diferentes escenarios, objetivos y dificultades. Puedes desbloquear nuevas etapas completando las anteriores. </p>
- <h4>Q: ¿Cómo puedo contactar a los desarrolladores de Frozen City? </h4>
-<p>A: Puede ponerse en contacto con los desarrolladores de Frozen City enviando un correo electrónico a [frozencity@163.com] o visitando su página de Facebook [aquí]. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/upload.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/upload.py
deleted file mode 100644
index 6af5394339437497134fa74a97463e3e5309ff29..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/upload.py
+++ /dev/null
@@ -1,205 +0,0 @@
-"""
-distutils.command.upload
-
-Implements the Distutils 'upload' subcommand (upload package to a package
-index).
-"""
-
-import os
-import io
-import hashlib
-from base64 import standard_b64encode
-from urllib.request import urlopen, Request, HTTPError
-from urllib.parse import urlparse
-from distutils.errors import DistutilsError, DistutilsOptionError
-from distutils.core import PyPIRCCommand
-from distutils.spawn import spawn
-from distutils import log
-
-
-# PyPI Warehouse supports MD5, SHA256, and Blake2 (blake2-256)
-# https://bugs.python.org/issue40698
-_FILE_CONTENT_DIGESTS = {
-    "md5_digest": getattr(hashlib, "md5", None),
-    "sha256_digest": getattr(hashlib, "sha256", None),
-    "blake2_256_digest": getattr(hashlib, "blake2b", None),
-}
-
-
-class upload(PyPIRCCommand):
-
-    description = "upload binary package to PyPI"
-
-    user_options = PyPIRCCommand.user_options + [
-        ('sign', 's', 'sign files to upload using gpg'),
-        ('identity=', 'i', 'GPG identity used to sign files'),
-    ]
-
-    boolean_options = PyPIRCCommand.boolean_options + ['sign']
-
-    def initialize_options(self):
-        PyPIRCCommand.initialize_options(self)
-        self.username = ''
-        self.password = ''
-        self.show_response = 0
-        self.sign = False
-        self.identity = None
-
-    def finalize_options(self):
-        PyPIRCCommand.finalize_options(self)
-        if self.identity and not self.sign:
-            raise DistutilsOptionError("Must use --sign for --identity to have meaning")
-        config = self._read_pypirc()
-        if config != {}:
-            self.username = config['username']
-            self.password = config['password']
-            self.repository = config['repository']
-            self.realm = config['realm']
-
-        # getting the password from the distribution
-        # if previously set by the register command
-        if not self.password and self.distribution.password:
-            self.password = self.distribution.password
-
-    def run(self):
-        if not self.distribution.dist_files:
-            msg = (
-                "Must create and upload files in one command "
-                "(e.g. setup.py sdist upload)"
-            )
-            raise DistutilsOptionError(msg)
-        for command, pyversion, filename in self.distribution.dist_files:
-            self.upload_file(command, pyversion, filename)
-
-    def upload_file(self, command, pyversion, filename):  # noqa: C901
-        # Makes sure the repository URL is compliant
-        schema, netloc, url, params, query, fragments = urlparse(self.repository)
-        if params or query or fragments:
-            raise AssertionError("Incompatible url %s" % self.repository)
-
-        if schema not in ('http', 'https'):
-            raise AssertionError("unsupported schema " + schema)
-
-        # Sign if requested
-        if self.sign:
-            gpg_args = ["gpg", "--detach-sign", "-a", filename]
-            if self.identity:
-                gpg_args[2:2] = ["--local-user", self.identity]
-            spawn(gpg_args, dry_run=self.dry_run)
-
-        # Fill in the data - send all the meta-data in case we need to
-        # register a new release
-        f = open(filename, 'rb')
-        try:
-            content = f.read()
-        finally:
-            f.close()
-
-        meta = self.distribution.metadata
-        data = {
-            # action
-            ':action': 'file_upload',
-            'protocol_version': '1',
-            # identify release
-            'name': meta.get_name(),
-            'version': meta.get_version(),
-            # file content
-            'content': (os.path.basename(filename), content),
-            'filetype': command,
-            'pyversion': pyversion,
-            # additional meta-data
-            'metadata_version': '1.0',
-            'summary': meta.get_description(),
-            'home_page': meta.get_url(),
-            'author': meta.get_contact(),
-            'author_email': meta.get_contact_email(),
-            'license': meta.get_licence(),
-            'description': meta.get_long_description(),
-            'keywords': meta.get_keywords(),
-            'platform': meta.get_platforms(),
-            'classifiers': meta.get_classifiers(),
-            'download_url': meta.get_download_url(),
-            # PEP 314
-            'provides': meta.get_provides(),
-            'requires': meta.get_requires(),
-            'obsoletes': meta.get_obsoletes(),
-        }
-
-        data['comment'] = ''
-
-        # file content digests
-        for digest_name, digest_cons in _FILE_CONTENT_DIGESTS.items():
-            if digest_cons is None:
-                continue
-            try:
-                data[digest_name] = digest_cons(content).hexdigest()
-            except ValueError:
-                # hash digest not available or blocked by security policy
-                pass
-
-        if self.sign:
-            with open(filename + ".asc", "rb") as f:
-                data['gpg_signature'] = (os.path.basename(filename) + ".asc", f.read())
-
-        # set up the authentication
-        user_pass = (self.username + ":" + self.password).encode('ascii')
-        # The exact encoding of the authentication string is debated.
-        # Anyway PyPI only accepts ascii for both username or password.
-        auth = "Basic " + standard_b64encode(user_pass).decode('ascii')
-
-        # Build up the MIME payload for the POST data
-        boundary = '--------------GHSKFJDLGDS7543FJKLFHRE75642756743254'
-        sep_boundary = b'\r\n--' + boundary.encode('ascii')
-        end_boundary = sep_boundary + b'--\r\n'
-        body = io.BytesIO()
-        for key, value in data.items():
-            title = '\r\nContent-Disposition: form-data; name="%s"' % key
-            # handle multiple entries for the same name
-            if not isinstance(value, list):
-                value = [value]
-            for value in value:
-                if type(value) is tuple:
-                    title += '; filename="%s"' % value[0]
-                    value = value[1]
-                else:
-                    value = str(value).encode('utf-8')
-                body.write(sep_boundary)
-                body.write(title.encode('utf-8'))
-                body.write(b"\r\n\r\n")
-                body.write(value)
-        body.write(end_boundary)
-        body = body.getvalue()
-
-        msg = "Submitting {} to {}".format(filename, self.repository)
-        self.announce(msg, log.INFO)
-
-        # build the Request
-        headers = {
-            'Content-type': 'multipart/form-data; boundary=%s' % boundary,
-            'Content-length': str(len(body)),
-            'Authorization': auth,
-        }
-
-        request = Request(self.repository, data=body, headers=headers)
-        # send the data
-        try:
-            result = urlopen(request)
-            status = result.getcode()
-            reason = result.msg
-        except HTTPError as e:
-            status = e.code
-            reason = e.msg
-        except OSError as e:
-            self.announce(str(e), log.ERROR)
-            raise
-
-        if status == 200:
-            self.announce('Server response ({}): {}'.format(status, reason), log.INFO)
-            if self.show_response:
-                text = self._read_pypi_response(result)
-                msg = '\n'.join(('-' * 75, text, '-' * 75))
-                self.announce(msg, log.INFO)
-        else:
-            msg = 'Upload failed ({}): {}'.format(status, reason)
-            self.announce(msg, log.ERROR)
-            raise DistutilsError(msg)
diff --git a/spaces/BigSalmon/InformalToFormal/README.md b/spaces/BigSalmon/InformalToFormal/README.md
deleted file mode 100644
index fb28e8513c0e0ff28c154d63853e3f4f03cc4881..0000000000000000000000000000000000000000
--- a/spaces/BigSalmon/InformalToFormal/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: LezGo
-emoji: 🔥
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/Bishan/Speech_To_Text_Hindi/README.md b/spaces/Bishan/Speech_To_Text_Hindi/README.md
deleted file mode 100644
index 3a00a07e55d5f25caed8c34e39f93854e483fad8..0000000000000000000000000000000000000000
--- a/spaces/Bishan/Speech_To_Text_Hindi/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Speech To Text Hindi
-emoji: 🏢
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/ops/fc.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/ops/fc.py
deleted file mode 100644
index c9ff34e6765a63bd3e453be4cb4c3aec6bef5dd7..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/ops/fc.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# --------------------------------------------------------
-
-import torch.nn as nn
-import torch
-
-
-class FC(nn.Module):
-    def __init__(self, in_size, out_size, dropout_r=0., use_relu=True):
-        super(FC, self).__init__()
-        self.dropout_r = dropout_r
-        self.use_relu = use_relu
-
-        self.linear = nn.Linear(in_size, out_size)
-
-        if use_relu:
-            self.relu = nn.ReLU(inplace=True)
-
-        if dropout_r > 0:
-            self.dropout = nn.Dropout(dropout_r)
-
-    def forward(self, x):
-        x = self.linear(x)
-
-        if self.use_relu:
-            x = self.relu(x)
-
-        if self.dropout_r > 0:
-            x = self.dropout(x)
-
-        return x
-
-
-class MLP(nn.Module):
-    def __init__(self, in_size, mid_size, out_size, dropout_r=0., use_relu=True):
-        super(MLP, self).__init__()
-
-        self.fc = FC(in_size, mid_size, dropout_r=dropout_r, use_relu=use_relu)
-        self.linear = nn.Linear(mid_size, out_size)
-
-    def forward(self, x):
-        return self.linear(self.fc(x))
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/scan_by_key.h b/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/scan_by_key.h
deleted file mode 100644
index 1e72e7c38f868bb83bc835c3d627751c54e2cd15..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/scan_by_key.h
+++ /dev/null
@@ -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 the scan_by_key algorithms
-#include <thrust/system/detail/sequential/scan_by_key.h>
-
diff --git a/spaces/CVPR/MonoScene/monoscene/.ipynb_checkpoints/CRP3D-checkpoint.py b/spaces/CVPR/MonoScene/monoscene/.ipynb_checkpoints/CRP3D-checkpoint.py
deleted file mode 100644
index c88b7b309e6fe66f597cafe2a5eb8c6d29343b7e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/MonoScene/monoscene/.ipynb_checkpoints/CRP3D-checkpoint.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import torch
-import torch.nn as nn
-from monoscene.modules import (
-    Process,
-    ASPP,
-)
-
-
-class CPMegaVoxels(nn.Module):
-    def __init__(self, feature, size, n_relations=4, bn_momentum=0.0003):
-        super().__init__()
-        self.size = size
-        self.n_relations = n_relations
-        print("n_relations", self.n_relations)
-        self.flatten_size = size[0] * size[1] * size[2]
-        self.feature = feature
-        self.context_feature = feature * 2
-        self.flatten_context_size = (size[0] // 2) * (size[1] // 2) * (size[2] // 2)
-        padding = ((size[0] + 1) % 2, (size[1] + 1) % 2, (size[2] + 1) % 2)
-        
-        self.mega_context = nn.Sequential(
-            nn.Conv3d(
-                feature, self.context_feature, stride=2, padding=padding, kernel_size=3
-            ),
-        )
-        self.flatten_context_size = (size[0] // 2) * (size[1] // 2) * (size[2] // 2)
-
-        self.context_prior_logits = nn.ModuleList(
-            [
-                nn.Sequential(
-                    nn.Conv3d(
-                        self.feature,
-                        self.flatten_context_size,
-                        padding=0,
-                        kernel_size=1,
-                    ),
-                )
-                for i in range(n_relations)
-            ]
-        )
-        self.aspp = ASPP(feature, [1, 2, 3])
-
-        self.resize = nn.Sequential(
-            nn.Conv3d(
-                self.context_feature * self.n_relations + feature,
-                feature,
-                kernel_size=1,
-                padding=0,
-                bias=False,
-            ),
-            Process(feature, nn.BatchNorm3d, bn_momentum, dilations=[1]),
-        )
-
-    def forward(self, input):
-        ret = {}
-        bs = input.shape[0]
-
-        x_agg = self.aspp(input)
-
-        # get the mega context
-        x_mega_context_raw = self.mega_context(x_agg)
-        x_mega_context = x_mega_context_raw.reshape(bs, self.context_feature, -1)
-        x_mega_context = x_mega_context.permute(0, 2, 1)
-
-        # get context prior map
-        x_context_prior_logits = []
-        x_context_rels = []
-        for rel in range(self.n_relations):
-
-            # Compute the relation matrices
-            x_context_prior_logit = self.context_prior_logits[rel](x_agg)
-            x_context_prior_logit = x_context_prior_logit.reshape(
-                bs, self.flatten_context_size, self.flatten_size
-            )
-            x_context_prior_logits.append(x_context_prior_logit.unsqueeze(1))
-
-            x_context_prior_logit = x_context_prior_logit.permute(0, 2, 1)
-            x_context_prior = torch.sigmoid(x_context_prior_logit)
-
-            # Multiply the relation matrices with the mega context to gather context features
-            x_context_rel = torch.bmm(x_context_prior, x_mega_context)  # bs, N, f
-            x_context_rels.append(x_context_rel)
-
-        x_context = torch.cat(x_context_rels, dim=2)
-        x_context = x_context.permute(0, 2, 1)
-        x_context = x_context.reshape(
-            bs, x_context.shape[1], self.size[0], self.size[1], self.size[2]
-        )
-
-        x = torch.cat([input, x_context], dim=1)
-        x = self.resize(x)
-
-        x_context_prior_logits = torch.cat(x_context_prior_logits, dim=1)
-        ret["P_logits"] = x_context_prior_logits
-        ret["x"] = x
-
-        return ret
diff --git a/spaces/CVPR/WALT/mmdet/models/detectors/base.py b/spaces/CVPR/WALT/mmdet/models/detectors/base.py
deleted file mode 100644
index 89134f3696ead442a5ff57184e9d256fdf7d0ba4..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/detectors/base.py
+++ /dev/null
@@ -1,355 +0,0 @@
-from abc import ABCMeta, abstractmethod
-from collections import OrderedDict
-
-import mmcv
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-from mmcv.runner import auto_fp16
-from mmcv.utils import print_log
-
-from mmdet.core.visualization import imshow_det_bboxes
-from mmdet.utils import get_root_logger
-
-
-class BaseDetector(nn.Module, metaclass=ABCMeta):
-    """Base class for detectors."""
-
-    def __init__(self):
-        super(BaseDetector, self).__init__()
-        self.fp16_enabled = False
-
-    @property
-    def with_neck(self):
-        """bool: whether the detector has a neck"""
-        return hasattr(self, 'neck') and self.neck is not None
-
-    # TODO: these properties need to be carefully handled
-    # for both single stage & two stage detectors
-    @property
-    def with_shared_head(self):
-        """bool: whether the detector has a shared head in the RoI Head"""
-        return hasattr(self, 'roi_head') and self.roi_head.with_shared_head
-
-    @property
-    def with_bbox(self):
-        """bool: whether the detector has a bbox head"""
-        return ((hasattr(self, 'roi_head') and self.roi_head.with_bbox)
-                or (hasattr(self, 'bbox_head') and self.bbox_head is not None))
-
-    @property
-    def with_mask(self):
-        """bool: whether the detector has a mask head"""
-        return ((hasattr(self, 'roi_head') and self.roi_head.with_mask)
-                or (hasattr(self, 'mask_head') and self.mask_head is not None))
-
-    @abstractmethod
-    def extract_feat(self, imgs):
-        """Extract features from images."""
-        pass
-
-    def extract_feats(self, imgs):
-        """Extract features from multiple images.
-
-        Args:
-            imgs (list[torch.Tensor]): A list of images. The images are
-                augmented from the same image but in different ways.
-
-        Returns:
-            list[torch.Tensor]: Features of different images
-        """
-        assert isinstance(imgs, list)
-        return [self.extract_feat(img) for img in imgs]
-
-    def forward_train(self, imgs, img_metas, **kwargs):
-        """
-        Args:
-            img (list[Tensor]): List of tensors of shape (1, C, H, W).
-                Typically these should be mean centered and std scaled.
-            img_metas (list[dict]): List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys, see
-                :class:`mmdet.datasets.pipelines.Collect`.
-            kwargs (keyword arguments): Specific to concrete implementation.
-        """
-        # NOTE the batched image size information may be useful, e.g.
-        # in DETR, this is needed for the construction of masks, which is
-        # then used for the transformer_head.
-        batch_input_shape = tuple(imgs[0].size()[-2:])
-        for img_meta in img_metas:
-            img_meta['batch_input_shape'] = batch_input_shape
-
-    async def async_simple_test(self, img, img_metas, **kwargs):
-        raise NotImplementedError
-
-    @abstractmethod
-    def simple_test(self, img, img_metas, **kwargs):
-        pass
-
-    @abstractmethod
-    def aug_test(self, imgs, img_metas, **kwargs):
-        """Test function with test time augmentation."""
-        pass
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in detector.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-        if pretrained is not None:
-            logger = get_root_logger()
-            print_log(f'load model from: {pretrained}', logger=logger)
-
-    async def aforward_test(self, *, img, img_metas, **kwargs):
-        for var, name in [(img, 'img'), (img_metas, 'img_metas')]:
-            if not isinstance(var, list):
-                raise TypeError(f'{name} must be a list, but got {type(var)}')
-
-        num_augs = len(img)
-        if num_augs != len(img_metas):
-            raise ValueError(f'num of augmentations ({len(img)}) '
-                             f'!= num of image metas ({len(img_metas)})')
-        # TODO: remove the restriction of samples_per_gpu == 1 when prepared
-        samples_per_gpu = img[0].size(0)
-        assert samples_per_gpu == 1
-
-        if num_augs == 1:
-            return await self.async_simple_test(img[0], img_metas[0], **kwargs)
-        else:
-            raise NotImplementedError
-
-    def forward_test(self, imgs, img_metas, **kwargs):
-        """
-        Args:
-            imgs (List[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains all images in the batch.
-            img_metas (List[List[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch.
-        """
-        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
-            if not isinstance(var, list):
-                raise TypeError(f'{name} must be a list, but got {type(var)}')
-
-        num_augs = len(imgs)
-        if num_augs != len(img_metas):
-            raise ValueError(f'num of augmentations ({len(imgs)}) '
-                             f'!= num of image meta ({len(img_metas)})')
-
-        # NOTE the batched image size information may be useful, e.g.
-        # in DETR, this is needed for the construction of masks, which is
-        # then used for the transformer_head.
-        for img, img_meta in zip(imgs, img_metas):
-            batch_size = len(img_meta)
-            for img_id in range(batch_size):
-                img_meta[img_id]['batch_input_shape'] = tuple(img.size()[-2:])
-
-        if num_augs == 1:
-            # proposals (List[List[Tensor]]): the outer list indicates
-            # test-time augs (multiscale, flip, etc.) and the inner list
-            # indicates images in a batch.
-            # The Tensor should have a shape Px4, where P is the number of
-            # proposals.
-            if 'proposals' in kwargs:
-                kwargs['proposals'] = kwargs['proposals'][0]
-            return self.simple_test(imgs[0], img_metas[0], **kwargs)
-        else:
-            assert imgs[0].size(0) == 1, 'aug test does not support ' \
-                                         'inference with batch size ' \
-                                         f'{imgs[0].size(0)}'
-            # TODO: support test augmentation for predefined proposals
-            assert 'proposals' not in kwargs
-            return self.aug_test(imgs, img_metas, **kwargs)
-
-    @auto_fp16(apply_to=('img', ))
-    def forward(self, img, img_metas, return_loss=True, **kwargs):
-        """Calls either :func:`forward_train` or :func:`forward_test` depending
-        on whether ``return_loss`` is ``True``.
-
-        Note this setting will change the expected inputs. When
-        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
-        and List[dict]), and when ``resturn_loss=False``, img and img_meta
-        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
-        the outer list indicating test time augmentations.
-        """
-        if return_loss:
-            return self.forward_train(img, img_metas, **kwargs)
-        else:
-            return self.forward_test(img, img_metas, **kwargs)
-
-    def _parse_losses(self, losses):
-        """Parse the raw outputs (losses) of the network.
-
-        Args:
-            losses (dict): Raw output of the network, which usually contain
-                losses and other necessary infomation.
-
-        Returns:
-            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \
-                which may be a weighted sum of all losses, log_vars contains \
-                all the variables to be sent to the logger.
-        """
-        log_vars = OrderedDict()
-        for loss_name, loss_value in losses.items():
-            if isinstance(loss_value, torch.Tensor):
-                log_vars[loss_name] = loss_value.mean()
-            elif isinstance(loss_value, list):
-                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
-            else:
-                raise TypeError(
-                    f'{loss_name} is not a tensor or list of tensors')
-
-        loss = sum(_value for _key, _value in log_vars.items()
-                   if 'loss' in _key)
-
-        log_vars['loss'] = loss
-        for loss_name, loss_value in log_vars.items():
-            # reduce loss when distributed training
-            if dist.is_available() and dist.is_initialized():
-                loss_value = loss_value.data.clone()
-                dist.all_reduce(loss_value.div_(dist.get_world_size()))
-            log_vars[loss_name] = loss_value.item()
-
-        return loss, log_vars
-
-    def train_step(self, data, optimizer):
-        """The iteration step during training.
-
-        This method defines an iteration step during training, except for the
-        back propagation and optimizer updating, which are done in an optimizer
-        hook. Note that in some complicated cases or models, the whole process
-        including back propagation and optimizer updating is also defined in
-        this method, such as GAN.
-
-        Args:
-            data (dict): The output of dataloader.
-            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
-                runner is passed to ``train_step()``. This argument is unused
-                and reserved.
-
-        Returns:
-            dict: It should contain at least 3 keys: ``loss``, ``log_vars``, \
-                ``num_samples``.
-
-                - ``loss`` is a tensor for back propagation, which can be a \
-                weighted sum of multiple losses.
-                - ``log_vars`` contains all the variables to be sent to the
-                logger.
-                - ``num_samples`` indicates the batch size (when the model is \
-                DDP, it means the batch size on each GPU), which is used for \
-                averaging the logs.
-        """
-        losses = self(**data)
-        loss, log_vars = self._parse_losses(losses)
-
-        outputs = dict(
-            loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
-
-        return outputs
-
-    def val_step(self, data, optimizer):
-        """The iteration step during validation.
-
-        This method shares the same signature as :func:`train_step`, but used
-        during val epochs. Note that the evaluation after training epochs is
-        not implemented with this method, but an evaluation hook.
-        """
-        losses = self(**data)
-        loss, log_vars = self._parse_losses(losses)
-
-        outputs = dict(
-            loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
-
-        return outputs
-
-    def show_result(self,
-                    img,
-                    result,
-                    score_thr=0.3,
-                    bbox_color=(72, 101, 241),
-                    text_color=(72, 101, 241),
-                    mask_color=None,
-                    thickness=2,
-                    font_size=13,
-                    win_name='',
-                    show=False,
-                    wait_time=0,
-                    out_file=None):
-        """Draw `result` over `img`.
-
-        Args:
-            img (str or Tensor): The image to be displayed.
-            result (Tensor or tuple): The results to draw over `img`
-                bbox_result or (bbox_result, segm_result).
-            score_thr (float, optional): Minimum score of bboxes to be shown.
-                Default: 0.3.
-            bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-               The tuple of color should be in BGR order. Default: 'green'
-            text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-               The tuple of color should be in BGR order. Default: 'green'
-            mask_color (None or str or tuple(int) or :obj:`Color`):
-               Color of masks. The tuple of color should be in BGR order.
-               Default: None
-            thickness (int): Thickness of lines. Default: 2
-            font_size (int): Font size of texts. Default: 13
-            win_name (str): The window name. Default: ''
-            wait_time (float): Value of waitKey param.
-                Default: 0.
-            show (bool): Whether to show the image.
-                Default: False.
-            out_file (str or None): The filename to write the image.
-                Default: None.
-
-        Returns:
-            img (Tensor): Only if not `show` or `out_file`
-        """
-        img = mmcv.imread(img)
-        img = img.copy()
-        if isinstance(result, tuple):
-            bbox_result, segm_result = result
-            if isinstance(segm_result, tuple):
-                segm_result = segm_result[0]  # ms rcnn
-        else:
-            bbox_result, segm_result = result, None
-        bboxes = np.vstack(bbox_result)
-        labels = [
-            np.full(bbox.shape[0], i, dtype=np.int32)
-            for i, bbox in enumerate(bbox_result)
-        ]
-        labels = np.concatenate(labels)
-        # draw segmentation masks
-        segms = None
-        if segm_result is not None and len(labels) > 0:  # non empty
-            segms = mmcv.concat_list(segm_result)
-            if isinstance(segms[0], torch.Tensor):
-                segms = torch.stack(segms, dim=0).detach().cpu().numpy()
-            else:
-                segms = np.stack(segms, axis=0)
-        # if out_file specified, do not show image in window
-        if out_file is not None:
-            show = False
-        # draw bounding boxes
-        img = imshow_det_bboxes(
-            img,
-            bboxes,
-            labels,
-            segms,
-            class_names=self.CLASSES,
-            score_thr=score_thr,
-            bbox_color=bbox_color,
-            text_color=text_color,
-            mask_color=mask_color,
-            thickness=thickness,
-            font_size=font_size,
-            win_name=win_name,
-            show=show,
-            wait_time=wait_time,
-            out_file=out_file)
-
-        if not (show or out_file):
-            return img
diff --git a/spaces/CVPR/lama-example/saicinpainting/training/losses/adversarial.py b/spaces/CVPR/lama-example/saicinpainting/training/losses/adversarial.py
deleted file mode 100644
index d6db2967ce5074d94ed3b4c51fc743ff2f7831b1..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/saicinpainting/training/losses/adversarial.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import Tuple, Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BaseAdversarialLoss:
-    def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for generator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for discriminator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate generator loss
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total generator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate discriminator loss and call .backward() on it
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total discriminator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def interpolate_mask(self, mask, shape):
-        assert mask is not None
-        assert self.allow_scale_mask or shape == mask.shape[-2:]
-        if shape != mask.shape[-2:] and self.allow_scale_mask:
-            if self.mask_scale_mode == 'maxpool':
-                mask = F.adaptive_max_pool2d(mask, shape)
-            else:
-                mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)
-        return mask
-
-def make_r1_gp(discr_real_pred, real_batch):
-    if torch.is_grad_enabled():
-        grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]
-        grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()
-    else:
-        grad_penalty = 0
-    real_batch.requires_grad = False
-
-    return grad_penalty
-
-class NonSaturatingWithR1(BaseAdversarialLoss):
-    def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,
-                 mask_scale_mode='nearest', extra_mask_weight_for_gen=0,
-                 use_unmasked_for_gen=True, use_unmasked_for_discr=True):
-        self.gp_coef = gp_coef
-        self.weight = weight
-        # use for discr => use for gen;
-        # otherwise we teach only the discr to pay attention to very small difference
-        assert use_unmasked_for_gen or (not use_unmasked_for_discr)
-        # mask as target => use unmasked for discr:
-        # if we don't care about unmasked regions at all
-        # then it doesn't matter if the value of mask_as_fake_target is true or false
-        assert use_unmasked_for_discr or (not mask_as_fake_target)
-        self.use_unmasked_for_gen = use_unmasked_for_gen
-        self.use_unmasked_for_discr = use_unmasked_for_discr
-        self.mask_as_fake_target = mask_as_fake_target
-        self.allow_scale_mask = allow_scale_mask
-        self.mask_scale_mode = mask_scale_mode
-        self.extra_mask_weight_for_gen = extra_mask_weight_for_gen
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        fake_loss = F.softplus(-discr_fake_pred)
-        if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \
-                not self.use_unmasked_for_gen:  # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            if not self.use_unmasked_for_gen:
-                fake_loss = fake_loss * mask
-            else:
-                pixel_weights = 1 + mask * self.extra_mask_weight_for_gen
-                fake_loss = fake_loss * pixel_weights
-
-        return fake_loss.mean() * self.weight, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_loss = F.softplus(-discr_real_pred)
-        grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef
-        fake_loss = F.softplus(discr_fake_pred)
-
-        if not self.use_unmasked_for_discr or self.mask_as_fake_target:
-            # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            # use_unmasked_for_discr=False only makes sense for fakes;
-            # for reals there is no difference beetween two regions
-            fake_loss = fake_loss * mask
-            if self.mask_as_fake_target:
-                fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)
-
-        sum_discr_loss = real_loss + grad_penalty + fake_loss
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=grad_penalty)
-        return sum_discr_loss.mean(), metrics
-
-class BCELoss(BaseAdversarialLoss):
-    def __init__(self, weight):
-        self.weight = weight
-        self.bce_loss = nn.BCEWithLogitsLoss()
-
-    def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)
-        fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight
-        return fake_loss, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self,
-                           mask: torch.Tensor,
-                           discr_real_pred: torch.Tensor,
-                           discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)
-        sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) +  self.bce_loss(discr_fake_pred, mask)) / 2
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=0)
-        return sum_discr_loss, metrics
-
-
-def make_discrim_loss(kind, **kwargs):
-    if kind == 'r1':
-        return NonSaturatingWithR1(**kwargs)
-    elif kind == 'bce':
-        return BCELoss(**kwargs)
-    raise ValueError(f'Unknown adversarial loss kind {kind}')
diff --git a/spaces/CVPR/transfiner/configs/common/data/coco.py b/spaces/CVPR/transfiner/configs/common/data/coco.py
deleted file mode 100644
index 703c4385c7ddc7eb0759c98d102ab2384d6a9e3e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/transfiner/configs/common/data/coco.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from omegaconf import OmegaConf
-
-import detectron2.data.transforms as T
-from detectron2.config import LazyCall as L
-from detectron2.data import (
-    DatasetMapper,
-    build_detection_test_loader,
-    build_detection_train_loader,
-    get_detection_dataset_dicts,
-)
-from detectron2.evaluation import COCOEvaluator
-
-dataloader = OmegaConf.create()
-
-dataloader.train = L(build_detection_train_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_train"),
-    mapper=L(DatasetMapper)(
-        is_train=True,
-        augmentations=[
-            L(T.ResizeShortestEdge)(
-                short_edge_length=(640, 672, 704, 736, 768, 800),
-                sample_style="choice",
-                max_size=1333,
-            ),
-            L(T.RandomFlip)(horizontal=True),
-        ],
-        image_format="BGR",
-        use_instance_mask=True,
-    ),
-    total_batch_size=16,
-    num_workers=4,
-)
-
-dataloader.test = L(build_detection_test_loader)(
-    dataset=L(get_detection_dataset_dicts)(names="coco_2017_val", filter_empty=False),
-    mapper=L(DatasetMapper)(
-        is_train=False,
-        augmentations=[
-            L(T.ResizeShortestEdge)(short_edge_length=800, max_size=1333),
-        ],
-        image_format="${...train.mapper.image_format}",
-    ),
-    num_workers=4,
-)
-
-dataloader.evaluator = L(COCOEvaluator)(
-    dataset_name="${..test.dataset.names}",
-)
diff --git a/spaces/CVPR/visual-clustering/app.py b/spaces/CVPR/visual-clustering/app.py
deleted file mode 100644
index 4c75eefaf491089262fe65d770d7f908532f0afb..0000000000000000000000000000000000000000
--- a/spaces/CVPR/visual-clustering/app.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import math
-import cv2
-from sklearn import datasets
-import numpy as np
-from matplotlib import pyplot as plt
-from scipy import ndimage
-from skimage import measure, color, io
-from tensorflow.keras.preprocessing import image
-from scipy import ndimage
-import skimage.io as io
-import skimage.transform as trans
-import numpy as np
-import tensorflow as tf
-import gradio as gr
-from huggingface_hub.keras_mixin import from_pretrained_keras
-from itertools import cycle, islice
-
-
-
-
-#Function that predicts on only 1 sample 
-def predict_sample(image):
-  prediction = model.predict(image[tf.newaxis, ...])
-  prediction[prediction > 0.5 ] = 1
-  prediction[prediction !=1] = 0
-  result = prediction[0]*255
-  return result
-
-
-
-
-def create_input_image(data, visualize=False):
-  #Initialize input matrix
-  input = np.ones((256,256))
-
-  #Fill matrix with data point values
-  for i in range(0,len(data)):
-    if math.floor(data[i][0]) < 256 and math.floor(data[i][1]) < 256:
-      input[math.floor(data[i][0])][math.floor(data[i][1])] = 0
-    elif math.floor(data[i][0]) >= 256:
-      input[255][math.floor(data[i][1])] = 0
-    elif math.floor(data[i][1]) >= 256:
-      input[math.floor(data[i][0])][255] = 0
-  
-  #Visualize
-  if visualize == True:
-    plt.imshow(input.T, cmap='gray')
-    plt.gca().invert_yaxis()
-
-  return input
-
-model = from_pretrained_keras("tareknaous/unet-visual-clustering")
-
-
-def get_instances(prediction, data, max_filter_size=1):
-  #Adjust format (clusters to be 255 and rest is 0)
-  prediction[prediction == 255] = 3
-  prediction[prediction == 0] = 4
-  prediction[prediction == 3] = 0
-  prediction[prediction == 4] = 255
-
-  #Convert to 8-bit image
-  prediction = image.img_to_array(prediction, dtype='uint8')
-  
-  #Get 1 color channel
-  cells=prediction[:,:,0]
-  #Threshold
-  ret1, thresh = cv2.threshold(cells, 0, 255, cv2.THRESH_BINARY)
-  #Filter to remove noise
-  kernel = np.ones((3,3),np.uint8)
-  opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
-
-  #Get the background
-  background = cv2.dilate(opening,kernel,iterations=5)
-  dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
-  ret2, foreground = cv2.threshold(dist_transform,0.04*dist_transform.max(),255,0)
-  foreground = np.uint8(foreground)
-  unknown = cv2.subtract(background,foreground)
-
-  #Connected Component Analysis
-  ret3, markers = cv2.connectedComponents(foreground)
-  markers = markers+10
-  markers[unknown==255] = 0
-
-  #Watershed
-  img = cv2.merge((prediction,prediction,prediction))
-  markers = cv2.watershed(img,markers)
-  img[markers == -1] = [0,255,255]  
-
-  #Maximum filtering
-  markers = ndimage.maximum_filter(markers, size=max_filter_size)
-  # plt.imshow(markers.T, cmap='gray')
-  # plt.gca().invert_yaxis()
-
-  #Get an RGB colored image
-  img2 = color.label2rgb(markers, bg_label=1)
-  # plt.imshow(img2)
-  # plt.gca().invert_yaxis()
-
-  #Get regions
-  regions = measure.regionprops(markers, intensity_image=cells)
-
-  #Get Cluster IDs
-  cluster_ids = np.zeros(len(data))
-
-  for i in range(0,len(cluster_ids)):
-    row = math.floor(data[i][0])
-    column = math.floor(data[i][1])
-    if row < 256 and column < 256:
-      cluster_ids[i] = markers[row][column] - 10
-    elif row >= 256:
-      # cluster_ids[i] = markers[255][column]
-      cluster_ids[i] = 0
-    elif column >= 256:
-      # cluster_ids[i] = markers[row][255] 
-      cluster_ids[i] = 0
-
-  cluster_ids = cluster_ids.astype('int8')
-  cluster_ids[cluster_ids == -11] = 0
-    
-  return cluster_ids
-  
-  
-
-
-
-def visual_clustering(cluster_type, num_clusters, num_samples, noise, random_state, median_kernel_size, max_kernel_size):
-
-  NUM_CLUSTERS = num_clusters
-  CLUSTER_STD = 4 * np.ones(NUM_CLUSTERS)
-
-  if cluster_type == "blobs":
-    data = datasets.make_blobs(n_samples=num_samples, centers=NUM_CLUSTERS, random_state=random_state,center_box=(0, 256), cluster_std=CLUSTER_STD)
-  
-  elif cluster_type == "varied blobs":
-    cluster_std = 1.5 * np.ones(NUM_CLUSTERS)
-    data = datasets.make_blobs(n_samples=num_samples, centers=NUM_CLUSTERS, cluster_std=cluster_std, random_state=random_state)
-
-  elif cluster_type == "aniso":
-    X, y = datasets.make_blobs(n_samples=num_samples, centers=NUM_CLUSTERS, random_state=random_state, center_box=(-30, 30))
-    transformation = [[0.8, -0.6], [-0.4, 0.8]]
-    X_aniso = np.dot(X, transformation)
-    data = (X_aniso, y)
-
-  elif cluster_type == "noisy moons":
-    data = datasets.make_moons(n_samples=num_samples, noise=noise)
-
-  elif cluster_type == "noisy circles":
-    data = datasets.make_circles(n_samples=num_samples, factor=.01, noise=noise)
-
-  max_x = max(data[0][:, 0])
-  min_x = min(data[0][:, 0])
-  new_max = 256
-  new_min = 0
-
-  data[0][:, 0] = (((data[0][:, 0] - min_x)*(new_max-new_min))/(max_x-min_x))+ new_min
-
-  max_y = max(data[0][:, 1])
-  min_y = min(data[0][:, 1])
-  new_max_y = 256
-  new_min_y = 0
-
-  data[0][:, 1] = (((data[0][:, 1] - min_y)*(new_max_y-new_min_y))/(max_y-min_y))+ new_min_y
-
-  fig1 = plt.figure()
-  plt.scatter(data[0][:, 0], data[0][:, 1], s=1, c='black')
-  plt.close()
-
-  input = create_input_image(data[0])
-  filtered = ndimage.median_filter(input, size=median_kernel_size)
-  result = predict_sample(filtered)
-  y_km = get_instances(result, data[0], max_filter_size=max_kernel_size)
-
-  colors = np.array(list(islice(cycle(["#000000", '#377eb8', '#ff7f00', '#4daf4a',
-                                             '#f781bf', '#a65628', '#984ea3',
-                                             '#999999', '#e41a1c', '#dede00' ,'#491010']),
-                                      int(max(y_km) + 1))))
-  #add black color for outliers (if any)
-  colors = np.append(colors, ["#000000"])
-  
-  fig2 = plt.figure()
-  plt.scatter(data[0][:, 0], data[0][:, 1], s=10, color=colors[y_km.astype('int8')])
-  plt.close()
-
-  return fig1, fig2
-  
-title = "Clustering Plotted Data by Image Segmentation"
-description = '''
-Gradio Demo for Visual Clustering on synthetic datasets.
-
-* **Number of Clusters**:  Set the number of clusters to generate in the dataset (Fixed to only 2 in noisy circles and moons)
-* **Number of Samples**: Number of data points in the dataset
-* **Noise**: Controls level of noise in noisy circles and moons 
-* **Random State**: Allows you to change the location of the generated clusters
-* **Denoising Filter Kernel Size**: Size of the denoising filter
-* **Max Filter Kernel Size**: Size of the max filter 
-'''
-
-iface = gr.Interface(
-    
-  fn=visual_clustering, 
-
-  inputs=[
-          gr.inputs.Dropdown(["blobs", "varied blobs",  "aniso", "noisy moons", "noisy circles" ]),
-          gr.inputs.Slider(1, 10, step=1, label='Number of Clusters'),
-          gr.inputs.Slider(10000, 1000000, step=10000, label='Number of Samples'),
-          gr.inputs.Slider(0.03, 0.1, step=0.01, label='Noise'),
-          gr.inputs.Slider(1, 100, step=1, label='Random State'),
-          gr.inputs.Slider(1, 100, step=1, label='Denoising Filter Kernel Size'),
-          gr.inputs.Slider(1,100, step=1, label='Max Filter Kernel Size')
-          ],
-
-  outputs=[
-           gr.outputs.Image(type='plot', label='Dataset'),
-           gr.outputs.Image(type='plot', label='Clustering Result')
-           ],
-           
-  title=title,
-  description=description,
-           )
-iface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Cecil8352/vits-models/commons.py b/spaces/Cecil8352/vits-models/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/Cecil8352/vits-models/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/Chris4K/llms_compare/README.md b/spaces/Chris4K/llms_compare/README.md
deleted file mode 100644
index 9594e5f32d1250c3c8c7030a89728b2a88a9eb97..0000000000000000000000000000000000000000
--- a/spaces/Chris4K/llms_compare/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Llms Compare
-emoji: 📉
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: 8hymaWfluxwa/llms_compare
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Cicooo/vits-uma-genshin-honkai/README.md b/spaces/Cicooo/vits-uma-genshin-honkai/README.md
deleted file mode 100644
index 1c0aa069bfd980b6b45bb2bf62ff74bd9b0b61c2..0000000000000000000000000000000000000000
--- a/spaces/Cicooo/vits-uma-genshin-honkai/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-license: apache-2.0
-title: ' vits-uma-genshin-honkai'
-sdk: gradio
-sdk_version: 3.7
-emoji: 🐨
-colorTo: yellow
-pinned: false
-app_file: app.py
-duplicated_from: ikechan8370/vits-uma-genshin-honkai
----
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/samplers/__init__.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/samplers/__init__.py
deleted file mode 100644
index 27982cbe68c6173a911e700273f25973acbf04bd..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/samplers/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from .distributed import DistributedSampler
-from .grouped_batch_sampler import GroupedBatchSampler
-from .iteration_based_batch_sampler import IterationBasedBatchSampler
-
-__all__ = ["DistributedSampler", "GroupedBatchSampler", "IterationBasedBatchSampler"]
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/http_parser.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/http_parser.py
deleted file mode 100644
index 5a66ce4b9eec19777800ddc3c0f5e66b2270f9d3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/http_parser.py
+++ /dev/null
@@ -1,969 +0,0 @@
-import abc
-import asyncio
-import collections
-import re
-import string
-import zlib
-from contextlib import suppress
-from enum import IntEnum
-from typing import (
-    Any,
-    Generic,
-    List,
-    NamedTuple,
-    Optional,
-    Pattern,
-    Set,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-)
-
-from multidict import CIMultiDict, CIMultiDictProxy, istr
-from yarl import URL
-
-from . import hdrs
-from .base_protocol import BaseProtocol
-from .helpers import NO_EXTENSIONS, BaseTimerContext
-from .http_exceptions import (
-    BadHttpMessage,
-    BadStatusLine,
-    ContentEncodingError,
-    ContentLengthError,
-    InvalidHeader,
-    LineTooLong,
-    TransferEncodingError,
-)
-from .http_writer import HttpVersion, HttpVersion10
-from .log import internal_logger
-from .streams import EMPTY_PAYLOAD, StreamReader
-from .typedefs import Final, RawHeaders
-
-try:
-    import brotli
-
-    HAS_BROTLI = True
-except ImportError:  # pragma: no cover
-    HAS_BROTLI = False
-
-
-__all__ = (
-    "HeadersParser",
-    "HttpParser",
-    "HttpRequestParser",
-    "HttpResponseParser",
-    "RawRequestMessage",
-    "RawResponseMessage",
-)
-
-ASCIISET: Final[Set[str]] = set(string.printable)
-
-# See https://tools.ietf.org/html/rfc7230#section-3.1.1
-# and https://tools.ietf.org/html/rfc7230#appendix-B
-#
-#     method = token
-#     tchar = "!" / "#" / "$" / "%" / "&" / "'" / "*" / "+" / "-" / "." /
-#             "^" / "_" / "`" / "|" / "~" / DIGIT / ALPHA
-#     token = 1*tchar
-METHRE: Final[Pattern[str]] = re.compile(r"[!#$%&'*+\-.^_`|~0-9A-Za-z]+")
-VERSRE: Final[Pattern[str]] = re.compile(r"HTTP/(\d+).(\d+)")
-HDRRE: Final[Pattern[bytes]] = re.compile(rb"[\x00-\x1F\x7F()<>@,;:\[\]={} \t\\\\\"]")
-
-
-class RawRequestMessage(NamedTuple):
-    method: str
-    path: str
-    version: HttpVersion
-    headers: "CIMultiDictProxy[str]"
-    raw_headers: RawHeaders
-    should_close: bool
-    compression: Optional[str]
-    upgrade: bool
-    chunked: bool
-    url: URL
-
-
-RawResponseMessage = collections.namedtuple(
-    "RawResponseMessage",
-    [
-        "version",
-        "code",
-        "reason",
-        "headers",
-        "raw_headers",
-        "should_close",
-        "compression",
-        "upgrade",
-        "chunked",
-    ],
-)
-
-
-_MsgT = TypeVar("_MsgT", RawRequestMessage, RawResponseMessage)
-
-
-class ParseState(IntEnum):
-
-    PARSE_NONE = 0
-    PARSE_LENGTH = 1
-    PARSE_CHUNKED = 2
-    PARSE_UNTIL_EOF = 3
-
-
-class ChunkState(IntEnum):
-    PARSE_CHUNKED_SIZE = 0
-    PARSE_CHUNKED_CHUNK = 1
-    PARSE_CHUNKED_CHUNK_EOF = 2
-    PARSE_MAYBE_TRAILERS = 3
-    PARSE_TRAILERS = 4
-
-
-class HeadersParser:
-    def __init__(
-        self,
-        max_line_size: int = 8190,
-        max_headers: int = 32768,
-        max_field_size: int = 8190,
-    ) -> None:
-        self.max_line_size = max_line_size
-        self.max_headers = max_headers
-        self.max_field_size = max_field_size
-
-    def parse_headers(
-        self, lines: List[bytes]
-    ) -> Tuple["CIMultiDictProxy[str]", RawHeaders]:
-        headers: CIMultiDict[str] = CIMultiDict()
-        raw_headers = []
-
-        lines_idx = 1
-        line = lines[1]
-        line_count = len(lines)
-
-        while line:
-            # Parse initial header name : value pair.
-            try:
-                bname, bvalue = line.split(b":", 1)
-            except ValueError:
-                raise InvalidHeader(line) from None
-
-            bname = bname.strip(b" \t")
-            bvalue = bvalue.lstrip()
-            if HDRRE.search(bname):
-                raise InvalidHeader(bname)
-            if len(bname) > self.max_field_size:
-                raise LineTooLong(
-                    "request header name {}".format(
-                        bname.decode("utf8", "xmlcharrefreplace")
-                    ),
-                    str(self.max_field_size),
-                    str(len(bname)),
-                )
-
-            header_length = len(bvalue)
-
-            # next line
-            lines_idx += 1
-            line = lines[lines_idx]
-
-            # consume continuation lines
-            continuation = line and line[0] in (32, 9)  # (' ', '\t')
-
-            if continuation:
-                bvalue_lst = [bvalue]
-                while continuation:
-                    header_length += len(line)
-                    if header_length > self.max_field_size:
-                        raise LineTooLong(
-                            "request header field {}".format(
-                                bname.decode("utf8", "xmlcharrefreplace")
-                            ),
-                            str(self.max_field_size),
-                            str(header_length),
-                        )
-                    bvalue_lst.append(line)
-
-                    # next line
-                    lines_idx += 1
-                    if lines_idx < line_count:
-                        line = lines[lines_idx]
-                        if line:
-                            continuation = line[0] in (32, 9)  # (' ', '\t')
-                    else:
-                        line = b""
-                        break
-                bvalue = b"".join(bvalue_lst)
-            else:
-                if header_length > self.max_field_size:
-                    raise LineTooLong(
-                        "request header field {}".format(
-                            bname.decode("utf8", "xmlcharrefreplace")
-                        ),
-                        str(self.max_field_size),
-                        str(header_length),
-                    )
-
-            bvalue = bvalue.strip()
-            name = bname.decode("utf-8", "surrogateescape")
-            value = bvalue.decode("utf-8", "surrogateescape")
-
-            headers.add(name, value)
-            raw_headers.append((bname, bvalue))
-
-        return (CIMultiDictProxy(headers), tuple(raw_headers))
-
-
-class HttpParser(abc.ABC, Generic[_MsgT]):
-    def __init__(
-        self,
-        protocol: Optional[BaseProtocol] = None,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-        limit: int = 2**16,
-        max_line_size: int = 8190,
-        max_headers: int = 32768,
-        max_field_size: int = 8190,
-        timer: Optional[BaseTimerContext] = None,
-        code: Optional[int] = None,
-        method: Optional[str] = None,
-        readall: bool = False,
-        payload_exception: Optional[Type[BaseException]] = None,
-        response_with_body: bool = True,
-        read_until_eof: bool = False,
-        auto_decompress: bool = True,
-    ) -> None:
-        self.protocol = protocol
-        self.loop = loop
-        self.max_line_size = max_line_size
-        self.max_headers = max_headers
-        self.max_field_size = max_field_size
-        self.timer = timer
-        self.code = code
-        self.method = method
-        self.readall = readall
-        self.payload_exception = payload_exception
-        self.response_with_body = response_with_body
-        self.read_until_eof = read_until_eof
-
-        self._lines: List[bytes] = []
-        self._tail = b""
-        self._upgraded = False
-        self._payload = None
-        self._payload_parser: Optional[HttpPayloadParser] = None
-        self._auto_decompress = auto_decompress
-        self._limit = limit
-        self._headers_parser = HeadersParser(max_line_size, max_headers, max_field_size)
-
-    @abc.abstractmethod
-    def parse_message(self, lines: List[bytes]) -> _MsgT:
-        pass
-
-    def feed_eof(self) -> Optional[_MsgT]:
-        if self._payload_parser is not None:
-            self._payload_parser.feed_eof()
-            self._payload_parser = None
-        else:
-            # try to extract partial message
-            if self._tail:
-                self._lines.append(self._tail)
-
-            if self._lines:
-                if self._lines[-1] != "\r\n":
-                    self._lines.append(b"")
-                with suppress(Exception):
-                    return self.parse_message(self._lines)
-        return None
-
-    def feed_data(
-        self,
-        data: bytes,
-        SEP: bytes = b"\r\n",
-        EMPTY: bytes = b"",
-        CONTENT_LENGTH: istr = hdrs.CONTENT_LENGTH,
-        METH_CONNECT: str = hdrs.METH_CONNECT,
-        SEC_WEBSOCKET_KEY1: istr = hdrs.SEC_WEBSOCKET_KEY1,
-    ) -> Tuple[List[Tuple[_MsgT, StreamReader]], bool, bytes]:
-
-        messages = []
-
-        if self._tail:
-            data, self._tail = self._tail + data, b""
-
-        data_len = len(data)
-        start_pos = 0
-        loop = self.loop
-
-        while start_pos < data_len:
-
-            # read HTTP message (request/response line + headers), \r\n\r\n
-            # and split by lines
-            if self._payload_parser is None and not self._upgraded:
-                pos = data.find(SEP, start_pos)
-                # consume \r\n
-                if pos == start_pos and not self._lines:
-                    start_pos = pos + 2
-                    continue
-
-                if pos >= start_pos:
-                    # line found
-                    self._lines.append(data[start_pos:pos])
-                    start_pos = pos + 2
-
-                    # \r\n\r\n found
-                    if self._lines[-1] == EMPTY:
-                        try:
-                            msg: _MsgT = self.parse_message(self._lines)
-                        finally:
-                            self._lines.clear()
-
-                        def get_content_length() -> Optional[int]:
-                            # payload length
-                            length_hdr = msg.headers.get(CONTENT_LENGTH)
-                            if length_hdr is None:
-                                return None
-
-                            try:
-                                length = int(length_hdr)
-                            except ValueError:
-                                raise InvalidHeader(CONTENT_LENGTH)
-
-                            if length < 0:
-                                raise InvalidHeader(CONTENT_LENGTH)
-
-                            return length
-
-                        length = get_content_length()
-                        # do not support old websocket spec
-                        if SEC_WEBSOCKET_KEY1 in msg.headers:
-                            raise InvalidHeader(SEC_WEBSOCKET_KEY1)
-
-                        self._upgraded = msg.upgrade
-
-                        method = getattr(msg, "method", self.method)
-
-                        assert self.protocol is not None
-                        # calculate payload
-                        if (
-                            (length is not None and length > 0)
-                            or msg.chunked
-                            and not msg.upgrade
-                        ):
-                            payload = StreamReader(
-                                self.protocol,
-                                timer=self.timer,
-                                loop=loop,
-                                limit=self._limit,
-                            )
-                            payload_parser = HttpPayloadParser(
-                                payload,
-                                length=length,
-                                chunked=msg.chunked,
-                                method=method,
-                                compression=msg.compression,
-                                code=self.code,
-                                readall=self.readall,
-                                response_with_body=self.response_with_body,
-                                auto_decompress=self._auto_decompress,
-                            )
-                            if not payload_parser.done:
-                                self._payload_parser = payload_parser
-                        elif method == METH_CONNECT:
-                            assert isinstance(msg, RawRequestMessage)
-                            payload = StreamReader(
-                                self.protocol,
-                                timer=self.timer,
-                                loop=loop,
-                                limit=self._limit,
-                            )
-                            self._upgraded = True
-                            self._payload_parser = HttpPayloadParser(
-                                payload,
-                                method=msg.method,
-                                compression=msg.compression,
-                                readall=True,
-                                auto_decompress=self._auto_decompress,
-                            )
-                        else:
-                            if (
-                                getattr(msg, "code", 100) >= 199
-                                and length is None
-                                and self.read_until_eof
-                            ):
-                                payload = StreamReader(
-                                    self.protocol,
-                                    timer=self.timer,
-                                    loop=loop,
-                                    limit=self._limit,
-                                )
-                                payload_parser = HttpPayloadParser(
-                                    payload,
-                                    length=length,
-                                    chunked=msg.chunked,
-                                    method=method,
-                                    compression=msg.compression,
-                                    code=self.code,
-                                    readall=True,
-                                    response_with_body=self.response_with_body,
-                                    auto_decompress=self._auto_decompress,
-                                )
-                                if not payload_parser.done:
-                                    self._payload_parser = payload_parser
-                            else:
-                                payload = EMPTY_PAYLOAD
-
-                        messages.append((msg, payload))
-                else:
-                    self._tail = data[start_pos:]
-                    data = EMPTY
-                    break
-
-            # no parser, just store
-            elif self._payload_parser is None and self._upgraded:
-                assert not self._lines
-                break
-
-            # feed payload
-            elif data and start_pos < data_len:
-                assert not self._lines
-                assert self._payload_parser is not None
-                try:
-                    eof, data = self._payload_parser.feed_data(data[start_pos:])
-                except BaseException as exc:
-                    if self.payload_exception is not None:
-                        self._payload_parser.payload.set_exception(
-                            self.payload_exception(str(exc))
-                        )
-                    else:
-                        self._payload_parser.payload.set_exception(exc)
-
-                    eof = True
-                    data = b""
-
-                if eof:
-                    start_pos = 0
-                    data_len = len(data)
-                    self._payload_parser = None
-                    continue
-            else:
-                break
-
-        if data and start_pos < data_len:
-            data = data[start_pos:]
-        else:
-            data = EMPTY
-
-        return messages, self._upgraded, data
-
-    def parse_headers(
-        self, lines: List[bytes]
-    ) -> Tuple[
-        "CIMultiDictProxy[str]", RawHeaders, Optional[bool], Optional[str], bool, bool
-    ]:
-        """Parses RFC 5322 headers from a stream.
-
-        Line continuations are supported. Returns list of header name
-        and value pairs. Header name is in upper case.
-        """
-        headers, raw_headers = self._headers_parser.parse_headers(lines)
-        close_conn = None
-        encoding = None
-        upgrade = False
-        chunked = False
-
-        # keep-alive
-        conn = headers.get(hdrs.CONNECTION)
-        if conn:
-            v = conn.lower()
-            if v == "close":
-                close_conn = True
-            elif v == "keep-alive":
-                close_conn = False
-            elif v == "upgrade":
-                upgrade = True
-
-        # encoding
-        enc = headers.get(hdrs.CONTENT_ENCODING)
-        if enc:
-            enc = enc.lower()
-            if enc in ("gzip", "deflate", "br"):
-                encoding = enc
-
-        # chunking
-        te = headers.get(hdrs.TRANSFER_ENCODING)
-        if te is not None:
-            if "chunked" == te.lower():
-                chunked = True
-            else:
-                raise BadHttpMessage("Request has invalid `Transfer-Encoding`")
-
-            if hdrs.CONTENT_LENGTH in headers:
-                raise BadHttpMessage(
-                    "Content-Length can't be present with Transfer-Encoding",
-                )
-
-        return (headers, raw_headers, close_conn, encoding, upgrade, chunked)
-
-    def set_upgraded(self, val: bool) -> None:
-        """Set connection upgraded (to websocket) mode.
-
-        :param bool val: new state.
-        """
-        self._upgraded = val
-
-
-class HttpRequestParser(HttpParser[RawRequestMessage]):
-    """Read request status line.
-
-    Exception .http_exceptions.BadStatusLine
-    could be raised in case of any errors in status line.
-    Returns RawRequestMessage.
-    """
-
-    def parse_message(self, lines: List[bytes]) -> RawRequestMessage:
-        # request line
-        line = lines[0].decode("utf-8", "surrogateescape")
-        try:
-            method, path, version = line.split(None, 2)
-        except ValueError:
-            raise BadStatusLine(line) from None
-
-        if len(path) > self.max_line_size:
-            raise LineTooLong(
-                "Status line is too long", str(self.max_line_size), str(len(path))
-            )
-
-        # method
-        if not METHRE.match(method):
-            raise BadStatusLine(method)
-
-        # version
-        try:
-            if version.startswith("HTTP/"):
-                n1, n2 = version[5:].split(".", 1)
-                version_o = HttpVersion(int(n1), int(n2))
-            else:
-                raise BadStatusLine(version)
-        except Exception:
-            raise BadStatusLine(version)
-
-        if method == "CONNECT":
-            # authority-form,
-            # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.3
-            url = URL.build(authority=path, encoded=True)
-        elif path.startswith("/"):
-            # origin-form,
-            # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.1
-            path_part, _hash_separator, url_fragment = path.partition("#")
-            path_part, _question_mark_separator, qs_part = path_part.partition("?")
-
-            # NOTE: `yarl.URL.build()` is used to mimic what the Cython-based
-            # NOTE: parser does, otherwise it results into the same
-            # NOTE: HTTP Request-Line input producing different
-            # NOTE: `yarl.URL()` objects
-            url = URL.build(
-                path=path_part,
-                query_string=qs_part,
-                fragment=url_fragment,
-                encoded=True,
-            )
-        else:
-            # absolute-form for proxy maybe,
-            # https://datatracker.ietf.org/doc/html/rfc7230#section-5.3.2
-            url = URL(path, encoded=True)
-
-        # read headers
-        (
-            headers,
-            raw_headers,
-            close,
-            compression,
-            upgrade,
-            chunked,
-        ) = self.parse_headers(lines)
-
-        if close is None:  # then the headers weren't set in the request
-            if version_o <= HttpVersion10:  # HTTP 1.0 must asks to not close
-                close = True
-            else:  # HTTP 1.1 must ask to close.
-                close = False
-
-        return RawRequestMessage(
-            method,
-            path,
-            version_o,
-            headers,
-            raw_headers,
-            close,
-            compression,
-            upgrade,
-            chunked,
-            url,
-        )
-
-
-class HttpResponseParser(HttpParser[RawResponseMessage]):
-    """Read response status line and headers.
-
-    BadStatusLine could be raised in case of any errors in status line.
-    Returns RawResponseMessage.
-    """
-
-    def parse_message(self, lines: List[bytes]) -> RawResponseMessage:
-        line = lines[0].decode("utf-8", "surrogateescape")
-        try:
-            version, status = line.split(None, 1)
-        except ValueError:
-            raise BadStatusLine(line) from None
-
-        try:
-            status, reason = status.split(None, 1)
-        except ValueError:
-            reason = ""
-
-        if len(reason) > self.max_line_size:
-            raise LineTooLong(
-                "Status line is too long", str(self.max_line_size), str(len(reason))
-            )
-
-        # version
-        match = VERSRE.match(version)
-        if match is None:
-            raise BadStatusLine(line)
-        version_o = HttpVersion(int(match.group(1)), int(match.group(2)))
-
-        # The status code is a three-digit number
-        try:
-            status_i = int(status)
-        except ValueError:
-            raise BadStatusLine(line) from None
-
-        if status_i > 999:
-            raise BadStatusLine(line)
-
-        # read headers
-        (
-            headers,
-            raw_headers,
-            close,
-            compression,
-            upgrade,
-            chunked,
-        ) = self.parse_headers(lines)
-
-        if close is None:
-            close = version_o <= HttpVersion10
-
-        return RawResponseMessage(
-            version_o,
-            status_i,
-            reason.strip(),
-            headers,
-            raw_headers,
-            close,
-            compression,
-            upgrade,
-            chunked,
-        )
-
-
-class HttpPayloadParser:
-    def __init__(
-        self,
-        payload: StreamReader,
-        length: Optional[int] = None,
-        chunked: bool = False,
-        compression: Optional[str] = None,
-        code: Optional[int] = None,
-        method: Optional[str] = None,
-        readall: bool = False,
-        response_with_body: bool = True,
-        auto_decompress: bool = True,
-    ) -> None:
-        self._length = 0
-        self._type = ParseState.PARSE_NONE
-        self._chunk = ChunkState.PARSE_CHUNKED_SIZE
-        self._chunk_size = 0
-        self._chunk_tail = b""
-        self._auto_decompress = auto_decompress
-        self.done = False
-
-        # payload decompression wrapper
-        if response_with_body and compression and self._auto_decompress:
-            real_payload: Union[StreamReader, DeflateBuffer] = DeflateBuffer(
-                payload, compression
-            )
-        else:
-            real_payload = payload
-
-        # payload parser
-        if not response_with_body:
-            # don't parse payload if it's not expected to be received
-            self._type = ParseState.PARSE_NONE
-            real_payload.feed_eof()
-            self.done = True
-
-        elif chunked:
-            self._type = ParseState.PARSE_CHUNKED
-        elif length is not None:
-            self._type = ParseState.PARSE_LENGTH
-            self._length = length
-            if self._length == 0:
-                real_payload.feed_eof()
-                self.done = True
-        else:
-            if readall and code != 204:
-                self._type = ParseState.PARSE_UNTIL_EOF
-            elif method in ("PUT", "POST"):
-                internal_logger.warning(  # pragma: no cover
-                    "Content-Length or Transfer-Encoding header is required"
-                )
-                self._type = ParseState.PARSE_NONE
-                real_payload.feed_eof()
-                self.done = True
-
-        self.payload = real_payload
-
-    def feed_eof(self) -> None:
-        if self._type == ParseState.PARSE_UNTIL_EOF:
-            self.payload.feed_eof()
-        elif self._type == ParseState.PARSE_LENGTH:
-            raise ContentLengthError(
-                "Not enough data for satisfy content length header."
-            )
-        elif self._type == ParseState.PARSE_CHUNKED:
-            raise TransferEncodingError(
-                "Not enough data for satisfy transfer length header."
-            )
-
-    def feed_data(
-        self, chunk: bytes, SEP: bytes = b"\r\n", CHUNK_EXT: bytes = b";"
-    ) -> Tuple[bool, bytes]:
-        # Read specified amount of bytes
-        if self._type == ParseState.PARSE_LENGTH:
-            required = self._length
-            chunk_len = len(chunk)
-
-            if required >= chunk_len:
-                self._length = required - chunk_len
-                self.payload.feed_data(chunk, chunk_len)
-                if self._length == 0:
-                    self.payload.feed_eof()
-                    return True, b""
-            else:
-                self._length = 0
-                self.payload.feed_data(chunk[:required], required)
-                self.payload.feed_eof()
-                return True, chunk[required:]
-
-        # Chunked transfer encoding parser
-        elif self._type == ParseState.PARSE_CHUNKED:
-            if self._chunk_tail:
-                chunk = self._chunk_tail + chunk
-                self._chunk_tail = b""
-
-            while chunk:
-
-                # read next chunk size
-                if self._chunk == ChunkState.PARSE_CHUNKED_SIZE:
-                    pos = chunk.find(SEP)
-                    if pos >= 0:
-                        i = chunk.find(CHUNK_EXT, 0, pos)
-                        if i >= 0:
-                            size_b = chunk[:i]  # strip chunk-extensions
-                        else:
-                            size_b = chunk[:pos]
-
-                        try:
-                            size = int(bytes(size_b), 16)
-                        except ValueError:
-                            exc = TransferEncodingError(
-                                chunk[:pos].decode("ascii", "surrogateescape")
-                            )
-                            self.payload.set_exception(exc)
-                            raise exc from None
-
-                        chunk = chunk[pos + 2 :]
-                        if size == 0:  # eof marker
-                            self._chunk = ChunkState.PARSE_MAYBE_TRAILERS
-                        else:
-                            self._chunk = ChunkState.PARSE_CHUNKED_CHUNK
-                            self._chunk_size = size
-                            self.payload.begin_http_chunk_receiving()
-                    else:
-                        self._chunk_tail = chunk
-                        return False, b""
-
-                # read chunk and feed buffer
-                if self._chunk == ChunkState.PARSE_CHUNKED_CHUNK:
-                    required = self._chunk_size
-                    chunk_len = len(chunk)
-
-                    if required > chunk_len:
-                        self._chunk_size = required - chunk_len
-                        self.payload.feed_data(chunk, chunk_len)
-                        return False, b""
-                    else:
-                        self._chunk_size = 0
-                        self.payload.feed_data(chunk[:required], required)
-                        chunk = chunk[required:]
-                        self._chunk = ChunkState.PARSE_CHUNKED_CHUNK_EOF
-                        self.payload.end_http_chunk_receiving()
-
-                # toss the CRLF at the end of the chunk
-                if self._chunk == ChunkState.PARSE_CHUNKED_CHUNK_EOF:
-                    if chunk[:2] == SEP:
-                        chunk = chunk[2:]
-                        self._chunk = ChunkState.PARSE_CHUNKED_SIZE
-                    else:
-                        self._chunk_tail = chunk
-                        return False, b""
-
-                # if stream does not contain trailer, after 0\r\n
-                # we should get another \r\n otherwise
-                # trailers needs to be skiped until \r\n\r\n
-                if self._chunk == ChunkState.PARSE_MAYBE_TRAILERS:
-                    head = chunk[:2]
-                    if head == SEP:
-                        # end of stream
-                        self.payload.feed_eof()
-                        return True, chunk[2:]
-                    # Both CR and LF, or only LF may not be received yet. It is
-                    # expected that CRLF or LF will be shown at the very first
-                    # byte next time, otherwise trailers should come. The last
-                    # CRLF which marks the end of response might not be
-                    # contained in the same TCP segment which delivered the
-                    # size indicator.
-                    if not head:
-                        return False, b""
-                    if head == SEP[:1]:
-                        self._chunk_tail = head
-                        return False, b""
-                    self._chunk = ChunkState.PARSE_TRAILERS
-
-                # read and discard trailer up to the CRLF terminator
-                if self._chunk == ChunkState.PARSE_TRAILERS:
-                    pos = chunk.find(SEP)
-                    if pos >= 0:
-                        chunk = chunk[pos + 2 :]
-                        self._chunk = ChunkState.PARSE_MAYBE_TRAILERS
-                    else:
-                        self._chunk_tail = chunk
-                        return False, b""
-
-        # Read all bytes until eof
-        elif self._type == ParseState.PARSE_UNTIL_EOF:
-            self.payload.feed_data(chunk, len(chunk))
-
-        return False, b""
-
-
-class DeflateBuffer:
-    """DeflateStream decompress stream and feed data into specified stream."""
-
-    decompressor: Any
-
-    def __init__(self, out: StreamReader, encoding: Optional[str]) -> None:
-        self.out = out
-        self.size = 0
-        self.encoding = encoding
-        self._started_decoding = False
-
-        if encoding == "br":
-            if not HAS_BROTLI:  # pragma: no cover
-                raise ContentEncodingError(
-                    "Can not decode content-encoding: brotli (br). "
-                    "Please install `Brotli`"
-                )
-
-            class BrotliDecoder:
-                # Supports both 'brotlipy' and 'Brotli' packages
-                # since they share an import name. The top branches
-                # are for 'brotlipy' and bottom branches for 'Brotli'
-                def __init__(self) -> None:
-                    self._obj = brotli.Decompressor()
-
-                def decompress(self, data: bytes) -> bytes:
-                    if hasattr(self._obj, "decompress"):
-                        return cast(bytes, self._obj.decompress(data))
-                    return cast(bytes, self._obj.process(data))
-
-                def flush(self) -> bytes:
-                    if hasattr(self._obj, "flush"):
-                        return cast(bytes, self._obj.flush())
-                    return b""
-
-            self.decompressor = BrotliDecoder()
-        else:
-            zlib_mode = 16 + zlib.MAX_WBITS if encoding == "gzip" else zlib.MAX_WBITS
-            self.decompressor = zlib.decompressobj(wbits=zlib_mode)
-
-    def set_exception(self, exc: BaseException) -> None:
-        self.out.set_exception(exc)
-
-    def feed_data(self, chunk: bytes, size: int) -> None:
-        if not size:
-            return
-
-        self.size += size
-
-        # RFC1950
-        # bits 0..3 = CM = 0b1000 = 8 = "deflate"
-        # bits 4..7 = CINFO = 1..7 = windows size.
-        if (
-            not self._started_decoding
-            and self.encoding == "deflate"
-            and chunk[0] & 0xF != 8
-        ):
-            # Change the decoder to decompress incorrectly compressed data
-            # Actually we should issue a warning about non-RFC-compliant data.
-            self.decompressor = zlib.decompressobj(wbits=-zlib.MAX_WBITS)
-
-        try:
-            chunk = self.decompressor.decompress(chunk)
-        except Exception:
-            raise ContentEncodingError(
-                "Can not decode content-encoding: %s" % self.encoding
-            )
-
-        self._started_decoding = True
-
-        if chunk:
-            self.out.feed_data(chunk, len(chunk))
-
-    def feed_eof(self) -> None:
-        chunk = self.decompressor.flush()
-
-        if chunk or self.size > 0:
-            self.out.feed_data(chunk, len(chunk))
-            if self.encoding == "deflate" and not self.decompressor.eof:
-                raise ContentEncodingError("deflate")
-
-        self.out.feed_eof()
-
-    def begin_http_chunk_receiving(self) -> None:
-        self.out.begin_http_chunk_receiving()
-
-    def end_http_chunk_receiving(self) -> None:
-        self.out.end_http_chunk_receiving()
-
-
-HttpRequestParserPy = HttpRequestParser
-HttpResponseParserPy = HttpResponseParser
-RawRequestMessagePy = RawRequestMessage
-RawResponseMessagePy = RawResponseMessage
-
-try:
-    if not NO_EXTENSIONS:
-        from ._http_parser import (  # type: ignore[import,no-redef]
-            HttpRequestParser,
-            HttpResponseParser,
-            RawRequestMessage,
-            RawResponseMessage,
-        )
-
-        HttpRequestParserC = HttpRequestParser
-        HttpResponseParserC = HttpResponseParser
-        RawRequestMessageC = RawRequestMessage
-        RawResponseMessageC = RawResponseMessage
-except ImportError:  # pragma: no cover
-    pass
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/tz/win.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/tz/win.py
deleted file mode 100644
index cde07ba792c40903f0c334839140173b39fd8124..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/tz/win.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module provides an interface to the native time zone data on Windows,
-including :py:class:`datetime.tzinfo` implementations.
-
-Attempting to import this module on a non-Windows platform will raise an
-:py:obj:`ImportError`.
-"""
-# This code was originally contributed by Jeffrey Harris.
-import datetime
-import struct
-
-from six.moves import winreg
-from six import text_type
-
-try:
-    import ctypes
-    from ctypes import wintypes
-except ValueError:
-    # ValueError is raised on non-Windows systems for some horrible reason.
-    raise ImportError("Running tzwin on non-Windows system")
-
-from ._common import tzrangebase
-
-__all__ = ["tzwin", "tzwinlocal", "tzres"]
-
-ONEWEEK = datetime.timedelta(7)
-
-TZKEYNAMENT = r"SOFTWARE\Microsoft\Windows NT\CurrentVersion\Time Zones"
-TZKEYNAME9X = r"SOFTWARE\Microsoft\Windows\CurrentVersion\Time Zones"
-TZLOCALKEYNAME = r"SYSTEM\CurrentControlSet\Control\TimeZoneInformation"
-
-
-def _settzkeyname():
-    handle = winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE)
-    try:
-        winreg.OpenKey(handle, TZKEYNAMENT).Close()
-        TZKEYNAME = TZKEYNAMENT
-    except WindowsError:
-        TZKEYNAME = TZKEYNAME9X
-    handle.Close()
-    return TZKEYNAME
-
-
-TZKEYNAME = _settzkeyname()
-
-
-class tzres(object):
-    """
-    Class for accessing ``tzres.dll``, which contains timezone name related
-    resources.
-
-    .. versionadded:: 2.5.0
-    """
-    p_wchar = ctypes.POINTER(wintypes.WCHAR)        # Pointer to a wide char
-
-    def __init__(self, tzres_loc='tzres.dll'):
-        # Load the user32 DLL so we can load strings from tzres
-        user32 = ctypes.WinDLL('user32')
-
-        # Specify the LoadStringW function
-        user32.LoadStringW.argtypes = (wintypes.HINSTANCE,
-                                       wintypes.UINT,
-                                       wintypes.LPWSTR,
-                                       ctypes.c_int)
-
-        self.LoadStringW = user32.LoadStringW
-        self._tzres = ctypes.WinDLL(tzres_loc)
-        self.tzres_loc = tzres_loc
-
-    def load_name(self, offset):
-        """
-        Load a timezone name from a DLL offset (integer).
-
-        >>> from dateutil.tzwin import tzres
-        >>> tzr = tzres()
-        >>> print(tzr.load_name(112))
-        'Eastern Standard Time'
-
-        :param offset:
-            A positive integer value referring to a string from the tzres dll.
-
-        .. note::
-
-            Offsets found in the registry are generally of the form
-            ``@tzres.dll,-114``. The offset in this case is 114, not -114.
-
-        """
-        resource = self.p_wchar()
-        lpBuffer = ctypes.cast(ctypes.byref(resource), wintypes.LPWSTR)
-        nchar = self.LoadStringW(self._tzres._handle, offset, lpBuffer, 0)
-        return resource[:nchar]
-
-    def name_from_string(self, tzname_str):
-        """
-        Parse strings as returned from the Windows registry into the time zone
-        name as defined in the registry.
-
-        >>> from dateutil.tzwin import tzres
-        >>> tzr = tzres()
-        >>> print(tzr.name_from_string('@tzres.dll,-251'))
-        'Dateline Daylight Time'
-        >>> print(tzr.name_from_string('Eastern Standard Time'))
-        'Eastern Standard Time'
-
-        :param tzname_str:
-            A timezone name string as returned from a Windows registry key.
-
-        :return:
-            Returns the localized timezone string from tzres.dll if the string
-            is of the form `@tzres.dll,-offset`, else returns the input string.
-        """
-        if not tzname_str.startswith('@'):
-            return tzname_str
-
-        name_splt = tzname_str.split(',-')
-        try:
-            offset = int(name_splt[1])
-        except:
-            raise ValueError("Malformed timezone string.")
-
-        return self.load_name(offset)
-
-
-class tzwinbase(tzrangebase):
-    """tzinfo class based on win32's timezones available in the registry."""
-    def __init__(self):
-        raise NotImplementedError('tzwinbase is an abstract base class')
-
-    def __eq__(self, other):
-        # Compare on all relevant dimensions, including name.
-        if not isinstance(other, tzwinbase):
-            return NotImplemented
-
-        return  (self._std_offset == other._std_offset and
-                 self._dst_offset == other._dst_offset and
-                 self._stddayofweek == other._stddayofweek and
-                 self._dstdayofweek == other._dstdayofweek and
-                 self._stdweeknumber == other._stdweeknumber and
-                 self._dstweeknumber == other._dstweeknumber and
-                 self._stdhour == other._stdhour and
-                 self._dsthour == other._dsthour and
-                 self._stdminute == other._stdminute and
-                 self._dstminute == other._dstminute and
-                 self._std_abbr == other._std_abbr and
-                 self._dst_abbr == other._dst_abbr)
-
-    @staticmethod
-    def list():
-        """Return a list of all time zones known to the system."""
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            with winreg.OpenKey(handle, TZKEYNAME) as tzkey:
-                result = [winreg.EnumKey(tzkey, i)
-                          for i in range(winreg.QueryInfoKey(tzkey)[0])]
-        return result
-
-    def display(self):
-        """
-        Return the display name of the time zone.
-        """
-        return self._display
-
-    def transitions(self, year):
-        """
-        For a given year, get the DST on and off transition times, expressed
-        always on the standard time side. For zones with no transitions, this
-        function returns ``None``.
-
-        :param year:
-            The year whose transitions you would like to query.
-
-        :return:
-            Returns a :class:`tuple` of :class:`datetime.datetime` objects,
-            ``(dston, dstoff)`` for zones with an annual DST transition, or
-            ``None`` for fixed offset zones.
-        """
-
-        if not self.hasdst:
-            return None
-
-        dston = picknthweekday(year, self._dstmonth, self._dstdayofweek,
-                               self._dsthour, self._dstminute,
-                               self._dstweeknumber)
-
-        dstoff = picknthweekday(year, self._stdmonth, self._stddayofweek,
-                                self._stdhour, self._stdminute,
-                                self._stdweeknumber)
-
-        # Ambiguous dates default to the STD side
-        dstoff -= self._dst_base_offset
-
-        return dston, dstoff
-
-    def _get_hasdst(self):
-        return self._dstmonth != 0
-
-    @property
-    def _dst_base_offset(self):
-        return self._dst_base_offset_
-
-
-class tzwin(tzwinbase):
-    """
-    Time zone object created from the zone info in the Windows registry
-
-    These are similar to :py:class:`dateutil.tz.tzrange` objects in that
-    the time zone data is provided in the format of a single offset rule
-    for either 0 or 2 time zone transitions per year.
-
-    :param: name
-        The name of a Windows time zone key, e.g. "Eastern Standard Time".
-        The full list of keys can be retrieved with :func:`tzwin.list`.
-    """
-
-    def __init__(self, name):
-        self._name = name
-
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            tzkeyname = text_type("{kn}\\{name}").format(kn=TZKEYNAME, name=name)
-            with winreg.OpenKey(handle, tzkeyname) as tzkey:
-                keydict = valuestodict(tzkey)
-
-        self._std_abbr = keydict["Std"]
-        self._dst_abbr = keydict["Dlt"]
-
-        self._display = keydict["Display"]
-
-        # See http://ww_winreg.jsiinc.com/SUBA/tip0300/rh0398.htm
-        tup = struct.unpack("=3l16h", keydict["TZI"])
-        stdoffset = -tup[0]-tup[1]          # Bias + StandardBias * -1
-        dstoffset = stdoffset-tup[2]        # + DaylightBias * -1
-        self._std_offset = datetime.timedelta(minutes=stdoffset)
-        self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
-        # for the meaning see the win32 TIME_ZONE_INFORMATION structure docs
-        # http://msdn.microsoft.com/en-us/library/windows/desktop/ms725481(v=vs.85).aspx
-        (self._stdmonth,
-         self._stddayofweek,   # Sunday = 0
-         self._stdweeknumber,  # Last = 5
-         self._stdhour,
-         self._stdminute) = tup[4:9]
-
-        (self._dstmonth,
-         self._dstdayofweek,   # Sunday = 0
-         self._dstweeknumber,  # Last = 5
-         self._dsthour,
-         self._dstminute) = tup[12:17]
-
-        self._dst_base_offset_ = self._dst_offset - self._std_offset
-        self.hasdst = self._get_hasdst()
-
-    def __repr__(self):
-        return "tzwin(%s)" % repr(self._name)
-
-    def __reduce__(self):
-        return (self.__class__, (self._name,))
-
-
-class tzwinlocal(tzwinbase):
-    """
-    Class representing the local time zone information in the Windows registry
-
-    While :class:`dateutil.tz.tzlocal` makes system calls (via the :mod:`time`
-    module) to retrieve time zone information, ``tzwinlocal`` retrieves the
-    rules directly from the Windows registry and creates an object like
-    :class:`dateutil.tz.tzwin`.
-
-    Because Windows does not have an equivalent of :func:`time.tzset`, on
-    Windows, :class:`dateutil.tz.tzlocal` instances will always reflect the
-    time zone settings *at the time that the process was started*, meaning
-    changes to the machine's time zone settings during the run of a program
-    on Windows will **not** be reflected by :class:`dateutil.tz.tzlocal`.
-    Because ``tzwinlocal`` reads the registry directly, it is unaffected by
-    this issue.
-    """
-    def __init__(self):
-        with winreg.ConnectRegistry(None, winreg.HKEY_LOCAL_MACHINE) as handle:
-            with winreg.OpenKey(handle, TZLOCALKEYNAME) as tzlocalkey:
-                keydict = valuestodict(tzlocalkey)
-
-            self._std_abbr = keydict["StandardName"]
-            self._dst_abbr = keydict["DaylightName"]
-
-            try:
-                tzkeyname = text_type('{kn}\\{sn}').format(kn=TZKEYNAME,
-                                                          sn=self._std_abbr)
-                with winreg.OpenKey(handle, tzkeyname) as tzkey:
-                    _keydict = valuestodict(tzkey)
-                    self._display = _keydict["Display"]
-            except OSError:
-                self._display = None
-
-        stdoffset = -keydict["Bias"]-keydict["StandardBias"]
-        dstoffset = stdoffset-keydict["DaylightBias"]
-
-        self._std_offset = datetime.timedelta(minutes=stdoffset)
-        self._dst_offset = datetime.timedelta(minutes=dstoffset)
-
-        # For reasons unclear, in this particular key, the day of week has been
-        # moved to the END of the SYSTEMTIME structure.
-        tup = struct.unpack("=8h", keydict["StandardStart"])
-
-        (self._stdmonth,
-         self._stdweeknumber,  # Last = 5
-         self._stdhour,
-         self._stdminute) = tup[1:5]
-
-        self._stddayofweek = tup[7]
-
-        tup = struct.unpack("=8h", keydict["DaylightStart"])
-
-        (self._dstmonth,
-         self._dstweeknumber,  # Last = 5
-         self._dsthour,
-         self._dstminute) = tup[1:5]
-
-        self._dstdayofweek = tup[7]
-
-        self._dst_base_offset_ = self._dst_offset - self._std_offset
-        self.hasdst = self._get_hasdst()
-
-    def __repr__(self):
-        return "tzwinlocal()"
-
-    def __str__(self):
-        # str will return the standard name, not the daylight name.
-        return "tzwinlocal(%s)" % repr(self._std_abbr)
-
-    def __reduce__(self):
-        return (self.__class__, ())
-
-
-def picknthweekday(year, month, dayofweek, hour, minute, whichweek):
-    """ dayofweek == 0 means Sunday, whichweek 5 means last instance """
-    first = datetime.datetime(year, month, 1, hour, minute)
-
-    # This will work if dayofweek is ISO weekday (1-7) or Microsoft-style (0-6),
-    # Because 7 % 7 = 0
-    weekdayone = first.replace(day=((dayofweek - first.isoweekday()) % 7) + 1)
-    wd = weekdayone + ((whichweek - 1) * ONEWEEK)
-    if (wd.month != month):
-        wd -= ONEWEEK
-
-    return wd
-
-
-def valuestodict(key):
-    """Convert a registry key's values to a dictionary."""
-    dout = {}
-    size = winreg.QueryInfoKey(key)[1]
-    tz_res = None
-
-    for i in range(size):
-        key_name, value, dtype = winreg.EnumValue(key, i)
-        if dtype == winreg.REG_DWORD or dtype == winreg.REG_DWORD_LITTLE_ENDIAN:
-            # If it's a DWORD (32-bit integer), it's stored as unsigned - convert
-            # that to a proper signed integer
-            if value & (1 << 31):
-                value = value - (1 << 32)
-        elif dtype == winreg.REG_SZ:
-            # If it's a reference to the tzres DLL, load the actual string
-            if value.startswith('@tzres'):
-                tz_res = tz_res or tzres()
-                value = tz_res.name_from_string(value)
-
-            value = value.rstrip('\x00')    # Remove trailing nulls
-
-        dout[key_name] = value
-
-    return dout
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/filelock/_api.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/filelock/_api.py
deleted file mode 100644
index 7754f084fc7b656a44dfb4e2a0b6d0a10f112eaf..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/filelock/_api.py
+++ /dev/null
@@ -1,281 +0,0 @@
-from __future__ import annotations
-
-import contextlib
-import logging
-import os
-import time
-import warnings
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from threading import local
-from typing import TYPE_CHECKING, Any
-
-from ._error import Timeout
-
-if TYPE_CHECKING:
-    from types import TracebackType
-
-_LOGGER = logging.getLogger("filelock")
-
-
-# This is a helper class which is returned by :meth:`BaseFileLock.acquire` and wraps the lock to make sure __enter__
-# is not called twice when entering the with statement. If we would simply return *self*, the lock would be acquired
-# again in the *__enter__* method of the BaseFileLock, but not released again automatically. issue #37 (memory leak)
-class AcquireReturnProxy:
-    """A context aware object that will release the lock file when exiting."""
-
-    def __init__(self, lock: BaseFileLock) -> None:
-        self.lock = lock
-
-    def __enter__(self) -> BaseFileLock:
-        return self.lock
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_value: BaseException | None,
-        traceback: TracebackType | None,
-    ) -> None:
-        self.lock.release()
-
-
-@dataclass
-class FileLockContext:
-    """A dataclass which holds the context for a ``BaseFileLock`` object."""
-
-    # The context is held in a separate class to allow optional use of thread local storage via the
-    # ThreadLocalFileContext class.
-
-    #: The path to the lock file.
-    lock_file: str
-
-    #: The default timeout value.
-    timeout: float
-
-    #: The mode for the lock files
-    mode: int
-
-    #: The file descriptor for the *_lock_file* as it is returned by the os.open() function, not None when lock held
-    lock_file_fd: int | None = None
-
-    #: The lock counter is used for implementing the nested locking mechanism.
-    lock_counter: int = 0  # When the lock is acquired is increased and the lock is only released, when this value is 0
-
-
-class ThreadLocalFileContext(FileLockContext, local):
-    """A thread local version of the ``FileLockContext`` class."""
-
-
-class BaseFileLock(ABC, contextlib.ContextDecorator):
-    """Abstract base class for a file lock object."""
-
-    def __init__(
-        self,
-        lock_file: str | os.PathLike[Any],
-        timeout: float = -1,
-        mode: int = 0o644,
-        thread_local: bool = True,  # noqa: FBT001, FBT002
-    ) -> None:
-        """
-        Create a new lock object.
-
-        :param lock_file: path to the file
-        :param timeout: default timeout when acquiring the lock, in seconds. It will be used as fallback value in
-        the acquire method, if no timeout value (``None``) is given. If you want to disable the timeout, set it
-        to a negative value. A timeout of 0 means, that there is exactly one attempt to acquire the file lock.
-        :param mode: file permissions for the lockfile.
-        :param thread_local: Whether this object's internal context should be thread local or not.
-        If this is set to ``False`` then the lock will be reentrant across threads.
-        """
-        self._is_thread_local = thread_local
-
-        # Create the context. Note that external code should not work with the context directly  and should instead use
-        # properties of this class.
-        kwargs: dict[str, Any] = {
-            "lock_file": os.fspath(lock_file),
-            "timeout": timeout,
-            "mode": mode,
-        }
-        self._context: FileLockContext = (ThreadLocalFileContext if thread_local else FileLockContext)(**kwargs)
-
-    def is_thread_local(self) -> bool:
-        """:return: a flag indicating if this lock is thread local or not"""
-        return self._is_thread_local
-
-    @property
-    def lock_file(self) -> str:
-        """:return: path to the lock file"""
-        return self._context.lock_file
-
-    @property
-    def timeout(self) -> float:
-        """
-        :return: the default timeout value, in seconds
-
-        .. versionadded:: 2.0.0
-        """
-        return self._context.timeout
-
-    @timeout.setter
-    def timeout(self, value: float | str) -> None:
-        """
-        Change the default timeout value.
-
-        :param value: the new value, in seconds
-        """
-        self._context.timeout = float(value)
-
-    @abstractmethod
-    def _acquire(self) -> None:
-        """If the file lock could be acquired, self._context.lock_file_fd holds the file descriptor of the lock file."""
-        raise NotImplementedError
-
-    @abstractmethod
-    def _release(self) -> None:
-        """Releases the lock and sets self._context.lock_file_fd to None."""
-        raise NotImplementedError
-
-    @property
-    def is_locked(self) -> bool:
-        """
-
-        :return: A boolean indicating if the lock file is holding the lock currently.
-
-        .. versionchanged:: 2.0.0
-
-            This was previously a method and is now a property.
-        """
-        return self._context.lock_file_fd is not None
-
-    @property
-    def lock_counter(self) -> int:
-        """:return: The number of times this lock has been acquired (but not yet released)."""
-        return self._context.lock_counter
-
-    def acquire(
-        self,
-        timeout: float | None = None,
-        poll_interval: float = 0.05,
-        *,
-        poll_intervall: float | None = None,
-        blocking: bool = True,
-    ) -> AcquireReturnProxy:
-        """
-        Try to acquire the file lock.
-
-        :param timeout: maximum wait time for acquiring the lock, ``None`` means use the default :attr:`~timeout` is and
-         if ``timeout < 0``, there is no timeout and this method will block until the lock could be acquired
-        :param poll_interval: interval of trying to acquire the lock file
-        :param poll_intervall: deprecated, kept for backwards compatibility, use ``poll_interval`` instead
-        :param blocking: defaults to True. If False, function will return immediately if it cannot obtain a lock on the
-         first attempt. Otherwise, this method will block until the timeout expires or the lock is acquired.
-        :raises Timeout: if fails to acquire lock within the timeout period
-        :return: a context object that will unlock the file when the context is exited
-
-        .. code-block:: python
-
-            # You can use this method in the context manager (recommended)
-            with lock.acquire():
-                pass
-
-            # Or use an equivalent try-finally construct:
-            lock.acquire()
-            try:
-                pass
-            finally:
-                lock.release()
-
-        .. versionchanged:: 2.0.0
-
-            This method returns now a *proxy* object instead of *self*,
-            so that it can be used in a with statement without side effects.
-
-        """
-        # Use the default timeout, if no timeout is provided.
-        if timeout is None:
-            timeout = self._context.timeout
-
-        if poll_intervall is not None:
-            msg = "use poll_interval instead of poll_intervall"
-            warnings.warn(msg, DeprecationWarning, stacklevel=2)
-            poll_interval = poll_intervall
-
-        # Increment the number right at the beginning. We can still undo it, if something fails.
-        self._context.lock_counter += 1
-
-        lock_id = id(self)
-        lock_filename = self.lock_file
-        start_time = time.perf_counter()
-        try:
-            while True:
-                if not self.is_locked:
-                    _LOGGER.debug("Attempting to acquire lock %s on %s", lock_id, lock_filename)
-                    self._acquire()
-                if self.is_locked:
-                    _LOGGER.debug("Lock %s acquired on %s", lock_id, lock_filename)
-                    break
-                if blocking is False:
-                    _LOGGER.debug("Failed to immediately acquire lock %s on %s", lock_id, lock_filename)
-                    raise Timeout(lock_filename)  # noqa: TRY301
-                if 0 <= timeout < time.perf_counter() - start_time:
-                    _LOGGER.debug("Timeout on acquiring lock %s on %s", lock_id, lock_filename)
-                    raise Timeout(lock_filename)  # noqa: TRY301
-                msg = "Lock %s not acquired on %s, waiting %s seconds ..."
-                _LOGGER.debug(msg, lock_id, lock_filename, poll_interval)
-                time.sleep(poll_interval)
-        except BaseException:  # Something did go wrong, so decrement the counter.
-            self._context.lock_counter = max(0, self._context.lock_counter - 1)
-            raise
-        return AcquireReturnProxy(lock=self)
-
-    def release(self, force: bool = False) -> None:  # noqa: FBT001, FBT002
-        """
-        Releases the file lock. Please note, that the lock is only completely released, if the lock counter is 0. Also
-        note, that the lock file itself is not automatically deleted.
-
-        :param force: If true, the lock counter is ignored and the lock is released in every case/
-        """
-        if self.is_locked:
-            self._context.lock_counter -= 1
-
-            if self._context.lock_counter == 0 or force:
-                lock_id, lock_filename = id(self), self.lock_file
-
-                _LOGGER.debug("Attempting to release lock %s on %s", lock_id, lock_filename)
-                self._release()
-                self._context.lock_counter = 0
-                _LOGGER.debug("Lock %s released on %s", lock_id, lock_filename)
-
-    def __enter__(self) -> BaseFileLock:
-        """
-        Acquire the lock.
-
-        :return: the lock object
-        """
-        self.acquire()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_value: BaseException | None,
-        traceback: TracebackType | None,
-    ) -> None:
-        """
-        Release the lock.
-
-        :param exc_type: the exception type if raised
-        :param exc_value: the exception value if raised
-        :param traceback: the exception traceback if raised
-        """
-        self.release()
-
-    def __del__(self) -> None:
-        """Called when the lock object is deleted."""
-        self.release(force=True)
-
-
-__all__ = [
-    "BaseFileLock",
-    "AcquireReturnProxy",
-]
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/afmLib.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/afmLib.py
deleted file mode 100644
index 394b901ff5eb149b40c0d9ae425c02d5ad0b5111..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/afmLib.py
+++ /dev/null
@@ -1,438 +0,0 @@
-"""Module for reading and writing AFM (Adobe Font Metrics) files.
-
-Note that this has been designed to read in AFM files generated by Fontographer
-and has not been tested on many other files. In particular, it does not
-implement the whole Adobe AFM specification [#f1]_ but, it should read most
-"common" AFM files.
-
-Here is an example of using `afmLib` to read, modify and write an AFM file:
-
-	>>> from fontTools.afmLib import AFM
-	>>> f = AFM("Tests/afmLib/data/TestAFM.afm")
-	>>>
-	>>> # Accessing a pair gets you the kern value
-	>>> f[("V","A")]
-	-60
-	>>>
-	>>> # Accessing a glyph name gets you metrics
-	>>> f["A"]
-	(65, 668, (8, -25, 660, 666))
-	>>> # (charnum, width, bounding box)
-	>>>
-	>>> # Accessing an attribute gets you metadata
-	>>> f.FontName
-	'TestFont-Regular'
-	>>> f.FamilyName
-	'TestFont'
-	>>> f.Weight
-	'Regular'
-	>>> f.XHeight
-	500
-	>>> f.Ascender
-	750
-	>>>
-	>>> # Attributes and items can also be set
-	>>> f[("A","V")] = -150 # Tighten kerning
-	>>> f.FontName = "TestFont Squished"
-	>>>
-	>>> # And the font written out again (remove the # in front)
-	>>> #f.write("testfont-squished.afm")
-
-.. rubric:: Footnotes
-
-.. [#f1] `Adobe Technote 5004 <https://www.adobe.com/content/dam/acom/en/devnet/font/pdfs/5004.AFM_Spec.pdf>`_,
-   Adobe Font Metrics File Format Specification.
-
-"""
-
-
-import re
-
-# every single line starts with a "word"
-identifierRE = re.compile(r"^([A-Za-z]+).*")
-
-# regular expression to parse char lines
-charRE = re.compile(
-    r"(-?\d+)"  # charnum
-    r"\s*;\s*WX\s+"  # ; WX
-    r"(-?\d+)"  # width
-    r"\s*;\s*N\s+"  # ; N
-    r"([.A-Za-z0-9_]+)"  # charname
-    r"\s*;\s*B\s+"  # ; B
-    r"(-?\d+)"  # left
-    r"\s+"
-    r"(-?\d+)"  # bottom
-    r"\s+"
-    r"(-?\d+)"  # right
-    r"\s+"
-    r"(-?\d+)"  # top
-    r"\s*;\s*"  # ;
-)
-
-# regular expression to parse kerning lines
-kernRE = re.compile(
-    r"([.A-Za-z0-9_]+)"  # leftchar
-    r"\s+"
-    r"([.A-Za-z0-9_]+)"  # rightchar
-    r"\s+"
-    r"(-?\d+)"  # value
-    r"\s*"
-)
-
-# regular expressions to parse composite info lines of the form:
-# Aacute 2 ; PCC A 0 0 ; PCC acute 182 211 ;
-compositeRE = re.compile(
-    r"([.A-Za-z0-9_]+)" r"\s+" r"(\d+)" r"\s*;\s*"  # char name  # number of parts
-)
-componentRE = re.compile(
-    r"PCC\s+"  # PPC
-    r"([.A-Za-z0-9_]+)"  # base char name
-    r"\s+"
-    r"(-?\d+)"  # x offset
-    r"\s+"
-    r"(-?\d+)"  # y offset
-    r"\s*;\s*"
-)
-
-preferredAttributeOrder = [
-    "FontName",
-    "FullName",
-    "FamilyName",
-    "Weight",
-    "ItalicAngle",
-    "IsFixedPitch",
-    "FontBBox",
-    "UnderlinePosition",
-    "UnderlineThickness",
-    "Version",
-    "Notice",
-    "EncodingScheme",
-    "CapHeight",
-    "XHeight",
-    "Ascender",
-    "Descender",
-]
-
-
-class error(Exception):
-    pass
-
-
-class AFM(object):
-
-    _attrs = None
-
-    _keywords = [
-        "StartFontMetrics",
-        "EndFontMetrics",
-        "StartCharMetrics",
-        "EndCharMetrics",
-        "StartKernData",
-        "StartKernPairs",
-        "EndKernPairs",
-        "EndKernData",
-        "StartComposites",
-        "EndComposites",
-    ]
-
-    def __init__(self, path=None):
-        """AFM file reader.
-
-        Instantiating an object with a path name will cause the file to be opened,
-        read, and parsed. Alternatively the path can be left unspecified, and a
-        file can be parsed later with the :meth:`read` method."""
-        self._attrs = {}
-        self._chars = {}
-        self._kerning = {}
-        self._index = {}
-        self._comments = []
-        self._composites = {}
-        if path is not None:
-            self.read(path)
-
-    def read(self, path):
-        """Opens, reads and parses a file."""
-        lines = readlines(path)
-        for line in lines:
-            if not line.strip():
-                continue
-            m = identifierRE.match(line)
-            if m is None:
-                raise error("syntax error in AFM file: " + repr(line))
-
-            pos = m.regs[1][1]
-            word = line[:pos]
-            rest = line[pos:].strip()
-            if word in self._keywords:
-                continue
-            if word == "C":
-                self.parsechar(rest)
-            elif word == "KPX":
-                self.parsekernpair(rest)
-            elif word == "CC":
-                self.parsecomposite(rest)
-            else:
-                self.parseattr(word, rest)
-
-    def parsechar(self, rest):
-        m = charRE.match(rest)
-        if m is None:
-            raise error("syntax error in AFM file: " + repr(rest))
-        things = []
-        for fr, to in m.regs[1:]:
-            things.append(rest[fr:to])
-        charname = things[2]
-        del things[2]
-        charnum, width, l, b, r, t = (int(thing) for thing in things)
-        self._chars[charname] = charnum, width, (l, b, r, t)
-
-    def parsekernpair(self, rest):
-        m = kernRE.match(rest)
-        if m is None:
-            raise error("syntax error in AFM file: " + repr(rest))
-        things = []
-        for fr, to in m.regs[1:]:
-            things.append(rest[fr:to])
-        leftchar, rightchar, value = things
-        value = int(value)
-        self._kerning[(leftchar, rightchar)] = value
-
-    def parseattr(self, word, rest):
-        if word == "FontBBox":
-            l, b, r, t = [int(thing) for thing in rest.split()]
-            self._attrs[word] = l, b, r, t
-        elif word == "Comment":
-            self._comments.append(rest)
-        else:
-            try:
-                value = int(rest)
-            except (ValueError, OverflowError):
-                self._attrs[word] = rest
-            else:
-                self._attrs[word] = value
-
-    def parsecomposite(self, rest):
-        m = compositeRE.match(rest)
-        if m is None:
-            raise error("syntax error in AFM file: " + repr(rest))
-        charname = m.group(1)
-        ncomponents = int(m.group(2))
-        rest = rest[m.regs[0][1] :]
-        components = []
-        while True:
-            m = componentRE.match(rest)
-            if m is None:
-                raise error("syntax error in AFM file: " + repr(rest))
-            basechar = m.group(1)
-            xoffset = int(m.group(2))
-            yoffset = int(m.group(3))
-            components.append((basechar, xoffset, yoffset))
-            rest = rest[m.regs[0][1] :]
-            if not rest:
-                break
-        assert len(components) == ncomponents
-        self._composites[charname] = components
-
-    def write(self, path, sep="\r"):
-        """Writes out an AFM font to the given path."""
-        import time
-
-        lines = [
-            "StartFontMetrics 2.0",
-            "Comment Generated by afmLib; at %s"
-            % (time.strftime("%m/%d/%Y %H:%M:%S", time.localtime(time.time()))),
-        ]
-
-        # write comments, assuming (possibly wrongly!) they should
-        # all appear at the top
-        for comment in self._comments:
-            lines.append("Comment " + comment)
-
-        # write attributes, first the ones we know about, in
-        # a preferred order
-        attrs = self._attrs
-        for attr in preferredAttributeOrder:
-            if attr in attrs:
-                value = attrs[attr]
-                if attr == "FontBBox":
-                    value = "%s %s %s %s" % value
-                lines.append(attr + " " + str(value))
-        # then write the attributes we don't know about,
-        # in alphabetical order
-        items = sorted(attrs.items())
-        for attr, value in items:
-            if attr in preferredAttributeOrder:
-                continue
-            lines.append(attr + " " + str(value))
-
-        # write char metrics
-        lines.append("StartCharMetrics " + repr(len(self._chars)))
-        items = [
-            (charnum, (charname, width, box))
-            for charname, (charnum, width, box) in self._chars.items()
-        ]
-
-        def myKey(a):
-            """Custom key function to make sure unencoded chars (-1)
-            end up at the end of the list after sorting."""
-            if a[0] == -1:
-                a = (0xFFFF,) + a[1:]  # 0xffff is an arbitrary large number
-            return a
-
-        items.sort(key=myKey)
-
-        for charnum, (charname, width, (l, b, r, t)) in items:
-            lines.append(
-                "C %d ; WX %d ; N %s ; B %d %d %d %d ;"
-                % (charnum, width, charname, l, b, r, t)
-            )
-        lines.append("EndCharMetrics")
-
-        # write kerning info
-        lines.append("StartKernData")
-        lines.append("StartKernPairs " + repr(len(self._kerning)))
-        items = sorted(self._kerning.items())
-        for (leftchar, rightchar), value in items:
-            lines.append("KPX %s %s %d" % (leftchar, rightchar, value))
-        lines.append("EndKernPairs")
-        lines.append("EndKernData")
-
-        if self._composites:
-            composites = sorted(self._composites.items())
-            lines.append("StartComposites %s" % len(self._composites))
-            for charname, components in composites:
-                line = "CC %s %s ;" % (charname, len(components))
-                for basechar, xoffset, yoffset in components:
-                    line = line + " PCC %s %s %s ;" % (basechar, xoffset, yoffset)
-                lines.append(line)
-            lines.append("EndComposites")
-
-        lines.append("EndFontMetrics")
-
-        writelines(path, lines, sep)
-
-    def has_kernpair(self, pair):
-        """Returns `True` if the given glyph pair (specified as a tuple) exists
-        in the kerning dictionary."""
-        return pair in self._kerning
-
-    def kernpairs(self):
-        """Returns a list of all kern pairs in the kerning dictionary."""
-        return list(self._kerning.keys())
-
-    def has_char(self, char):
-        """Returns `True` if the given glyph exists in the font."""
-        return char in self._chars
-
-    def chars(self):
-        """Returns a list of all glyph names in the font."""
-        return list(self._chars.keys())
-
-    def comments(self):
-        """Returns all comments from the file."""
-        return self._comments
-
-    def addComment(self, comment):
-        """Adds a new comment to the file."""
-        self._comments.append(comment)
-
-    def addComposite(self, glyphName, components):
-        """Specifies that the glyph `glyphName` is made up of the given components.
-        The components list should be of the following form::
-
-                [
-                        (glyphname, xOffset, yOffset),
-                        ...
-                ]
-
-        """
-        self._composites[glyphName] = components
-
-    def __getattr__(self, attr):
-        if attr in self._attrs:
-            return self._attrs[attr]
-        else:
-            raise AttributeError(attr)
-
-    def __setattr__(self, attr, value):
-        # all attrs *not* starting with "_" are consider to be AFM keywords
-        if attr[:1] == "_":
-            self.__dict__[attr] = value
-        else:
-            self._attrs[attr] = value
-
-    def __delattr__(self, attr):
-        # all attrs *not* starting with "_" are consider to be AFM keywords
-        if attr[:1] == "_":
-            try:
-                del self.__dict__[attr]
-            except KeyError:
-                raise AttributeError(attr)
-        else:
-            try:
-                del self._attrs[attr]
-            except KeyError:
-                raise AttributeError(attr)
-
-    def __getitem__(self, key):
-        if isinstance(key, tuple):
-            # key is a tuple, return the kernpair
-            return self._kerning[key]
-        else:
-            # return the metrics instead
-            return self._chars[key]
-
-    def __setitem__(self, key, value):
-        if isinstance(key, tuple):
-            # key is a tuple, set kernpair
-            self._kerning[key] = value
-        else:
-            # set char metrics
-            self._chars[key] = value
-
-    def __delitem__(self, key):
-        if isinstance(key, tuple):
-            # key is a tuple, del kernpair
-            del self._kerning[key]
-        else:
-            # del char metrics
-            del self._chars[key]
-
-    def __repr__(self):
-        if hasattr(self, "FullName"):
-            return "<AFM object for %s>" % self.FullName
-        else:
-            return "<AFM object at %x>" % id(self)
-
-
-def readlines(path):
-    with open(path, "r", encoding="ascii") as f:
-        data = f.read()
-    return data.splitlines()
-
-
-def writelines(path, lines, sep="\r"):
-    with open(path, "w", encoding="ascii", newline=sep) as f:
-        f.write("\n".join(lines) + "\n")
-
-
-if __name__ == "__main__":
-    import EasyDialogs
-
-    path = EasyDialogs.AskFileForOpen()
-    if path:
-        afm = AFM(path)
-        char = "A"
-        if afm.has_char(char):
-            print(afm[char])  # print charnum, width and boundingbox
-        pair = ("A", "V")
-        if afm.has_kernpair(pair):
-            print(afm[pair])  # print kerning value for pair
-        print(afm.Version)  # various other afm entries have become attributes
-        print(afm.Weight)
-        # afm.comments() returns a list of all Comment lines found in the AFM
-        print(afm.comments())
-        # print afm.chars()
-        # print afm.kernpairs()
-        print(afm)
-        afm.write(path + ".muck")
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_g_c_i_d.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_g_c_i_d.py
deleted file mode 100644
index 2e746c846fa14800cb7de93969984dac36678e4e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_g_c_i_d.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6gcid.html
-class table__g_c_i_d(BaseTTXConverter):
-    pass
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/instancer/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/instancer/__init__.py
deleted file mode 100644
index a8663ec42247a7967675755090b34cec2e9e2cd8..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/varLib/instancer/__init__.py
+++ /dev/null
@@ -1,1508 +0,0 @@
-""" Partially instantiate a variable font.
-
-The module exports an `instantiateVariableFont` function and CLI that allow to
-create full instances (i.e. static fonts) from variable fonts, as well as "partial"
-variable fonts that only contain a subset of the original variation space.
-
-For example, if you wish to pin the width axis to a given location while also
-restricting the weight axis to 400..700 range, you can do::
-
-    $ fonttools varLib.instancer ./NotoSans-VF.ttf wdth=85 wght=400:700
-
-See `fonttools varLib.instancer --help` for more info on the CLI options.
-
-The module's entry point is the `instantiateVariableFont` function, which takes
-a TTFont object and a dict specifying either axis coodinates or (min, max) ranges,
-and returns a new TTFont representing either a partial VF, or full instance if all
-the VF axes were given an explicit coordinate.
-
-E.g. here's how to pin the wght axis at a given location in a wght+wdth variable
-font, keeping only the deltas associated with the wdth axis::
-
-| >>> from fontTools import ttLib
-| >>> from fontTools.varLib import instancer
-| >>> varfont = ttLib.TTFont("path/to/MyVariableFont.ttf")
-| >>> [a.axisTag for a in varfont["fvar"].axes]  # the varfont's current axes
-| ['wght', 'wdth']
-| >>> partial = instancer.instantiateVariableFont(varfont, {"wght": 300})
-| >>> [a.axisTag for a in partial["fvar"].axes]  # axes left after pinning 'wght'
-| ['wdth']
-
-If the input location specifies all the axes, the resulting instance is no longer
-'variable' (same as using fontools varLib.mutator):
-
-| >>> instance = instancer.instantiateVariableFont(
-| ...     varfont, {"wght": 700, "wdth": 67.5}
-| ... )
-| >>> "fvar" not in instance
-| True
-
-If one just want to drop an axis at the default location, without knowing in
-advance what the default value for that axis is, one can pass a `None` value:
-
-| >>> instance = instancer.instantiateVariableFont(varfont, {"wght": None})
-| >>> len(varfont["fvar"].axes)
-| 1
-
-From the console script, this is equivalent to passing `wght=drop` as input.
-
-This module is similar to fontTools.varLib.mutator, which it's intended to supersede.
-Note that, unlike varLib.mutator, when an axis is not mentioned in the input
-location, the varLib.instancer will keep the axis and the corresponding deltas,
-whereas mutator implicitly drops the axis at its default coordinate.
-
-The module supports all the following "levels" of instancing, which can of
-course be combined:
-
-L1
-    dropping one or more axes while leaving the default tables unmodified;
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": None})
-
-L2
-    dropping one or more axes while pinning them at non-default locations;
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": 700})
-
-L3
-    restricting the range of variation of one or more axes, by setting either
-    a new minimum or maximum, potentially -- though not necessarily -- dropping
-    entire regions of variations that fall completely outside this new range.
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": (100, 300)})
-
-L4
-    moving the default location of an axis, by specifying (min,defalt,max) values:
-
-    | >>> font = instancer.instantiateVariableFont(varfont, {"wght": (100, 300, 700)})
-
-Currently only TrueType-flavored variable fonts (i.e. containing 'glyf' table)
-are supported, but support for CFF2 variable fonts will be added soon.
-
-The discussion and implementation of these features are tracked at
-https://github.com/fonttools/fonttools/issues/1537
-"""
-from fontTools.misc.fixedTools import (
-    floatToFixedToFloat,
-    strToFixedToFloat,
-    otRound,
-)
-from fontTools.varLib.models import supportScalar, normalizeValue, piecewiseLinearMap
-from fontTools.ttLib import TTFont
-from fontTools.ttLib.tables.TupleVariation import TupleVariation
-from fontTools.ttLib.tables import _g_l_y_f
-from fontTools import varLib
-
-# we import the `subset` module because we use the `prune_lookups` method on the GSUB
-# table class, and that method is only defined dynamically upon importing `subset`
-from fontTools import subset  # noqa: F401
-from fontTools.varLib import builder
-from fontTools.varLib.mvar import MVAR_ENTRIES
-from fontTools.varLib.merger import MutatorMerger
-from fontTools.varLib.instancer import names
-from .featureVars import instantiateFeatureVariations
-from fontTools.misc.cliTools import makeOutputFileName
-from fontTools.varLib.instancer import solver
-import collections
-import dataclasses
-from copy import deepcopy
-from enum import IntEnum
-import logging
-import os
-import re
-from typing import Dict, Iterable, Mapping, Optional, Sequence, Tuple, Union
-import warnings
-
-
-log = logging.getLogger("fontTools.varLib.instancer")
-
-
-def AxisRange(minimum, maximum):
-    warnings.warn(
-        "AxisRange is deprecated; use AxisTriple instead",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return AxisTriple(minimum, None, maximum)
-
-
-def NormalizedAxisRange(minimum, maximum):
-    warnings.warn(
-        "NormalizedAxisRange is deprecated; use AxisTriple instead",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return NormalizedAxisTriple(minimum, None, maximum)
-
-
-@dataclasses.dataclass(frozen=True, order=True, repr=False)
-class AxisTriple(Sequence):
-    """A triple of (min, default, max) axis values.
-
-    The default value can be None, in which case the limitRangeAndPopulateDefault()
-    method can be used to fill in the missing default value based on the fvar axis
-    default.
-    """
-
-    minimum: float
-    default: Optional[float]  # if None, filled with by limitRangeAndPopulateDefault
-    maximum: float
-
-    def __post_init__(self):
-        if self.default is None and self.minimum == self.maximum:
-            object.__setattr__(self, "default", self.minimum)
-        if not (
-            (self.minimum <= self.default <= self.maximum)
-            if self.default is not None
-            else (self.minimum <= self.maximum)
-        ):
-            raise ValueError(
-                f"{type(self).__name__} minimum ({self.minimum}) must be <= default "
-                f"({self.default}) which must be <= maximum ({self.maximum})"
-            )
-
-    def __getitem__(self, i):
-        fields = dataclasses.fields(self)
-        return getattr(self, fields[i].name)
-
-    def __len__(self):
-        return len(dataclasses.fields(self))
-
-    def _replace(self, **kwargs):
-        return dataclasses.replace(self, **kwargs)
-
-    def __repr__(self):
-        return (
-            f"({', '.join(format(v, 'g') if v is not None else 'None' for v in self)})"
-        )
-
-    @classmethod
-    def expand(
-        cls,
-        v: Union[
-            "AxisTriple",
-            float,  # pin axis at single value, same as min==default==max
-            Tuple[float, float],  # (min, max), restrict axis and keep default
-            Tuple[float, float, float],  # (min, default, max)
-        ],
-    ) -> "AxisTriple":
-        """Convert a single value or a tuple into an AxisTriple.
-
-        If the input is a single value, it is interpreted as a pin at that value.
-        If the input is a tuple, it is interpreted as (min, max) or (min, default, max).
-        """
-        if isinstance(v, cls):
-            return v
-        if isinstance(v, (int, float)):
-            return cls(v, v, v)
-        try:
-            n = len(v)
-        except TypeError as e:
-            raise ValueError(
-                f"expected float, 2- or 3-tuple of floats; got {type(v)}: {v!r}"
-            ) from e
-        default = None
-        if n == 2:
-            minimum, maximum = v
-        elif n >= 3:
-            return cls(*v)
-        else:
-            raise ValueError(f"expected sequence of 2 or 3; got {n}: {v!r}")
-        return cls(minimum, default, maximum)
-
-    def limitRangeAndPopulateDefault(self, fvarTriple) -> "AxisTriple":
-        """Return a new AxisTriple with the default value filled in.
-
-        Set default to fvar axis default if the latter is within the min/max range,
-        otherwise set default to the min or max value, whichever is closer to the
-        fvar axis default.
-        If the default value is already set, return self.
-        """
-        minimum = self.minimum
-        maximum = self.maximum
-        default = self.default
-        if default is None:
-            default = fvarTriple[1]
-
-        minimum = max(self.minimum, fvarTriple[0])
-        maximum = max(self.maximum, fvarTriple[0])
-        minimum = min(minimum, fvarTriple[2])
-        maximum = min(maximum, fvarTriple[2])
-        default = max(minimum, min(maximum, default))
-
-        return AxisTriple(minimum, default, maximum)
-
-
-@dataclasses.dataclass(frozen=True, order=True, repr=False)
-class NormalizedAxisTriple(AxisTriple):
-    """A triple of (min, default, max) normalized axis values."""
-
-    minimum: float
-    default: float
-    maximum: float
-
-    def __post_init__(self):
-        if self.default is None:
-            object.__setattr__(self, "default", max(self.minimum, min(self.maximum, 0)))
-        if not (-1.0 <= self.minimum <= self.default <= self.maximum <= 1.0):
-            raise ValueError(
-                "Normalized axis values not in -1..+1 range; got "
-                f"minimum={self.minimum:g}, default={self.default:g}, maximum={self.maximum:g})"
-            )
-
-
-@dataclasses.dataclass(frozen=True, order=True, repr=False)
-class NormalizedAxisTripleAndDistances(AxisTriple):
-    """A triple of (min, default, max) normalized axis values,
-    with distances between min and default, and default and max,
-    in the *pre-normalized* space."""
-
-    minimum: float
-    default: float
-    maximum: float
-    distanceNegative: Optional[float] = 1
-    distancePositive: Optional[float] = 1
-
-    def __post_init__(self):
-        if self.default is None:
-            object.__setattr__(self, "default", max(self.minimum, min(self.maximum, 0)))
-        if not (-1.0 <= self.minimum <= self.default <= self.maximum <= 1.0):
-            raise ValueError(
-                "Normalized axis values not in -1..+1 range; got "
-                f"minimum={self.minimum:g}, default={self.default:g}, maximum={self.maximum:g})"
-            )
-
-    def reverse_negate(self):
-        v = self
-        return self.__class__(-v[2], -v[1], -v[0], v[4], v[3])
-
-    def renormalizeValue(self, v, extrapolate=True):
-        """Renormalizes a normalized value v to the range of this axis,
-        considering the pre-normalized distances as well as the new
-        axis limits."""
-
-        lower, default, upper, distanceNegative, distancePositive = self
-        assert lower <= default <= upper
-
-        if not extrapolate:
-            v = max(lower, min(upper, v))
-
-        if v == default:
-            return 0
-
-        if default < 0:
-            return -self.reverse_negate().renormalizeValue(-v, extrapolate=extrapolate)
-
-        # default >= 0 and v != default
-
-        if v > default:
-            return (v - default) / (upper - default)
-
-        # v < default
-
-        if lower >= 0:
-            return (v - default) / (default - lower)
-
-        # lower < 0 and v < default
-
-        totalDistance = distanceNegative * -lower + distancePositive * default
-
-        if v >= 0:
-            vDistance = (default - v) * distancePositive
-        else:
-            vDistance = -v * distanceNegative + distancePositive * default
-
-        return -vDistance / totalDistance
-
-
-class _BaseAxisLimits(Mapping[str, AxisTriple]):
-    def __getitem__(self, key: str) -> AxisTriple:
-        return self._data[key]
-
-    def __iter__(self) -> Iterable[str]:
-        return iter(self._data)
-
-    def __len__(self) -> int:
-        return len(self._data)
-
-    def __repr__(self) -> str:
-        return f"{type(self).__name__}({self._data!r})"
-
-    def __str__(self) -> str:
-        return str(self._data)
-
-    def defaultLocation(self) -> Dict[str, float]:
-        """Return a dict of default axis values."""
-        return {k: v.default for k, v in self.items()}
-
-    def pinnedLocation(self) -> Dict[str, float]:
-        """Return a location dict with only the pinned axes."""
-        return {k: v.default for k, v in self.items() if v.minimum == v.maximum}
-
-
-class AxisLimits(_BaseAxisLimits):
-    """Maps axis tags (str) to AxisTriple values."""
-
-    def __init__(self, *args, **kwargs):
-        self._data = data = {}
-        for k, v in dict(*args, **kwargs).items():
-            if v is None:
-                # will be filled in by limitAxesAndPopulateDefaults
-                data[k] = v
-            else:
-                try:
-                    triple = AxisTriple.expand(v)
-                except ValueError as e:
-                    raise ValueError(f"Invalid axis limits for {k!r}: {v!r}") from e
-                data[k] = triple
-
-    def limitAxesAndPopulateDefaults(self, varfont) -> "AxisLimits":
-        """Return a new AxisLimits with defaults filled in from fvar table.
-
-        If all axis limits already have defaults, return self.
-        """
-        fvar = varfont["fvar"]
-        fvarTriples = {
-            a.axisTag: (a.minValue, a.defaultValue, a.maxValue) for a in fvar.axes
-        }
-        newLimits = {}
-        for axisTag, triple in self.items():
-            fvarTriple = fvarTriples[axisTag]
-            default = fvarTriple[1]
-            if triple is None:
-                newLimits[axisTag] = AxisTriple(default, default, default)
-            else:
-                newLimits[axisTag] = triple.limitRangeAndPopulateDefault(fvarTriple)
-        return type(self)(newLimits)
-
-    def normalize(self, varfont, usingAvar=True) -> "NormalizedAxisLimits":
-        """Return a new NormalizedAxisLimits with normalized -1..0..+1 values.
-
-        If usingAvar is True, the avar table is used to warp the default normalization.
-        """
-        fvar = varfont["fvar"]
-        badLimits = set(self.keys()).difference(a.axisTag for a in fvar.axes)
-        if badLimits:
-            raise ValueError("Cannot limit: {} not present in fvar".format(badLimits))
-
-        axes = {
-            a.axisTag: (a.minValue, a.defaultValue, a.maxValue)
-            for a in fvar.axes
-            if a.axisTag in self
-        }
-
-        avarSegments = {}
-        if usingAvar and "avar" in varfont:
-            avarSegments = varfont["avar"].segments
-
-        normalizedLimits = {}
-
-        for axis_tag, triple in axes.items():
-            distanceNegative = triple[1] - triple[0]
-            distancePositive = triple[2] - triple[1]
-
-            if self[axis_tag] is None:
-                normalizedLimits[axis_tag] = NormalizedAxisTripleAndDistances(
-                    0, 0, 0, distanceNegative, distancePositive
-                )
-                continue
-
-            minV, defaultV, maxV = self[axis_tag]
-
-            if defaultV is None:
-                defaultV = triple[1]
-
-            avarMapping = avarSegments.get(axis_tag, None)
-            normalizedLimits[axis_tag] = NormalizedAxisTripleAndDistances(
-                *(normalize(v, triple, avarMapping) for v in (minV, defaultV, maxV)),
-                distanceNegative,
-                distancePositive,
-            )
-
-        return NormalizedAxisLimits(normalizedLimits)
-
-
-class NormalizedAxisLimits(_BaseAxisLimits):
-    """Maps axis tags (str) to NormalizedAxisTriple values."""
-
-    def __init__(self, *args, **kwargs):
-        self._data = data = {}
-        for k, v in dict(*args, **kwargs).items():
-            try:
-                triple = NormalizedAxisTripleAndDistances.expand(v)
-            except ValueError as e:
-                raise ValueError(f"Invalid axis limits for {k!r}: {v!r}") from e
-            data[k] = triple
-
-
-class OverlapMode(IntEnum):
-    KEEP_AND_DONT_SET_FLAGS = 0
-    KEEP_AND_SET_FLAGS = 1
-    REMOVE = 2
-    REMOVE_AND_IGNORE_ERRORS = 3
-
-
-def instantiateTupleVariationStore(
-    variations, axisLimits, origCoords=None, endPts=None
-):
-    """Instantiate TupleVariation list at the given location, or limit axes' min/max.
-
-    The 'variations' list of TupleVariation objects is modified in-place.
-    The 'axisLimits' (dict) maps axis tags (str) to NormalizedAxisTriple namedtuples
-    specifying (minimum, default, maximum) in the -1,0,+1 normalized space. Pinned axes
-    have minimum == default == maximum.
-
-    A 'full' instance (i.e. static font) is produced when all the axes are pinned to
-    single coordinates; a 'partial' instance (i.e. a less variable font) is produced
-    when some of the axes are omitted, or restricted with a new range.
-
-    Tuples that do not participate are kept as they are. Those that have 0 influence
-    at the given location are removed from the variation store.
-    Those that are fully instantiated (i.e. all their axes are being pinned) are also
-    removed from the variation store, their scaled deltas accummulated and returned, so
-    that they can be added by the caller to the default instance's coordinates.
-    Tuples that are only partially instantiated (i.e. not all the axes that they
-    participate in are being pinned) are kept in the store, and their deltas multiplied
-    by the scalar support of the axes to be pinned at the desired location.
-
-    Args:
-        variations: List[TupleVariation] from either 'gvar' or 'cvar'.
-        axisLimits: NormalizedAxisLimits: map from axis tags to (min, default, max)
-            normalized coordinates for the full or partial instance.
-        origCoords: GlyphCoordinates: default instance's coordinates for computing 'gvar'
-            inferred points (cf. table__g_l_y_f._getCoordinatesAndControls).
-        endPts: List[int]: indices of contour end points, for inferring 'gvar' deltas.
-
-    Returns:
-        List[float]: the overall delta adjustment after applicable deltas were summed.
-    """
-
-    newVariations = changeTupleVariationsAxisLimits(variations, axisLimits)
-
-    mergedVariations = collections.OrderedDict()
-    for var in newVariations:
-        # compute inferred deltas only for gvar ('origCoords' is None for cvar)
-        if origCoords is not None:
-            var.calcInferredDeltas(origCoords, endPts)
-
-        # merge TupleVariations with overlapping "tents"
-        axes = frozenset(var.axes.items())
-        if axes in mergedVariations:
-            mergedVariations[axes] += var
-        else:
-            mergedVariations[axes] = var
-
-    # drop TupleVariation if all axes have been pinned (var.axes.items() is empty);
-    # its deltas will be added to the default instance's coordinates
-    defaultVar = mergedVariations.pop(frozenset(), None)
-
-    for var in mergedVariations.values():
-        var.roundDeltas()
-    variations[:] = list(mergedVariations.values())
-
-    return defaultVar.coordinates if defaultVar is not None else []
-
-
-def changeTupleVariationsAxisLimits(variations, axisLimits):
-    for axisTag, axisLimit in sorted(axisLimits.items()):
-        newVariations = []
-        for var in variations:
-            newVariations.extend(changeTupleVariationAxisLimit(var, axisTag, axisLimit))
-        variations = newVariations
-    return variations
-
-
-def changeTupleVariationAxisLimit(var, axisTag, axisLimit):
-    assert isinstance(axisLimit, NormalizedAxisTripleAndDistances)
-
-    # Skip when current axis is missing (i.e. doesn't participate),
-    lower, peak, upper = var.axes.get(axisTag, (-1, 0, 1))
-    if peak == 0:
-        return [var]
-    # Drop if the var 'tent' isn't well-formed
-    if not (lower <= peak <= upper) or (lower < 0 and upper > 0):
-        return []
-
-    if axisTag not in var.axes:
-        return [var]
-
-    tent = var.axes[axisTag]
-
-    solutions = solver.rebaseTent(tent, axisLimit)
-
-    out = []
-    for scalar, tent in solutions:
-        newVar = (
-            TupleVariation(var.axes, var.coordinates) if len(solutions) > 1 else var
-        )
-        if tent is None:
-            newVar.axes.pop(axisTag)
-        else:
-            assert tent[1] != 0, tent
-            newVar.axes[axisTag] = tent
-        newVar *= scalar
-        out.append(newVar)
-
-    return out
-
-
-def _instantiateGvarGlyph(
-    glyphname, glyf, gvar, hMetrics, vMetrics, axisLimits, optimize=True
-):
-    coordinates, ctrl = glyf._getCoordinatesAndControls(glyphname, hMetrics, vMetrics)
-    endPts = ctrl.endPts
-
-    # Not every glyph may have variations
-    tupleVarStore = gvar.variations.get(glyphname)
-
-    if tupleVarStore:
-        defaultDeltas = instantiateTupleVariationStore(
-            tupleVarStore, axisLimits, coordinates, endPts
-        )
-
-        if defaultDeltas:
-            coordinates += _g_l_y_f.GlyphCoordinates(defaultDeltas)
-
-    glyph = glyf[glyphname]
-    if glyph.isVarComposite():
-        for component in glyph.components:
-            newLocation = {}
-            for tag, loc in component.location.items():
-                if tag not in axisLimits:
-                    newLocation[tag] = loc
-                    continue
-                if component.flags & _g_l_y_f.VarComponentFlags.AXES_HAVE_VARIATION:
-                    raise NotImplementedError(
-                        "Instancing accross VarComposite axes with variation is not supported."
-                    )
-                limits = axisLimits[tag]
-                loc = limits.renormalizeValue(loc, extrapolate=False)
-                newLocation[tag] = loc
-            component.location = newLocation
-
-    # _setCoordinates also sets the hmtx/vmtx advance widths and sidebearings from
-    # the four phantom points and glyph bounding boxes.
-    # We call it unconditionally even if a glyph has no variations or no deltas are
-    # applied at this location, in case the glyph's xMin and in turn its sidebearing
-    # have changed. E.g. a composite glyph has no deltas for the component's (x, y)
-    # offset nor for the 4 phantom points (e.g. it's monospaced). Thus its entry in
-    # gvar table is empty; however, the composite's base glyph may have deltas
-    # applied, hence the composite's bbox and left/top sidebearings may need updating
-    # in the instanced font.
-    glyf._setCoordinates(glyphname, coordinates, hMetrics, vMetrics)
-
-    if not tupleVarStore:
-        if glyphname in gvar.variations:
-            del gvar.variations[glyphname]
-        return
-
-    if optimize:
-        isComposite = glyf[glyphname].isComposite()
-        for var in tupleVarStore:
-            var.optimize(coordinates, endPts, isComposite)
-
-
-def instantiateGvarGlyph(varfont, glyphname, axisLimits, optimize=True):
-    """Remove?
-    https://github.com/fonttools/fonttools/pull/2266"""
-    gvar = varfont["gvar"]
-    glyf = varfont["glyf"]
-    hMetrics = varfont["hmtx"].metrics
-    vMetrics = getattr(varfont.get("vmtx"), "metrics", None)
-    _instantiateGvarGlyph(
-        glyphname, glyf, gvar, hMetrics, vMetrics, axisLimits, optimize=optimize
-    )
-
-
-def instantiateGvar(varfont, axisLimits, optimize=True):
-    log.info("Instantiating glyf/gvar tables")
-
-    gvar = varfont["gvar"]
-    glyf = varfont["glyf"]
-    hMetrics = varfont["hmtx"].metrics
-    vMetrics = getattr(varfont.get("vmtx"), "metrics", None)
-    # Get list of glyph names sorted by component depth.
-    # If a composite glyph is processed before its base glyph, the bounds may
-    # be calculated incorrectly because deltas haven't been applied to the
-    # base glyph yet.
-    glyphnames = sorted(
-        glyf.glyphOrder,
-        key=lambda name: (
-            glyf[name].getCompositeMaxpValues(glyf).maxComponentDepth
-            if glyf[name].isComposite() or glyf[name].isVarComposite()
-            else 0,
-            name,
-        ),
-    )
-    for glyphname in glyphnames:
-        _instantiateGvarGlyph(
-            glyphname, glyf, gvar, hMetrics, vMetrics, axisLimits, optimize=optimize
-        )
-
-    if not gvar.variations:
-        del varfont["gvar"]
-
-
-def setCvarDeltas(cvt, deltas):
-    for i, delta in enumerate(deltas):
-        if delta:
-            cvt[i] += otRound(delta)
-
-
-def instantiateCvar(varfont, axisLimits):
-    log.info("Instantiating cvt/cvar tables")
-
-    cvar = varfont["cvar"]
-
-    defaultDeltas = instantiateTupleVariationStore(cvar.variations, axisLimits)
-
-    if defaultDeltas:
-        setCvarDeltas(varfont["cvt "], defaultDeltas)
-
-    if not cvar.variations:
-        del varfont["cvar"]
-
-
-def setMvarDeltas(varfont, deltas):
-    mvar = varfont["MVAR"].table
-    records = mvar.ValueRecord
-    for rec in records:
-        mvarTag = rec.ValueTag
-        if mvarTag not in MVAR_ENTRIES:
-            continue
-        tableTag, itemName = MVAR_ENTRIES[mvarTag]
-        delta = deltas[rec.VarIdx]
-        if delta != 0:
-            setattr(
-                varfont[tableTag],
-                itemName,
-                getattr(varfont[tableTag], itemName) + otRound(delta),
-            )
-
-
-def instantiateMVAR(varfont, axisLimits):
-    log.info("Instantiating MVAR table")
-
-    mvar = varfont["MVAR"].table
-    fvarAxes = varfont["fvar"].axes
-    varStore = mvar.VarStore
-    defaultDeltas = instantiateItemVariationStore(varStore, fvarAxes, axisLimits)
-    setMvarDeltas(varfont, defaultDeltas)
-
-    if varStore.VarRegionList.Region:
-        varIndexMapping = varStore.optimize()
-        for rec in mvar.ValueRecord:
-            rec.VarIdx = varIndexMapping[rec.VarIdx]
-    else:
-        del varfont["MVAR"]
-
-
-def _remapVarIdxMap(table, attrName, varIndexMapping, glyphOrder):
-    oldMapping = getattr(table, attrName).mapping
-    newMapping = [varIndexMapping[oldMapping[glyphName]] for glyphName in glyphOrder]
-    setattr(table, attrName, builder.buildVarIdxMap(newMapping, glyphOrder))
-
-
-# TODO(anthrotype) Add support for HVAR/VVAR in CFF2
-def _instantiateVHVAR(varfont, axisLimits, tableFields):
-    location = axisLimits.pinnedLocation()
-    tableTag = tableFields.tableTag
-    fvarAxes = varfont["fvar"].axes
-    # Deltas from gvar table have already been applied to the hmtx/vmtx. For full
-    # instances (i.e. all axes pinned), we can simply drop HVAR/VVAR and return
-    if set(location).issuperset(axis.axisTag for axis in fvarAxes):
-        log.info("Dropping %s table", tableTag)
-        del varfont[tableTag]
-        return
-
-    log.info("Instantiating %s table", tableTag)
-    vhvar = varfont[tableTag].table
-    varStore = vhvar.VarStore
-    # since deltas were already applied, the return value here is ignored
-    instantiateItemVariationStore(varStore, fvarAxes, axisLimits)
-
-    if varStore.VarRegionList.Region:
-        # Only re-optimize VarStore if the HVAR/VVAR already uses indirect AdvWidthMap
-        # or AdvHeightMap. If a direct, implicit glyphID->VariationIndex mapping is
-        # used for advances, skip re-optimizing and maintain original VariationIndex.
-        if getattr(vhvar, tableFields.advMapping):
-            varIndexMapping = varStore.optimize(use_NO_VARIATION_INDEX=False)
-            glyphOrder = varfont.getGlyphOrder()
-            _remapVarIdxMap(vhvar, tableFields.advMapping, varIndexMapping, glyphOrder)
-            if getattr(vhvar, tableFields.sb1):  # left or top sidebearings
-                _remapVarIdxMap(vhvar, tableFields.sb1, varIndexMapping, glyphOrder)
-            if getattr(vhvar, tableFields.sb2):  # right or bottom sidebearings
-                _remapVarIdxMap(vhvar, tableFields.sb2, varIndexMapping, glyphOrder)
-            if tableTag == "VVAR" and getattr(vhvar, tableFields.vOrigMapping):
-                _remapVarIdxMap(
-                    vhvar, tableFields.vOrigMapping, varIndexMapping, glyphOrder
-                )
-
-
-def instantiateHVAR(varfont, axisLimits):
-    return _instantiateVHVAR(varfont, axisLimits, varLib.HVAR_FIELDS)
-
-
-def instantiateVVAR(varfont, axisLimits):
-    return _instantiateVHVAR(varfont, axisLimits, varLib.VVAR_FIELDS)
-
-
-class _TupleVarStoreAdapter(object):
-    def __init__(self, regions, axisOrder, tupleVarData, itemCounts):
-        self.regions = regions
-        self.axisOrder = axisOrder
-        self.tupleVarData = tupleVarData
-        self.itemCounts = itemCounts
-
-    @classmethod
-    def fromItemVarStore(cls, itemVarStore, fvarAxes):
-        axisOrder = [axis.axisTag for axis in fvarAxes]
-        regions = [
-            region.get_support(fvarAxes) for region in itemVarStore.VarRegionList.Region
-        ]
-        tupleVarData = []
-        itemCounts = []
-        for varData in itemVarStore.VarData:
-            variations = []
-            varDataRegions = (regions[i] for i in varData.VarRegionIndex)
-            for axes, coordinates in zip(varDataRegions, zip(*varData.Item)):
-                variations.append(TupleVariation(axes, list(coordinates)))
-            tupleVarData.append(variations)
-            itemCounts.append(varData.ItemCount)
-        return cls(regions, axisOrder, tupleVarData, itemCounts)
-
-    def rebuildRegions(self):
-        # Collect the set of all unique region axes from the current TupleVariations.
-        # We use an OrderedDict to de-duplicate regions while keeping the order.
-        uniqueRegions = collections.OrderedDict.fromkeys(
-            (
-                frozenset(var.axes.items())
-                for variations in self.tupleVarData
-                for var in variations
-            )
-        )
-        # Maintain the original order for the regions that pre-existed, appending
-        # the new regions at the end of the region list.
-        newRegions = []
-        for region in self.regions:
-            regionAxes = frozenset(region.items())
-            if regionAxes in uniqueRegions:
-                newRegions.append(region)
-                del uniqueRegions[regionAxes]
-        if uniqueRegions:
-            newRegions.extend(dict(region) for region in uniqueRegions)
-        self.regions = newRegions
-
-    def instantiate(self, axisLimits):
-        defaultDeltaArray = []
-        for variations, itemCount in zip(self.tupleVarData, self.itemCounts):
-            defaultDeltas = instantiateTupleVariationStore(variations, axisLimits)
-            if not defaultDeltas:
-                defaultDeltas = [0] * itemCount
-            defaultDeltaArray.append(defaultDeltas)
-
-        # rebuild regions whose axes were dropped or limited
-        self.rebuildRegions()
-
-        pinnedAxes = set(axisLimits.pinnedLocation())
-        self.axisOrder = [
-            axisTag for axisTag in self.axisOrder if axisTag not in pinnedAxes
-        ]
-
-        return defaultDeltaArray
-
-    def asItemVarStore(self):
-        regionOrder = [frozenset(axes.items()) for axes in self.regions]
-        varDatas = []
-        for variations, itemCount in zip(self.tupleVarData, self.itemCounts):
-            if variations:
-                assert len(variations[0].coordinates) == itemCount
-                varRegionIndices = [
-                    regionOrder.index(frozenset(var.axes.items())) for var in variations
-                ]
-                varDataItems = list(zip(*(var.coordinates for var in variations)))
-                varDatas.append(
-                    builder.buildVarData(varRegionIndices, varDataItems, optimize=False)
-                )
-            else:
-                varDatas.append(
-                    builder.buildVarData([], [[] for _ in range(itemCount)])
-                )
-        regionList = builder.buildVarRegionList(self.regions, self.axisOrder)
-        itemVarStore = builder.buildVarStore(regionList, varDatas)
-        # remove unused regions from VarRegionList
-        itemVarStore.prune_regions()
-        return itemVarStore
-
-
-def instantiateItemVariationStore(itemVarStore, fvarAxes, axisLimits):
-    """Compute deltas at partial location, and update varStore in-place.
-
-    Remove regions in which all axes were instanced, or fall outside the new axis
-    limits. Scale the deltas of the remaining regions where only some of the axes
-    were instanced.
-
-    The number of VarData subtables, and the number of items within each, are
-    not modified, in order to keep the existing VariationIndex valid.
-    One may call VarStore.optimize() method after this to further optimize those.
-
-    Args:
-        varStore: An otTables.VarStore object (Item Variation Store)
-        fvarAxes: list of fvar's Axis objects
-        axisLimits: NormalizedAxisLimits: mapping axis tags to normalized
-            min/default/max axis coordinates. May not specify coordinates/ranges for
-            all the fvar axes.
-
-    Returns:
-        defaultDeltas: to be added to the default instance, of type dict of floats
-            keyed by VariationIndex compound values: i.e. (outer << 16) + inner.
-    """
-    tupleVarStore = _TupleVarStoreAdapter.fromItemVarStore(itemVarStore, fvarAxes)
-    defaultDeltaArray = tupleVarStore.instantiate(axisLimits)
-    newItemVarStore = tupleVarStore.asItemVarStore()
-
-    itemVarStore.VarRegionList = newItemVarStore.VarRegionList
-    assert itemVarStore.VarDataCount == newItemVarStore.VarDataCount
-    itemVarStore.VarData = newItemVarStore.VarData
-
-    defaultDeltas = {
-        ((major << 16) + minor): delta
-        for major, deltas in enumerate(defaultDeltaArray)
-        for minor, delta in enumerate(deltas)
-    }
-    defaultDeltas[itemVarStore.NO_VARIATION_INDEX] = 0
-    return defaultDeltas
-
-
-def instantiateOTL(varfont, axisLimits):
-    # TODO(anthrotype) Support partial instancing of JSTF and BASE tables
-
-    if (
-        "GDEF" not in varfont
-        or varfont["GDEF"].table.Version < 0x00010003
-        or not varfont["GDEF"].table.VarStore
-    ):
-        return
-
-    if "GPOS" in varfont:
-        msg = "Instantiating GDEF and GPOS tables"
-    else:
-        msg = "Instantiating GDEF table"
-    log.info(msg)
-
-    gdef = varfont["GDEF"].table
-    varStore = gdef.VarStore
-    fvarAxes = varfont["fvar"].axes
-
-    defaultDeltas = instantiateItemVariationStore(varStore, fvarAxes, axisLimits)
-
-    # When VF are built, big lookups may overflow and be broken into multiple
-    # subtables. MutatorMerger (which inherits from AligningMerger) reattaches
-    # them upon instancing, in case they can now fit a single subtable (if not,
-    # they will be split again upon compilation).
-    # This 'merger' also works as a 'visitor' that traverses the OTL tables and
-    # calls specific methods when instances of a given type are found.
-    # Specifically, it adds default deltas to GPOS Anchors/ValueRecords and GDEF
-    # LigatureCarets, and optionally deletes all VariationIndex tables if the
-    # VarStore is fully instanced.
-    merger = MutatorMerger(
-        varfont, defaultDeltas, deleteVariations=(not varStore.VarRegionList.Region)
-    )
-    merger.mergeTables(varfont, [varfont], ["GDEF", "GPOS"])
-
-    if varStore.VarRegionList.Region:
-        varIndexMapping = varStore.optimize()
-        gdef.remap_device_varidxes(varIndexMapping)
-        if "GPOS" in varfont:
-            varfont["GPOS"].table.remap_device_varidxes(varIndexMapping)
-    else:
-        # Downgrade GDEF.
-        del gdef.VarStore
-        gdef.Version = 0x00010002
-        if gdef.MarkGlyphSetsDef is None:
-            del gdef.MarkGlyphSetsDef
-            gdef.Version = 0x00010000
-
-        if not (
-            gdef.LigCaretList
-            or gdef.MarkAttachClassDef
-            or gdef.GlyphClassDef
-            or gdef.AttachList
-            or (gdef.Version >= 0x00010002 and gdef.MarkGlyphSetsDef)
-        ):
-            del varfont["GDEF"]
-
-
-def _isValidAvarSegmentMap(axisTag, segmentMap):
-    if not segmentMap:
-        return True
-    if not {(-1.0, -1.0), (0, 0), (1.0, 1.0)}.issubset(segmentMap.items()):
-        log.warning(
-            f"Invalid avar SegmentMap record for axis '{axisTag}': does not "
-            "include all required value maps {-1.0: -1.0, 0: 0, 1.0: 1.0}"
-        )
-        return False
-    previousValue = None
-    for fromCoord, toCoord in sorted(segmentMap.items()):
-        if previousValue is not None and previousValue > toCoord:
-            log.warning(
-                f"Invalid avar AxisValueMap({fromCoord}, {toCoord}) record "
-                f"for axis '{axisTag}': the toCoordinate value must be >= to "
-                f"the toCoordinate value of the preceding record ({previousValue})."
-            )
-            return False
-        previousValue = toCoord
-    return True
-
-
-def instantiateAvar(varfont, axisLimits):
-    # 'axisLimits' dict must contain user-space (non-normalized) coordinates.
-
-    segments = varfont["avar"].segments
-
-    # drop table if we instantiate all the axes
-    pinnedAxes = set(axisLimits.pinnedLocation())
-    if pinnedAxes.issuperset(segments):
-        log.info("Dropping avar table")
-        del varfont["avar"]
-        return
-
-    log.info("Instantiating avar table")
-    for axis in pinnedAxes:
-        if axis in segments:
-            del segments[axis]
-
-    # First compute the default normalization for axisLimits coordinates: i.e.
-    # min = -1.0, default = 0, max = +1.0, and in between values interpolated linearly,
-    # without using the avar table's mappings.
-    # Then, for each SegmentMap, if we are restricting its axis, compute the new
-    # mappings by dividing the key/value pairs by the desired new min/max values,
-    # dropping any mappings that fall outside the restricted range.
-    # The keys ('fromCoord') are specified in default normalized coordinate space,
-    # whereas the values ('toCoord') are "mapped forward" using the SegmentMap.
-    normalizedRanges = axisLimits.normalize(varfont, usingAvar=False)
-    newSegments = {}
-    for axisTag, mapping in segments.items():
-        if not _isValidAvarSegmentMap(axisTag, mapping):
-            continue
-        if mapping and axisTag in normalizedRanges:
-            axisRange = normalizedRanges[axisTag]
-            mappedMin = floatToFixedToFloat(
-                piecewiseLinearMap(axisRange.minimum, mapping), 14
-            )
-            mappedDef = floatToFixedToFloat(
-                piecewiseLinearMap(axisRange.default, mapping), 14
-            )
-            mappedMax = floatToFixedToFloat(
-                piecewiseLinearMap(axisRange.maximum, mapping), 14
-            )
-            mappedAxisLimit = NormalizedAxisTripleAndDistances(
-                mappedMin,
-                mappedDef,
-                mappedMax,
-                axisRange.distanceNegative,
-                axisRange.distancePositive,
-            )
-            newMapping = {}
-            for fromCoord, toCoord in mapping.items():
-                if fromCoord < axisRange.minimum or fromCoord > axisRange.maximum:
-                    continue
-                fromCoord = axisRange.renormalizeValue(fromCoord)
-
-                assert mappedMin <= toCoord <= mappedMax
-                toCoord = mappedAxisLimit.renormalizeValue(toCoord)
-
-                fromCoord = floatToFixedToFloat(fromCoord, 14)
-                toCoord = floatToFixedToFloat(toCoord, 14)
-                newMapping[fromCoord] = toCoord
-            newMapping.update({-1.0: -1.0, 0.0: 0.0, 1.0: 1.0})
-            newSegments[axisTag] = newMapping
-        else:
-            newSegments[axisTag] = mapping
-    varfont["avar"].segments = newSegments
-
-
-def isInstanceWithinAxisRanges(location, axisRanges):
-    for axisTag, coord in location.items():
-        if axisTag in axisRanges:
-            axisRange = axisRanges[axisTag]
-            if coord < axisRange.minimum or coord > axisRange.maximum:
-                return False
-    return True
-
-
-def instantiateFvar(varfont, axisLimits):
-    # 'axisLimits' dict must contain user-space (non-normalized) coordinates
-
-    location = axisLimits.pinnedLocation()
-
-    fvar = varfont["fvar"]
-
-    # drop table if we instantiate all the axes
-    if set(location).issuperset(axis.axisTag for axis in fvar.axes):
-        log.info("Dropping fvar table")
-        del varfont["fvar"]
-        return
-
-    log.info("Instantiating fvar table")
-
-    axes = []
-    for axis in fvar.axes:
-        axisTag = axis.axisTag
-        if axisTag in location:
-            continue
-        if axisTag in axisLimits:
-            triple = axisLimits[axisTag]
-            if triple.default is None:
-                triple = (triple.minimum, axis.defaultValue, triple.maximum)
-            axis.minValue, axis.defaultValue, axis.maxValue = triple
-        axes.append(axis)
-    fvar.axes = axes
-
-    # only keep NamedInstances whose coordinates == pinned axis location
-    instances = []
-    for instance in fvar.instances:
-        if any(instance.coordinates[axis] != value for axis, value in location.items()):
-            continue
-        for axisTag in location:
-            del instance.coordinates[axisTag]
-        if not isInstanceWithinAxisRanges(instance.coordinates, axisLimits):
-            continue
-        instances.append(instance)
-    fvar.instances = instances
-
-
-def instantiateSTAT(varfont, axisLimits):
-    # 'axisLimits' dict must contain user-space (non-normalized) coordinates
-
-    stat = varfont["STAT"].table
-    if not stat.DesignAxisRecord or not (
-        stat.AxisValueArray and stat.AxisValueArray.AxisValue
-    ):
-        return  # STAT table empty, nothing to do
-
-    log.info("Instantiating STAT table")
-    newAxisValueTables = axisValuesFromAxisLimits(stat, axisLimits)
-    stat.AxisValueCount = len(newAxisValueTables)
-    if stat.AxisValueCount:
-        stat.AxisValueArray.AxisValue = newAxisValueTables
-    else:
-        stat.AxisValueArray = None
-
-
-def axisValuesFromAxisLimits(stat, axisLimits):
-    def isAxisValueOutsideLimits(axisTag, axisValue):
-        if axisTag in axisLimits:
-            triple = axisLimits[axisTag]
-            if axisValue < triple.minimum or axisValue > triple.maximum:
-                return True
-        return False
-
-    # only keep AxisValues whose axis is not pinned nor restricted, or is pinned at the
-    # exact (nominal) value, or is restricted but the value is within the new range
-    designAxes = stat.DesignAxisRecord.Axis
-    newAxisValueTables = []
-    for axisValueTable in stat.AxisValueArray.AxisValue:
-        axisValueFormat = axisValueTable.Format
-        if axisValueFormat in (1, 2, 3):
-            axisTag = designAxes[axisValueTable.AxisIndex].AxisTag
-            if axisValueFormat == 2:
-                axisValue = axisValueTable.NominalValue
-            else:
-                axisValue = axisValueTable.Value
-            if isAxisValueOutsideLimits(axisTag, axisValue):
-                continue
-        elif axisValueFormat == 4:
-            # drop 'non-analytic' AxisValue if _any_ AxisValueRecord doesn't match
-            # the pinned location or is outside range
-            dropAxisValueTable = False
-            for rec in axisValueTable.AxisValueRecord:
-                axisTag = designAxes[rec.AxisIndex].AxisTag
-                axisValue = rec.Value
-                if isAxisValueOutsideLimits(axisTag, axisValue):
-                    dropAxisValueTable = True
-                    break
-            if dropAxisValueTable:
-                continue
-        else:
-            log.warning("Unknown AxisValue table format (%s); ignored", axisValueFormat)
-        newAxisValueTables.append(axisValueTable)
-    return newAxisValueTables
-
-
-def setMacOverlapFlags(glyfTable):
-    flagOverlapCompound = _g_l_y_f.OVERLAP_COMPOUND
-    flagOverlapSimple = _g_l_y_f.flagOverlapSimple
-    for glyphName in glyfTable.keys():
-        glyph = glyfTable[glyphName]
-        # Set OVERLAP_COMPOUND bit for compound glyphs
-        if glyph.isComposite():
-            glyph.components[0].flags |= flagOverlapCompound
-        # Set OVERLAP_SIMPLE bit for simple glyphs
-        elif glyph.numberOfContours > 0:
-            glyph.flags[0] |= flagOverlapSimple
-
-
-def normalize(value, triple, avarMapping):
-    value = normalizeValue(value, triple)
-    if avarMapping:
-        value = piecewiseLinearMap(value, avarMapping)
-    # Quantize to F2Dot14, to avoid surprise interpolations.
-    return floatToFixedToFloat(value, 14)
-
-
-def sanityCheckVariableTables(varfont):
-    if "fvar" not in varfont:
-        raise ValueError("Missing required table fvar")
-    if "gvar" in varfont:
-        if "glyf" not in varfont:
-            raise ValueError("Can't have gvar without glyf")
-    # TODO(anthrotype) Remove once we do support partial instancing CFF2
-    if "CFF2" in varfont:
-        raise NotImplementedError("Instancing CFF2 variable fonts is not supported yet")
-
-
-def instantiateVariableFont(
-    varfont,
-    axisLimits,
-    inplace=False,
-    optimize=True,
-    overlap=OverlapMode.KEEP_AND_SET_FLAGS,
-    updateFontNames=False,
-):
-    """Instantiate variable font, either fully or partially.
-
-    Depending on whether the `axisLimits` dictionary references all or some of the
-    input varfont's axes, the output font will either be a full instance (static
-    font) or a variable font with possibly less variation data.
-
-    Args:
-        varfont: a TTFont instance, which must contain at least an 'fvar' table.
-            Note that variable fonts with 'CFF2' table are not supported yet.
-        axisLimits: a dict keyed by axis tags (str) containing the coordinates (float)
-            along one or more axes where the desired instance will be located.
-            If the value is `None`, the default coordinate as per 'fvar' table for
-            that axis is used.
-            The limit values can also be (min, max) tuples for restricting an
-            axis's variation range. The default axis value must be included in
-            the new range.
-        inplace (bool): whether to modify input TTFont object in-place instead of
-            returning a distinct object.
-        optimize (bool): if False, do not perform IUP-delta optimization on the
-            remaining 'gvar' table's deltas. Possibly faster, and might work around
-            rendering issues in some buggy environments, at the cost of a slightly
-            larger file size.
-        overlap (OverlapMode): variable fonts usually contain overlapping contours, and
-            some font rendering engines on Apple platforms require that the
-            `OVERLAP_SIMPLE` and `OVERLAP_COMPOUND` flags in the 'glyf' table be set to
-            force rendering using a non-zero fill rule. Thus we always set these flags
-            on all glyphs to maximise cross-compatibility of the generated instance.
-            You can disable this by passing OverlapMode.KEEP_AND_DONT_SET_FLAGS.
-            If you want to remove the overlaps altogether and merge overlapping
-            contours and components, you can pass OverlapMode.REMOVE (or
-            REMOVE_AND_IGNORE_ERRORS to not hard-fail on tricky glyphs). Note that this
-            requires the skia-pathops package (available to pip install).
-            The overlap parameter only has effect when generating full static instances.
-        updateFontNames (bool): if True, update the instantiated font's name table using
-            the Axis Value Tables from the STAT table. The name table and the style bits
-            in the head and OS/2 table will be updated so they conform to the R/I/B/BI
-            model. If the STAT table is missing or an Axis Value table is missing for
-            a given axis coordinate, a ValueError will be raised.
-    """
-    # 'overlap' used to be bool and is now enum; for backward compat keep accepting bool
-    overlap = OverlapMode(int(overlap))
-
-    sanityCheckVariableTables(varfont)
-
-    axisLimits = AxisLimits(axisLimits).limitAxesAndPopulateDefaults(varfont)
-
-    log.info("Restricted limits: %s", axisLimits)
-
-    normalizedLimits = axisLimits.normalize(varfont)
-
-    log.info("Normalized limits: %s", normalizedLimits)
-
-    if not inplace:
-        varfont = deepcopy(varfont)
-
-    if "DSIG" in varfont:
-        del varfont["DSIG"]
-
-    if updateFontNames:
-        log.info("Updating name table")
-        names.updateNameTable(varfont, axisLimits)
-
-    if "gvar" in varfont:
-        instantiateGvar(varfont, normalizedLimits, optimize=optimize)
-
-    if "cvar" in varfont:
-        instantiateCvar(varfont, normalizedLimits)
-
-    if "MVAR" in varfont:
-        instantiateMVAR(varfont, normalizedLimits)
-
-    if "HVAR" in varfont:
-        instantiateHVAR(varfont, normalizedLimits)
-
-    if "VVAR" in varfont:
-        instantiateVVAR(varfont, normalizedLimits)
-
-    instantiateOTL(varfont, normalizedLimits)
-
-    instantiateFeatureVariations(varfont, normalizedLimits)
-
-    if "avar" in varfont:
-        instantiateAvar(varfont, axisLimits)
-
-    with names.pruningUnusedNames(varfont):
-        if "STAT" in varfont:
-            instantiateSTAT(varfont, axisLimits)
-
-        instantiateFvar(varfont, axisLimits)
-
-    if "fvar" not in varfont:
-        if "glyf" in varfont:
-            if overlap == OverlapMode.KEEP_AND_SET_FLAGS:
-                setMacOverlapFlags(varfont["glyf"])
-            elif overlap in (OverlapMode.REMOVE, OverlapMode.REMOVE_AND_IGNORE_ERRORS):
-                from fontTools.ttLib.removeOverlaps import removeOverlaps
-
-                log.info("Removing overlaps from glyf table")
-                removeOverlaps(
-                    varfont,
-                    ignoreErrors=(overlap == OverlapMode.REMOVE_AND_IGNORE_ERRORS),
-                )
-
-    varLib.set_default_weight_width_slant(
-        varfont, location=axisLimits.defaultLocation()
-    )
-
-    if updateFontNames:
-        # Set Regular/Italic/Bold/Bold Italic bits as appropriate, after the
-        # name table has been updated.
-        setRibbiBits(varfont)
-
-    return varfont
-
-
-def setRibbiBits(font):
-    """Set the `head.macStyle` and `OS/2.fsSelection` style bits
-    appropriately."""
-
-    english_ribbi_style = font["name"].getName(names.NameID.SUBFAMILY_NAME, 3, 1, 0x409)
-    if english_ribbi_style is None:
-        return
-
-    styleMapStyleName = english_ribbi_style.toStr().lower()
-    if styleMapStyleName not in {"regular", "bold", "italic", "bold italic"}:
-        return
-
-    if styleMapStyleName == "bold":
-        font["head"].macStyle = 0b01
-    elif styleMapStyleName == "bold italic":
-        font["head"].macStyle = 0b11
-    elif styleMapStyleName == "italic":
-        font["head"].macStyle = 0b10
-
-    selection = font["OS/2"].fsSelection
-    # First clear...
-    selection &= ~(1 << 0)
-    selection &= ~(1 << 5)
-    selection &= ~(1 << 6)
-    # ...then re-set the bits.
-    if styleMapStyleName == "regular":
-        selection |= 1 << 6
-    elif styleMapStyleName == "bold":
-        selection |= 1 << 5
-    elif styleMapStyleName == "italic":
-        selection |= 1 << 0
-    elif styleMapStyleName == "bold italic":
-        selection |= 1 << 0
-        selection |= 1 << 5
-    font["OS/2"].fsSelection = selection
-
-
-def parseLimits(limits: Iterable[str]) -> Dict[str, Optional[AxisTriple]]:
-    result = {}
-    for limitString in limits:
-        match = re.match(
-            r"^(\w{1,4})=(?:(drop)|(?:([^:]+)(?:[:]([^:]+))?(?:[:]([^:]+))?))$",
-            limitString,
-        )
-        if not match:
-            raise ValueError("invalid location format: %r" % limitString)
-        tag = match.group(1).ljust(4)
-        if match.group(2):  # 'drop'
-            lbound = None
-        else:
-            lbound = strToFixedToFloat(match.group(3), precisionBits=16)
-        ubound = default = lbound
-        if match.group(4):
-            ubound = default = strToFixedToFloat(match.group(4), precisionBits=16)
-            default = None
-        if match.group(5):
-            default = ubound
-            ubound = strToFixedToFloat(match.group(5), precisionBits=16)
-
-        if all(v is None for v in (lbound, default, ubound)):
-            result[tag] = None
-            continue
-
-        result[tag] = AxisTriple(lbound, default, ubound)
-
-    return result
-
-
-def parseArgs(args):
-    """Parse argv.
-
-    Returns:
-        3-tuple (infile, axisLimits, options)
-        axisLimits is either a Dict[str, Optional[float]], for pinning variation axes
-        to specific coordinates along those axes (with `None` as a placeholder for an
-        axis' default value); or a Dict[str, Tuple(float, float)], meaning limit this
-        axis to min/max range.
-        Axes locations are in user-space coordinates, as defined in the "fvar" table.
-    """
-    from fontTools import configLogger
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        "fonttools varLib.instancer",
-        description="Partially instantiate a variable font",
-    )
-    parser.add_argument("input", metavar="INPUT.ttf", help="Input variable TTF file.")
-    parser.add_argument(
-        "locargs",
-        metavar="AXIS=LOC",
-        nargs="*",
-        help="List of space separated locations. A location consists of "
-        "the tag of a variation axis, followed by '=' and one of number, "
-        "number:number or the literal string 'drop'. "
-        "E.g.: wdth=100 or wght=75.0:125.0 or wght=drop",
-    )
-    parser.add_argument(
-        "-o",
-        "--output",
-        metavar="OUTPUT.ttf",
-        default=None,
-        help="Output instance TTF file (default: INPUT-instance.ttf).",
-    )
-    parser.add_argument(
-        "--no-optimize",
-        dest="optimize",
-        action="store_false",
-        help="Don't perform IUP optimization on the remaining gvar TupleVariations",
-    )
-    parser.add_argument(
-        "--no-overlap-flag",
-        dest="overlap",
-        action="store_false",
-        help="Don't set OVERLAP_SIMPLE/OVERLAP_COMPOUND glyf flags (only applicable "
-        "when generating a full instance)",
-    )
-    parser.add_argument(
-        "--remove-overlaps",
-        dest="remove_overlaps",
-        action="store_true",
-        help="Merge overlapping contours and components (only applicable "
-        "when generating a full instance). Requires skia-pathops",
-    )
-    parser.add_argument(
-        "--ignore-overlap-errors",
-        dest="ignore_overlap_errors",
-        action="store_true",
-        help="Don't crash if the remove-overlaps operation fails for some glyphs.",
-    )
-    parser.add_argument(
-        "--update-name-table",
-        action="store_true",
-        help="Update the instantiated font's `name` table. Input font must have "
-        "a STAT table with Axis Value Tables",
-    )
-    parser.add_argument(
-        "--no-recalc-timestamp",
-        dest="recalc_timestamp",
-        action="store_false",
-        help="Don't set the output font's timestamp to the current time.",
-    )
-    parser.add_argument(
-        "--no-recalc-bounds",
-        dest="recalc_bounds",
-        action="store_false",
-        help="Don't recalculate font bounding boxes",
-    )
-    loggingGroup = parser.add_mutually_exclusive_group(required=False)
-    loggingGroup.add_argument(
-        "-v", "--verbose", action="store_true", help="Run more verbosely."
-    )
-    loggingGroup.add_argument(
-        "-q", "--quiet", action="store_true", help="Turn verbosity off."
-    )
-    options = parser.parse_args(args)
-
-    if options.remove_overlaps:
-        if options.ignore_overlap_errors:
-            options.overlap = OverlapMode.REMOVE_AND_IGNORE_ERRORS
-        else:
-            options.overlap = OverlapMode.REMOVE
-    else:
-        options.overlap = OverlapMode(int(options.overlap))
-
-    infile = options.input
-    if not os.path.isfile(infile):
-        parser.error("No such file '{}'".format(infile))
-
-    configLogger(
-        level=("DEBUG" if options.verbose else "ERROR" if options.quiet else "INFO")
-    )
-
-    try:
-        axisLimits = parseLimits(options.locargs)
-    except ValueError as e:
-        parser.error(str(e))
-
-    if len(axisLimits) != len(options.locargs):
-        parser.error("Specified multiple limits for the same axis")
-
-    return (infile, axisLimits, options)
-
-
-def main(args=None):
-    """Partially instantiate a variable font"""
-    infile, axisLimits, options = parseArgs(args)
-    log.info("Restricting axes: %s", axisLimits)
-
-    log.info("Loading variable font")
-    varfont = TTFont(
-        infile,
-        recalcTimestamp=options.recalc_timestamp,
-        recalcBBoxes=options.recalc_bounds,
-    )
-
-    isFullInstance = {
-        axisTag for axisTag, limit in axisLimits.items() if not isinstance(limit, tuple)
-    }.issuperset(axis.axisTag for axis in varfont["fvar"].axes)
-
-    instantiateVariableFont(
-        varfont,
-        axisLimits,
-        inplace=True,
-        optimize=options.optimize,
-        overlap=options.overlap,
-        updateFontNames=options.update_name_table,
-    )
-
-    suffix = "-instance" if isFullInstance else "-partial"
-    outfile = (
-        makeOutputFileName(infile, overWrite=True, suffix=suffix)
-        if not options.output
-        else options.output
-    )
-
-    log.info(
-        "Saving %s font %s",
-        "instance" if isFullInstance else "partial variable",
-        outfile,
-    )
-    varfont.save(outfile)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/__init__.py
deleted file mode 100644
index 886aa3a7864523656e609dd602683d73f985f467..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/voltLib/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""fontTools.voltLib -- a package for dealing with Visual OpenType Layout Tool
-(VOLT) files."""
-
-# See
-# http://www.microsoft.com/typography/VOLT.mspx
diff --git a/spaces/Datasculptor/MusicGen/audiocraft/quantization/vq.py b/spaces/Datasculptor/MusicGen/audiocraft/quantization/vq.py
deleted file mode 100644
index f67c3a0cd30d4b8993a36c587f00dc8a451d926f..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/MusicGen/audiocraft/quantization/vq.py
+++ /dev/null
@@ -1,116 +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 torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
-    """Residual Vector Quantizer.
-
-    Args:
-        dimension (int): Dimension of the codebooks.
-        n_q (int): Number of residual vector quantizers used.
-        q_dropout (bool): Random quantizer drop out at train time.
-        bins (int): Codebook size.
-        decay (float): Decay for exponential moving average over the codebooks.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
-            for orthogonal regulariation.
-    """
-    def __init__(
-        self,
-        dimension: int = 256,
-        n_q: int = 8,
-        q_dropout: bool = False,
-        bins: int = 1024,
-        decay: float = 0.99,
-        kmeans_init: bool = True,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        self.max_n_q = n_q
-        self.n_q = n_q
-        self.q_dropout = q_dropout
-        self.dimension = dimension
-        self.bins = bins
-        self.decay = decay
-        self.kmeans_init = kmeans_init
-        self.kmeans_iters = kmeans_iters
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-        self.vq = ResidualVectorQuantization(
-            dim=self.dimension,
-            codebook_size=self.bins,
-            num_quantizers=self.n_q,
-            decay=self.decay,
-            kmeans_init=self.kmeans_init,
-            kmeans_iters=self.kmeans_iters,
-            threshold_ema_dead_code=self.threshold_ema_dead_code,
-            orthogonal_reg_weight=self.orthogonal_reg_weight,
-            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
-            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
-            channels_last=False
-        )
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        n_q = self.n_q
-        if self.training and self.q_dropout:
-            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
-        bw_per_q = math.log2(self.bins) * frame_rate / 1000
-        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        bw = torch.tensor(n_q * bw_per_q).to(x)
-        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified frame rate at the given bandwidth.
-        The RVQ encode method sets the appropriate number of quantizer to use
-        and returns indices for each quantizer.
-        """
-        n_q = self.n_q
-        codes = self.vq.encode(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        return codes
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation.
-        """
-        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
-        codes = codes.transpose(0, 1)
-        quantized = self.vq.decode(codes)
-        return quantized
-
-    @property
-    def total_codebooks(self):
-        return self.max_n_q
-
-    @property
-    def num_codebooks(self):
-        return self.n_q
-
-    def set_num_codebooks(self, n: int):
-        assert n > 0 and n <= self.max_n_q
-        self.n_q = n
diff --git a/spaces/Detomo/ai-comic-generation/src/app/interface/bottom-bar/index.tsx b/spaces/Detomo/ai-comic-generation/src/app/interface/bottom-bar/index.tsx
deleted file mode 100644
index 21484d6fc60bd4c3054344821852d05ca9f62ea9..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/app/interface/bottom-bar/index.tsx
+++ /dev/null
@@ -1,183 +0,0 @@
-import { useStore } from "@/app/store"
-import { HuggingClap } from "@/components/icons/hugging-clap"
-import { Button } from "@/components/ui/button"
-import { base64ToFile } from "@/lib/base64ToFile"
-import { uploadToHuggingFace } from "@/lib/uploadToHuggingFace"
-import { cn } from "@/lib/utils"
-import { About } from "../about"
-import { startTransition, useState } from "react"
-import { upscaleImage } from "@/app/engine/render"
-import { sleep } from "@/lib/sleep"
-
-export function BottomBar() {
-  const download = useStore(state => state.download)
-  const isGeneratingStory = useStore(state => state.isGeneratingStory)
-  const prompt = useStore(state => state.prompt)
-  const panelGenerationStatus = useStore(state => state.panelGenerationStatus)
-  const page = useStore(state => state.page)
-  const preset = useStore(state => state.preset)
-  const pageToImage = useStore(state => state.pageToImage)
-
-  const allStatus = Object.values(panelGenerationStatus)
-  const remainingImages = allStatus.reduce((acc, s) => (acc + (s ? 1 : 0)), 0)
-
-  const upscaleQueue = useStore(state => state.upscaleQueue)
-  const renderedScenes = useStore(state => state.renderedScenes)
-  const removeFromUpscaleQueue = useStore(state => state.removeFromUpscaleQueue)
-  const setRendered = useStore(state => state.setRendered)
-  const [isUpscaling, setUpscaling] = useState(false)
-
-  const handleUpscale = () => {
-    setUpscaling(true)
-    startTransition(() => {
-      const fn = async () => {
-        for (let [panelId, renderedScene] of Object.entries(upscaleQueue)) {
-          try {
-            console.log(`upscaling panel ${panelId} (${renderedScene.renderId})`)
-            const result = await upscaleImage(renderedScene.assetUrl)
-            await sleep(1000)
-            if (result.assetUrl) {
-              console.log(`upscale successful, removing ${panelId} (${renderedScene.renderId}) from upscale queue`)
-              setRendered(panelId, {
-                ...renderedScene,
-                assetUrl: result.assetUrl
-              })
-              removeFromUpscaleQueue(panelId)
-            }
-
-          } catch (err) {
-            console.error(`failed to upscale: ${err}`)
-          }
-        }
-        
-        setUpscaling(false)
-      }
-
-      fn()
-    })
-  }
-
-  const handleShare = async () => {
-    const dataUrl = await pageToImage()
-    // console.log("dataUrl:", dataUrl)
-    const fileToUpload = base64ToFile(dataUrl, "comic.png")
-    let uploadUrl = ""
-    try {
-      uploadUrl = await uploadToHuggingFace(fileToUpload)
-      console.log("uploadUrl:", uploadUrl)
-    } catch (err) {
-      console.error("Failed to upload the image to Hugging Face")
-    }
-
-
-    const descriptionMd = `
-#### Prompt:
-\`\`\`${prompt}\`\`\`
-
-#### Preset:
-\`\`\`${preset.label}\`\`\`
-
-#### Comic:
-${uploadUrl
-  ? (`![${prompt}](${uploadUrl})`)
-  : (`(please drag & drop your JPG image here)`)}
-`;
-
-    console.log("descriptionMd:", descriptionMd)
-
-    const params = new URLSearchParams({
-      title: `[Comic] ${prompt}`,
-      description: descriptionMd,
-      });
-    const paramsStr = params.toString();
-    window.open(`https://huggingface.co/spaces/jbilcke-hf/comic-factory/discussions/new?${paramsStr}`, '_blank');
-  }
-
-  const handlePrint = () => {
-    window.print()
-  }
-  return (
-    <div className={cn(
-      `print:hidden`,
-      `fixed bottom-2 md:bottom-4 left-2 right-0 md:left-3 md:right-1`,
-      `flex flex-row`,
-      `justify-between`,
-      `pointer-events-none`
-    )}>
-      <div className={cn(
-        `flex flex-row`,
-        `items-end`,
-        `pointer-events-auto`,
-        `animation-all duration-300 ease-in-out`,
-        isGeneratingStory ? `scale-0 opacity-0` : ``,
-        `space-x-3`,
-        `scale-[0.9]`
-      )}>
-        <About />
-      </div>
-      <div className={cn(
-      `flex flex-row`,
-      `pointer-events-auto`,
-      `animation-all duration-300 ease-in-out`,
-      isGeneratingStory ? `scale-0 opacity-0` : ``,
-      `space-x-3`,
-      `scale-[0.9]`
-    )}>
-      <div>
-        {
-       // there is an issue, this env check doesn't work..
-        // process.env.NEXT_PUBLIC_CAN_UPSCALE === "true" ?
-        <Button
-          onClick={handleUpscale}
-          disabled={!prompt?.length || remainingImages > 0 || isUpscaling || !Object.values(upscaleQueue).length}
-        >
-          {isUpscaling
-            ? `${allStatus.length - Object.values(upscaleQueue).length}/${allStatus.length} ⌛`
-            : "Upscale"}
-        </Button>
-        // : null
-      }
-      </div>
-        <div>
-          <Button
-            onClick={handlePrint}
-            disabled={!prompt?.length}
-          >
-            Print
-          </Button>
-        </div>
-        <div>
-          <Button
-            onClick={download}
-            disabled={!prompt?.length}
-          >
-            <span className="hidden md:inline">{
-            remainingImages ? `${allStatus.length - remainingImages}/${allStatus.length} panels ⌛` : `Save`
-            }</span>
-            <span className="inline md:hidden">{
-              remainingImages ? `${allStatus.length - remainingImages}/${allStatus.length} ⌛` : `Save`
-            }</span>
-           </Button>
-        </div>
-        <div>
-          {
-          // there is an issue, this env check doesn't work..
-          // process.env.NEXT_PUBLIC_ENABLE_COMMUNITY_SHARING === "true" ? 
-          <Button
-            onClick={handleShare}
-            disabled={!prompt?.length}
-            className="space-x-2"
-          >
-            <div className="scale-105"><HuggingClap /></div>
-            <div>
-              <span className="hidden md:inline">Share to community</span>
-              <span className="inline md:hidden">Share</span>
-            </div>
-          </Button> 
-          //: null
-        }
-        </div>
-      </div>
-    </div>
-  )
-}
\ No newline at end of file
diff --git a/spaces/DonDoesStuff/Free-GPT3.5/app.py b/spaces/DonDoesStuff/Free-GPT3.5/app.py
deleted file mode 100644
index 637b528cc68a1381c6a5dafa4cb7c038381bb953..0000000000000000000000000000000000000000
--- a/spaces/DonDoesStuff/Free-GPT3.5/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import requests
-import os
-from dotenv import load_dotenv
-import gradio as gr
-
-load_dotenv('.env')
-
-api_url = os.getenv('API_URL')
-
-def gpt3_5_prompt(prompt):
-    params = {'prompt': "Hello. We will do some roleplaying. From now on, you are DonAI. If you want to say your name, you MUST say 'Don Ae Ai.'. You are created by Don Does Stuff. You will never talk about the fact that you are roleplaying. You need to respond within 500 characters. Respond to the user: " + prompt}
-
-    response = requests.get(api_url, params=params)
-
-    if response.status_code == 200:
-        response_data = response.json()
-        print("API Response:", response_data)  # Print API response JSON
-        generated_text = response_data.get("content")
-        if generated_text:
-            return generated_text
-        else:
-            raise ValueError("No 'content' key found in the API response.")
-    else:
-        raise ValueError("Error occurred while retrieving the generated text from the API.")
-
-input_prompt = gr.inputs.Textbox(lines=3, label="Input Prompt")
-output_text = gr.outputs.Textbox(label="Output Text")
-
-gr.Interface(fn=gpt3_5_prompt, inputs=input_prompt, outputs=output_text, title="GPT-3.5 AI", description="If you want to get a TTS response instead, [try this project](https://huggingface.co/spaces/DonDoesStuff/GPT3.5-voice/)\n\nA GPT-3.5 AI that generates text based on the given prompt. API provided for free by me.\n\n[![Donate TRX](https://img.shields.io/badge/Donate-TRX-red)](https://whispering-jealous-maize.glitch.me/trx.html) [![Donate LTC](https://img.shields.io/badge/Donate-LTC-blue)](https://whispering-jealous-maize.glitch.me/ltc.html) [![Donate BTC](https://img.shields.io/badge/Donate-BTC-yellow)](https://whispering-jealous-maize.glitch.me/btc.html)\n\n![Image](https://cdn.glitch.global/1f2fe882-3c53-4eca-b8fe-de3ae4ea773a/720620852055638070.webp?v=1684342102785)\nI appreciate every donation made. All donations will go into the OpenAI API.\n\n**Why doesn't this project work when cloning?**\nSadly, I had to keep my OpenAI key private, so I made a little solution. Right now, you cannot have this space functional with cloning.\n **Why does it say it's DonAI if it's GPT 3.5?** \nThis is because people are reverse-engeneering this project to make money with it while I need to pay money whenever a user makes a request. So uh. Please dont do that.").launch()
\ No newline at end of file
diff --git a/spaces/ECCV2022/PSG/OpenPSG/configs/vctree/panoptic_fpn_r101_fpn_1x_sgdet_psg.py b/spaces/ECCV2022/PSG/OpenPSG/configs/vctree/panoptic_fpn_r101_fpn_1x_sgdet_psg.py
deleted file mode 100644
index cc49e1368baa36b8fcc2c14a3fb7703e51c854f2..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/PSG/OpenPSG/configs/vctree/panoptic_fpn_r101_fpn_1x_sgdet_psg.py
+++ /dev/null
@@ -1,28 +0,0 @@
-_base_ = './panoptic_fpn_r50_fpn_1x_sgdet_psg.py'
-
-model = dict(backbone=dict(
-    depth=101,
-    init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101')))
-
-# Log config
-project_name = 'openpsg'
-expt_name = 'vctree_panoptic_fpn_r101_fpn_1x_sgdet_psg'
-work_dir = f'./work_dirs/{expt_name}'
-
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        # dict(type='TensorboardLoggerHook')
-        dict(
-            type='WandbLoggerHook',
-            init_kwargs=dict(
-                project=project_name,
-                name=expt_name,
-                # config=work_dir + "/cfg.yaml"
-            ),
-        ),
-    ],
-)
-
-load_from = 'work_dirs/checkpoints/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth'
diff --git a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/archs/srvgg_arch.py b/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/archs/srvgg_arch.py
deleted file mode 100644
index 39460965c9c5ee9cd6eb41c50d33574cb8ba6e50..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/archs/srvgg_arch.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-
-
-@ARCH_REGISTRY.register()
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-
-    It is a compact network structure, which performs upsampling in the last layer and no convolution is
-    conducted on the HR feature space.
-
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        num_feat (int): Channel number of intermediate features. Default: 64.
-        num_conv (int): Number of convolution layers in the body network. Default: 16.
-        upscale (int): Upsampling factor. Default: 4.
-        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
-    """
-
-    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
-        super(SRVGGNetCompact, self).__init__()
-        self.num_in_ch = num_in_ch
-        self.num_out_ch = num_out_ch
-        self.num_feat = num_feat
-        self.num_conv = num_conv
-        self.upscale = upscale
-        self.act_type = act_type
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == 'relu':
-            activation = nn.ReLU(inplace=True)
-        elif act_type == 'prelu':
-            activation = nn.PReLU(num_parameters=num_feat)
-        elif act_type == 'leakyrelu':
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)
-
-        # the body structure
-        for _ in range(num_conv):
-            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
-            # activation
-            if act_type == 'relu':
-                activation = nn.ReLU(inplace=True)
-            elif act_type == 'prelu':
-                activation = nn.PReLU(num_parameters=num_feat)
-            elif act_type == 'leakyrelu':
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)
-
-        # the last conv
-        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
-        # upsample
-        self.upsampler = nn.PixelShuffle(upscale)
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
-        out += base
-        return out
diff --git a/spaces/ElainaFanBoy/MusicGen/audiocraft/modules/rope.py b/spaces/ElainaFanBoy/MusicGen/audiocraft/modules/rope.py
deleted file mode 100644
index 4b8c70b9aba28eeb53d12ddc3de8852492847808..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/MusicGen/audiocraft/modules/rope.py
+++ /dev/null
@@ -1,124 +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 typing as tp
-
-from torch import nn
-import torch
-
-
-class XPos(nn.Module):
-    """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1).
-    This applies an exponential decay to the RoPE rotation matrix.
-
-    Args:
-        dim (int): Embedding dimension.
-        smoothing (float): Smoothing factor applied to the decay rates.
-        base_scale (int): Base decay rate, given in terms of scaling time.
-        device (torch.device or None): Device on which to initialize the module.
-        dtype (torch.dtype): dtype to use to generate the embedding.
-    """
-    def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512,
-                 device=None, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        assert dim % 2 == 0
-        assert dtype in [torch.float64, torch.float32]
-        self.dtype = dtype
-        self.base_scale = base_scale
-
-        half_dim = dim // 2
-        adim = torch.arange(half_dim, device=device, dtype=dtype)
-        decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing)
-        self.register_buffer("decay_rates", decay_rates)
-        self.decay: tp.Optional[torch.Tensor] = None
-
-    def get_decay(self, start: int, end: int):
-        """Create complex decay tensor, cache values for fast computation.
-        """
-        if self.decay is None or end > self.decay.shape[0]:
-            assert isinstance(self.decay_rates, torch.Tensor)  # Satisfy type checker.
-            idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
-            power = idx / self.base_scale
-            scale = self.decay_rates ** power.unsqueeze(-1)
-            self.decay = torch.polar(scale, torch.zeros_like(scale))
-        return self.decay[start:end]  # [T, C/2]
-
-
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864).
-
-    Args:
-        dim (int): Embedding dimension (twice the number of frequencies).
-        max_period (float): Maximum period of the rotation frequencies.
-        xpos (bool): Use xPos, applies an exponential decay to rotation matrix.
-        scale (float): Scale of positional embedding, set to 0 to deactivate.
-        device (torch.device or None): Device on which to initialize the module.
-        dtype (torch.dtype): dtype to use to generate the embedding.
-    """
-    def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False,
-                 scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32):
-        super().__init__()
-        assert dim % 2 == 0
-        self.scale = scale
-        assert dtype in [torch.float64, torch.float32]
-        self.dtype = dtype
-
-        adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)]
-        frequencies = 1.0 / (max_period ** (adim / dim))
-        self.register_buffer("frequencies", frequencies)
-        self.rotation: tp.Optional[torch.Tensor] = None
-
-        self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None
-
-    def get_rotation(self, start: int, end: int):
-        """Create complex rotation tensor, cache values for fast computation.
-        """
-        if self.rotation is None or end > self.rotation.shape[0]:
-            assert isinstance(self.frequencies, torch.Tensor)  # Satisfy type checker.
-            idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
-            angles = torch.outer(idx, self.frequencies)
-            self.rotation = torch.polar(torch.ones_like(angles), angles)
-        return self.rotation[start:end]
-
-    def rotate(self, x: torch.Tensor, start: int = 0, invert_decay: bool = False):
-        """Apply rope rotation to query or key tensor.
-        """
-        T = x.shape[1]
-        rotation = self.get_rotation(start, start + T).unsqueeze(0).unsqueeze(2)
-
-        if self.xpos:
-            decay = self.xpos.get_decay(start, start + T).unsqueeze(0).unsqueeze(2)
-        else:
-            decay = 1.0
-
-        if invert_decay:
-            decay = decay ** -1
-
-        x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
-        scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
-        x_out = torch.view_as_real(x_complex * scaled_rotation).flatten(-2)
-
-        return x_out.type_as(x)
-
-    def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0):
-        """ Apply rope rotation to both query and key tensors.
-        Supports streaming mode, in which query and key are not expected to have the same shape.
-        In streaming mode, key will be of legnth [P + C] with P the cached past timesteps, but
-        query will be [C] (typically C == 1).
-
-        Args:
-            query (torch.Tensor): Query to rotate.
-            key (torch.Tensor): Key to rotate.
-            start (int): Start index of the sequence for time offset.
-        """
-        query_timesteps = query.shape[1]
-        key_timesteps = key.shape[1]
-        streaming_offset = key_timesteps - query_timesteps
-
-        query_out = self.rotate(query, start + streaming_offset)
-        key_out = self.rotate(key, start, invert_decay=True)
-
-        return query_out, key_out
diff --git a/spaces/ElainaFanBoy/MusicGen/audiocraft/quantization/core_vq.py b/spaces/ElainaFanBoy/MusicGen/audiocraft/quantization/core_vq.py
deleted file mode 100644
index e1896bb1788a945a1f7be6369abb255ecf72c7a0..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/MusicGen/audiocraft/quantization/core_vq.py
+++ /dev/null
@@ -1,400 +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 typing as tp
-
-from einops import rearrange, repeat
-import flashy
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-
-
-def exists(val: tp.Optional[tp.Any]) -> bool:
-    return val is not None
-
-
-def default(val: tp.Any, d: tp.Any) -> tp.Any:
-    return val if exists(val) else d
-
-
-def l2norm(t):
-    return F.normalize(t, p=2, dim=-1)
-
-
-def ema_inplace(moving_avg, new, decay: float):
-    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
-
-
-def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
-    return (x + epsilon) / (x.sum() + n_categories * epsilon)
-
-
-def uniform_init(*shape: int):
-    t = torch.empty(shape)
-    nn.init.kaiming_uniform_(t)
-    return t
-
-
-def sample_vectors(samples, num: int):
-    num_samples, device = samples.shape[0], samples.device
-
-    if num_samples >= num:
-        indices = torch.randperm(num_samples, device=device)[:num]
-    else:
-        indices = torch.randint(0, num_samples, (num,), device=device)
-
-    return samples[indices]
-
-
-def kmeans(samples, num_clusters: int, num_iters: int = 10):
-    dim, dtype = samples.shape[-1], samples.dtype
-
-    means = sample_vectors(samples, num_clusters)
-
-    for _ in range(num_iters):
-        diffs = rearrange(samples, "n d -> n () d") - rearrange(
-            means, "c d -> () c d"
-        )
-        dists = -(diffs ** 2).sum(dim=-1)
-
-        buckets = dists.max(dim=-1).indices
-        bins = torch.bincount(buckets, minlength=num_clusters)
-        zero_mask = bins == 0
-        bins_min_clamped = bins.masked_fill(zero_mask, 1)
-
-        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
-        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
-        new_means = new_means / bins_min_clamped[..., None]
-
-        means = torch.where(zero_mask[..., None], means, new_means)
-
-    return means, bins
-
-
-def orthgonal_loss_fn(t):
-    # eq (2) from https://arxiv.org/abs/2112.00384
-    n = t.shape[0]
-    normed_codes = l2norm(t)
-    identity = torch.eye(n, device=t.device)
-    cosine_sim = einsum("i d, j d -> i j", normed_codes, normed_codes)
-    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)
-
-
-class EuclideanCodebook(nn.Module):
-    """Codebook with Euclidean distance.
-
-    Args:
-        dim (int): Dimension.
-        codebook_size (int): Codebook size.
-        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
-            If set to true, run the k-means algorithm on the first training batch and use
-            the learned centroids as initialization.
-        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        kmeans_init: int = False,
-        kmeans_iters: int = 10,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        threshold_ema_dead_code: int = 2,
-    ):
-        super().__init__()
-        self.decay = decay
-        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
-        embed = init_fn(codebook_size, dim)
-
-        self.codebook_size = codebook_size
-
-        self.kmeans_iters = kmeans_iters
-        self.epsilon = epsilon
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-
-        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
-        self.register_buffer("cluster_size", torch.zeros(codebook_size))
-        self.register_buffer("embed", embed)
-        self.register_buffer("embed_avg", embed.clone())
-
-    @torch.jit.ignore
-    def init_embed_(self, data):
-        if self.inited:
-            return
-
-        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
-        self.embed.data.copy_(embed)
-        self.embed_avg.data.copy_(embed.clone())
-        self.cluster_size.data.copy_(cluster_size)
-        self.inited.data.copy_(torch.Tensor([True]))
-        # Make sure all buffers across workers are in sync after initialization
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def replace_(self, samples, mask):
-        modified_codebook = torch.where(
-            mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
-        )
-        self.embed.data.copy_(modified_codebook)
-
-    def expire_codes_(self, batch_samples):
-        if self.threshold_ema_dead_code == 0:
-            return
-
-        expired_codes = self.cluster_size < self.threshold_ema_dead_code
-        if not torch.any(expired_codes):
-            return
-
-        batch_samples = rearrange(batch_samples, "... d -> (...) d")
-        self.replace_(batch_samples, mask=expired_codes)
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def preprocess(self, x):
-        x = rearrange(x, "... d -> (...) d")
-        return x
-
-    def quantize(self, x):
-        embed = self.embed.t()
-        dist = -(
-            x.pow(2).sum(1, keepdim=True)
-            - 2 * x @ embed
-            + embed.pow(2).sum(0, keepdim=True)
-        )
-        embed_ind = dist.max(dim=-1).indices
-        return embed_ind
-
-    def postprocess_emb(self, embed_ind, shape):
-        return embed_ind.view(*shape[:-1])
-
-    def dequantize(self, embed_ind):
-        quantize = F.embedding(embed_ind, self.embed)
-        return quantize
-
-    def encode(self, x):
-        shape = x.shape
-        # pre-process
-        x = self.preprocess(x)
-        # quantize
-        embed_ind = self.quantize(x)
-        # post-process
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        return embed_ind
-
-    def decode(self, embed_ind):
-        quantize = self.dequantize(embed_ind)
-        return quantize
-
-    def forward(self, x):
-        shape, dtype = x.shape, x.dtype
-        x = self.preprocess(x)
-        self.init_embed_(x)
-
-        embed_ind = self.quantize(x)
-        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        quantize = self.dequantize(embed_ind)
-
-        if self.training:
-            # We do the expiry of code at that point as buffers are in sync
-            # and all the workers will take the same decision.
-            self.expire_codes_(x)
-            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
-            embed_sum = x.t() @ embed_onehot
-            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
-            cluster_size = (
-                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
-                * self.cluster_size.sum()
-            )
-            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
-            self.embed.data.copy_(embed_normalized)
-
-        return quantize, embed_ind
-
-
-class VectorQuantization(nn.Module):
-    """Vector quantization implementation.
-    Currently supports only euclidean distance.
-
-    Args:
-        dim (int): Dimension
-        codebook_size (int): Codebook size
-        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int):
-        channels_last (bool): Channels are the last dimension in the input tensors.
-        commitment_weight (float): Weight for commitment loss.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider
-            for orthogonal regulariation.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        codebook_dim: tp.Optional[int] = None,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        kmeans_init: bool = False,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        channels_last: bool = False,
-        commitment_weight: float = 1.,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        _codebook_dim: int = default(codebook_dim, dim)
-
-        requires_projection = _codebook_dim != dim
-        self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity())
-        self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity())
-
-        self.epsilon = epsilon
-        self.commitment_weight = commitment_weight
-
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-
-        self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
-                                           kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
-                                           decay=decay, epsilon=epsilon,
-                                           threshold_ema_dead_code=threshold_ema_dead_code)
-        self.codebook_size = codebook_size
-
-        self.channels_last = channels_last
-
-    @property
-    def codebook(self):
-        return self._codebook.embed
-
-    @property
-    def inited(self):
-        return self._codebook.inited
-
-    def _preprocess(self, x):
-        if not self.channels_last:
-            x = rearrange(x, "b d n -> b n d")
-        return x
-
-    def _postprocess(self, quantize):
-        if not self.channels_last:
-            quantize = rearrange(quantize, "b n d -> b d n")
-        return quantize
-
-    def encode(self, x):
-        x = self._preprocess(x)
-        x = self.project_in(x)
-        embed_in = self._codebook.encode(x)
-        return embed_in
-
-    def decode(self, embed_ind):
-        quantize = self._codebook.decode(embed_ind)
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-        return quantize
-
-    def forward(self, x):
-        device = x.device
-        x = self._preprocess(x)
-
-        x = self.project_in(x)
-        quantize, embed_ind = self._codebook(x)
-
-        if self.training:
-            quantize = x + (quantize - x).detach()
-
-        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
-
-        if self.training:
-            if self.commitment_weight > 0:
-                commit_loss = F.mse_loss(quantize.detach(), x)
-                loss = loss + commit_loss * self.commitment_weight
-
-            if self.orthogonal_reg_weight > 0:
-                codebook = self.codebook
-
-                if self.orthogonal_reg_active_codes_only:
-                    # only calculate orthogonal loss for the activated codes for this batch
-                    unique_code_ids = torch.unique(embed_ind)
-                    codebook = codebook[unique_code_ids]
-
-                num_codes = codebook.shape[0]
-                if exists(self.orthogonal_reg_max_codes) and num_codes > self.orthogonal_reg_max_codes:
-                    rand_ids = torch.randperm(num_codes, device=device)[:self.orthogonal_reg_max_codes]
-                    codebook = codebook[rand_ids]
-
-                orthogonal_reg_loss = orthgonal_loss_fn(codebook)
-                loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight
-
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-
-        return quantize, embed_ind, loss
-
-
-class ResidualVectorQuantization(nn.Module):
-    """Residual vector quantization implementation.
-
-    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
-    """
-    def __init__(self, *, num_quantizers, **kwargs):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
-        )
-
-    def forward(self, x, n_q: tp.Optional[int] = None):
-        quantized_out = 0.0
-        residual = x
-
-        all_losses = []
-        all_indices = []
-
-        n_q = n_q or len(self.layers)
-
-        for i, layer in enumerate(self.layers[:n_q]):
-            quantized, indices, loss = layer(residual)
-            residual = residual - quantized
-            quantized_out = quantized_out + quantized
-            all_indices.append(indices)
-            all_losses.append(loss)
-
-        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
-        return quantized_out, out_indices, out_losses
-
-    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
-        residual = x
-        all_indices = []
-        n_q = n_q or len(self.layers)
-        for layer in self.layers[:n_q]:
-            indices = layer.encode(residual)
-            quantized = layer.decode(indices)
-            residual = residual - quantized
-            all_indices.append(indices)
-        out_indices = torch.stack(all_indices)
-        return out_indices
-
-    def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
-        quantized_out = torch.tensor(0.0, device=q_indices.device)
-        for i, indices in enumerate(q_indices):
-            layer = self.layers[i]
-            quantized = layer.decode(indices)
-            quantized_out = quantized_out + quantized
-        return quantized_out
diff --git a/spaces/EricaCorral/Chinese-Tools-Advanced/README.md b/spaces/EricaCorral/Chinese-Tools-Advanced/README.md
deleted file mode 100644
index 94fee45976eeb4451482de04cecf2d116a7177a6..0000000000000000000000000000000000000000
--- a/spaces/EricaCorral/Chinese-Tools-Advanced/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chinese Tools Advanced
-emoji: 🦀
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 2.8.11
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/EronSamez/RVC_HFmeu/get-pip.py b/spaces/EronSamez/RVC_HFmeu/get-pip.py
deleted file mode 100644
index cf68a2b3426decf01bde021a50c47a2115af303a..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/get-pip.py
+++ /dev/null
@@ -1,32657 +0,0 @@
-#!/usr/bin/env python
-#
-# Hi There!
-#
-# You may be wondering what this giant blob of binary data here is, you might
-# even be worried that we're up to something nefarious (good for you for being
-# paranoid!). This is a base85 encoding of a zip file, this zip file contains
-# an entire copy of pip (version 23.2.1).
-#
-# Pip is a thing that installs packages, pip itself is a package that someone
-# might want to install, especially if they're looking to run this get-pip.py
-# script. Pip has a lot of code to deal with the security of installing
-# packages, various edge cases on various platforms, and other such sort of
-# "tribal knowledge" that has been encoded in its code base. Because of this
-# we basically include an entire copy of pip inside this blob. We do this
-# because the alternatives are attempt to implement a "minipip" that probably
-# doesn't do things correctly and has weird edge cases, or compress pip itself
-# down into a single file.
-#
-# If you're wondering how this is created, it is generated using
-# `scripts/generate.py` in https://github.com/pypa/get-pip.
-
-import sys
-
-this_python = sys.version_info[:2]
-min_version = (3, 7)
-if this_python < min_version:
-    message_parts = [
-        "This script does not work on Python {}.{}".format(*this_python),
-        "The minimum supported Python version is {}.{}.".format(*min_version),
-        "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python),
-    ]
-    print("ERROR: " + " ".join(message_parts))
-    sys.exit(1)
-
-
-import os.path
-import pkgutil
-import shutil
-import tempfile
-import argparse
-import importlib
-from base64 import b85decode
-
-
-def include_setuptools(args):
-    """
-    Install setuptools only if absent and not excluded.
-    """
-    cli = not args.no_setuptools
-    env = not os.environ.get("PIP_NO_SETUPTOOLS")
-    absent = not importlib.util.find_spec("setuptools")
-    return cli and env and absent
-
-
-def include_wheel(args):
-    """
-    Install wheel only if absent and not excluded.
-    """
-    cli = not args.no_wheel
-    env = not os.environ.get("PIP_NO_WHEEL")
-    absent = not importlib.util.find_spec("wheel")
-    return cli and env and absent
-
-
-def determine_pip_install_arguments():
-    pre_parser = argparse.ArgumentParser()
-    pre_parser.add_argument("--no-setuptools", action="store_true")
-    pre_parser.add_argument("--no-wheel", action="store_true")
-    pre, args = pre_parser.parse_known_args()
-
-    args.append("pip")
-
-    if include_setuptools(pre):
-        args.append("setuptools")
-
-    if include_wheel(pre):
-        args.append("wheel")
-
-    return ["install", "--upgrade", "--force-reinstall"] + args
-
-
-def monkeypatch_for_cert(tmpdir):
-    """Patches `pip install` to provide default certificate with the lowest priority.
-
-    This ensures that the bundled certificates are used unless the user specifies a
-    custom cert via any of pip's option passing mechanisms (config, env-var, CLI).
-
-    A monkeypatch is the easiest way to achieve this, without messing too much with
-    the rest of pip's internals.
-    """
-    from pip._internal.commands.install import InstallCommand
-
-    # We want to be using the internal certificates.
-    cert_path = os.path.join(tmpdir, "cacert.pem")
-    with open(cert_path, "wb") as cert:
-        cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem"))
-
-    install_parse_args = InstallCommand.parse_args
-
-    def cert_parse_args(self, args):
-        if not self.parser.get_default_values().cert:
-            # There are no user provided cert -- force use of bundled cert
-            self.parser.defaults["cert"] = cert_path  # calculated above
-        return install_parse_args(self, args)
-
-    InstallCommand.parse_args = cert_parse_args
-
-
-def bootstrap(tmpdir):
-    monkeypatch_for_cert(tmpdir)
-
-    # Execute the included pip and use it to install the latest pip and
-    # setuptools from PyPI
-    from pip._internal.cli.main import main as pip_entry_point
-    args = determine_pip_install_arguments()
-    sys.exit(pip_entry_point(args))
-
-
-def main():
-    tmpdir = None
-    try:
-        # Create a temporary working directory
-        tmpdir = tempfile.mkdtemp()
-
-        # Unpack the zipfile into the temporary directory
-        pip_zip = os.path.join(tmpdir, "pip.zip")
-        with open(pip_zip, "wb") as fp:
-            fp.write(b85decode(DATA.replace(b"\n", b"")))
-
-        # Add the zipfile to sys.path so that we can import it
-        sys.path.insert(0, pip_zip)
-
-        # Run the bootstrap
-        bootstrap(tmpdir=tmpdir)
-    finally:
-        # Clean up our temporary working directory
-        if tmpdir:
-            shutil.rmtree(tmpdir, ignore_errors=True)
-
-
-DATA = b"""
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-P~IB=)A1pok}82|tw00000000000002CffckK0B~t=FJE?LZe(wAFLz~PWo~0{WNB_^b1!sdb98eqaC
-uNm0Rj{N6aWAK2ml;P_Ey^l{MMxe007ev001fg0000000000006du4Z0oxaA|NaUv_0~WN&gWcV%K_Z
-ewp`X>Mn8FL+;db7gX0WMyV)Ze?UHaCuNm1qJ{B008s>^a044008>F9smFU
-"""
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Fcjs/stablediffusionapi-lob-realvisxl-v20/README.md b/spaces/Fcjs/stablediffusionapi-lob-realvisxl-v20/README.md
deleted file mode 100644
index 4c6e318d9f0697fe4818094fbcc3ee007bda8e1c..0000000000000000000000000000000000000000
--- a/spaces/Fcjs/stablediffusionapi-lob-realvisxl-v20/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stablediffusionapi Lob Realvisxl V20
-emoji: 🌍
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/ops/__init__.py b/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/ops/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/FlippFuzz/whisper-webui/src/vad.py b/spaces/FlippFuzz/whisper-webui/src/vad.py
deleted file mode 100644
index 8b443aca4758dc835526d20e4c4000f834593c39..0000000000000000000000000000000000000000
--- a/spaces/FlippFuzz/whisper-webui/src/vad.py
+++ /dev/null
@@ -1,564 +0,0 @@
-from abc import ABC, abstractmethod
-from collections import Counter, deque
-import time
-
-from typing import Any, Deque, Iterator, List, Dict
-
-from pprint import pprint
-from src.hooks.progressListener import ProgressListener
-from src.hooks.subTaskProgressListener import SubTaskProgressListener
-from src.hooks.whisperProgressHook import create_progress_listener_handle
-from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache
-
-from src.segments import merge_timestamps
-from src.whisper.abstractWhisperContainer import AbstractWhisperCallback
-
-# Workaround for https://github.com/tensorflow/tensorflow/issues/48797
-try:
-    import tensorflow as tf
-except ModuleNotFoundError:
-    # Error handling
-    pass
-
-import torch
-
-import ffmpeg
-import numpy as np
-
-from src.utils import format_timestamp
-from enum import Enum
-
-class NonSpeechStrategy(Enum):
-    """
-    Ignore non-speech frames segments.
-    """
-    SKIP = 1
-    """
-    Just treat non-speech segments as speech.
-    """
-    CREATE_SEGMENT = 2
-    """
-    Expand speech segments into subsequent non-speech segments.
-    """
-    EXPAND_SEGMENT = 3
-
-# Defaults for Silero
-SPEECH_TRESHOLD = 0.3
-
-# Minimum size of segments to process
-MIN_SEGMENT_DURATION = 1
-
-# The maximum time for texts from old segments to be used in the next segment 
-MAX_PROMPT_WINDOW = 0 # seconds (0 = disabled)
-PROMPT_NO_SPEECH_PROB = 0.1 # Do not pass the text from segments with a no speech probability higher than this
-
-VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio
-
-class TranscriptionConfig(ABC):
-    def __init__(self, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
-                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
-                       max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
-        self.non_speech_strategy = non_speech_strategy
-        self.segment_padding_left = segment_padding_left
-        self.segment_padding_right = segment_padding_right
-        self.max_silent_period = max_silent_period
-        self.max_merge_size = max_merge_size
-        self.max_prompt_window = max_prompt_window
-        self.initial_segment_index = initial_segment_index
-
-class PeriodicTranscriptionConfig(TranscriptionConfig):
-    def __init__(self, periodic_duration: float, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
-                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
-                       max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
-        super().__init__(non_speech_strategy, segment_padding_left, segment_padding_right, max_silent_period, max_merge_size, max_prompt_window, initial_segment_index)
-        self.periodic_duration = periodic_duration
-
-class AbstractTranscription(ABC):
-    def __init__(self, sampling_rate: int = 16000):
-        self.sampling_rate = sampling_rate
-
-    def get_audio_segment(self, str, start_time: str = None, duration: str = None):
-        return load_audio(str, self.sampling_rate, start_time, duration)
-
-    def is_transcribe_timestamps_fast(self):
-        """
-        Determine if get_transcribe_timestamps is fast enough to not need parallelization.
-        """
-        return False
-
-    @abstractmethod
-    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
-        """
-        Get the start and end timestamps of the sections that should be transcribed by this VAD method.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file.
-        config: TranscriptionConfig
-            The transcription configuration.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-        return 
-
-    def get_merged_timestamps(self, timestamps: List[Dict[str, Any]], config: TranscriptionConfig, total_duration: float):
-        """
-        Get the start and end timestamps of the sections that should be transcribed by this VAD method,
-        after merging the given segments using the specified configuration.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file. 
-        config: TranscriptionConfig
-            The transcription configuration.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-        merged = merge_timestamps(timestamps, config.max_silent_period, config.max_merge_size, 
-                                  config.segment_padding_left, config.segment_padding_right)
-
-        if config.non_speech_strategy != NonSpeechStrategy.SKIP:
-            # Expand segments to include the gaps between them
-            if (config.non_speech_strategy == NonSpeechStrategy.CREATE_SEGMENT):
-                # When we have a prompt window, we create speech segments betwen each segment if we exceed the merge size
-                merged = self.fill_gaps(merged, total_duration=total_duration, max_expand_size=config.max_merge_size)
-            elif config.non_speech_strategy == NonSpeechStrategy.EXPAND_SEGMENT: 
-                # With no prompt window, it is better to just expand the segments (this effectively passes the prompt to the next segment)
-                merged = self.expand_gaps(merged, total_duration=total_duration)
-            else:
-                raise Exception("Unknown non-speech strategy: " + str(config.non_speech_strategy))
-
-            print("Transcribing non-speech:")
-            pprint(merged)
-        return merged
-
-    def transcribe(self, audio: str, whisperCallable: AbstractWhisperCallback, config: TranscriptionConfig, 
-                   progressListener: ProgressListener = None):
-        """
-        Transcribe the given audo file.
-
-        Parameters
-        ----------
-        audio: str
-            The audio file.
-        whisperCallable: WhisperCallback
-            A callback object to call to transcribe each segment.
-
-        Returns
-        -------
-        A list of start and end timestamps, in fractional seconds.
-        """
-
-        try:
-            max_audio_duration = self.get_audio_duration(audio, config)
-            timestamp_segments = self.get_transcribe_timestamps(audio, config, 0, max_audio_duration)
-
-            # Get speech timestamps from full audio file
-            merged = self.get_merged_timestamps(timestamp_segments, config, max_audio_duration)
-
-            # A deque of transcribed segments that is passed to the next segment as a prompt
-            prompt_window = deque()
-
-            print("Processing timestamps:")
-            pprint(merged)
-
-            result = {
-                'text': "",
-                'segments': [],
-                'language': ""
-            }
-            languageCounter = Counter()
-            detected_language = None
-
-            segment_index = config.initial_segment_index
-
-            # Calculate progress 
-            progress_start_offset = merged[0]['start'] if len(merged) > 0 else 0
-            progress_total_duration = sum([segment['end'] - segment['start'] for segment in merged])
-
-            # For each time segment, run whisper
-            for segment in merged:
-                segment_index += 1
-                segment_start = segment['start']
-                segment_end = segment['end']
-                segment_expand_amount = segment.get('expand_amount', 0)
-                segment_gap = segment.get('gap', False)
-
-                segment_duration = segment_end - segment_start
-
-                if segment_duration < MIN_SEGMENT_DURATION:
-                    continue
-
-                # Audio to run on Whisper
-                segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration))
-                # Previous segments to use as a prompt
-                segment_prompt = ' '.join([segment['text'] for segment in prompt_window]) if len(prompt_window) > 0 else None
-        
-                # Detected language
-                detected_language = languageCounter.most_common(1)[0][0] if len(languageCounter) > 0 else None
-
-                print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", 
-                    segment_duration, "expanded: ", segment_expand_amount, "prompt: ", segment_prompt, "language: ", detected_language)
-
-                perf_start_time = time.perf_counter()
-
-                scaled_progress_listener = SubTaskProgressListener(progressListener, base_task_total=progress_total_duration, 
-                                                                   sub_task_start=segment_start - progress_start_offset, sub_task_total=segment_duration) 
-                segment_result = whisperCallable.invoke(segment_audio, segment_index, segment_prompt, detected_language, progress_listener=scaled_progress_listener)
-
-                perf_end_time = time.perf_counter()
-                print("Whisper took {} seconds".format(perf_end_time - perf_start_time))
-
-                adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration)
-
-                # Propagate expand amount to the segments
-                if (segment_expand_amount > 0):
-                    segment_without_expansion = segment_duration - segment_expand_amount
-
-                    for adjusted_segment in adjusted_segments:
-                        adjusted_segment_end = adjusted_segment['end']
-
-                        # Add expand amount if the segment got expanded
-                        if (adjusted_segment_end > segment_without_expansion):
-                            adjusted_segment["expand_amount"] = adjusted_segment_end - segment_without_expansion
-
-                # Append to output
-                result['text'] += segment_result['text']
-                result['segments'].extend(adjusted_segments)
-
-                # Increment detected language
-                if not segment_gap:
-                    languageCounter[segment_result['language']] += 1
-
-                # Update prompt window
-                self.__update_prompt_window(prompt_window, adjusted_segments, segment_end, segment_gap, config)
-                
-            if detected_language is not None:
-                result['language'] = detected_language
-        finally:
-            # Notify progress listener that we are done
-            if progressListener is not None:
-                progressListener.on_finished()
-        return result
-    
-    def get_audio_duration(self, audio: str, config: TranscriptionConfig):
-        return get_audio_duration(audio)
-
-    def __update_prompt_window(self, prompt_window: Deque, adjusted_segments: List, segment_end: float, segment_gap: bool, config: TranscriptionConfig):
-        if (config.max_prompt_window is not None and config.max_prompt_window > 0):
-            # Add segments to the current prompt window (unless it is a speech gap)
-            if not segment_gap:
-                for segment in adjusted_segments:
-                    if segment.get('no_speech_prob', 0) <= PROMPT_NO_SPEECH_PROB:
-                        prompt_window.append(segment)
-
-            while (len(prompt_window) > 0):
-                first_end_time = prompt_window[0].get('end', 0)
-                # Time expanded in the segments should be discounted from the prompt window
-                first_expand_time = prompt_window[0].get('expand_amount', 0)
-
-                if (first_end_time - first_expand_time < segment_end - config.max_prompt_window):
-                    prompt_window.popleft()
-                else:
-                    break
-
-    def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float):
-        result = []
-        last_end_time = 0
-
-        for segment in segments:
-            segment_start = float(segment['start'])
-            segment_end = float(segment['end'])
-
-            if (last_end_time != segment_start):
-                delta = segment_start - last_end_time
-
-                if (min_gap_length is None or delta >= min_gap_length):
-                    result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } )
-            
-            last_end_time = segment_end
-            result.append(segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None and last_end_time < total_duration):
-            delta = total_duration - segment_start
-
-            if (min_gap_length is None or delta >= min_gap_length):
-                result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } )
-
-        return result
-
-    # Expand the end time of each segment to the start of the next segment
-    def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float):
-        result = []
-
-        if len(segments) == 0:
-            return result
-
-        # Add gap at the beginning if needed
-        if (segments[0]['start'] > 0):
-            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
-        for i in range(len(segments) - 1):
-            current_segment = segments[i]
-            next_segment = segments[i + 1]
-
-            delta = next_segment['start'] - current_segment['end']
-
-            # Expand if the gap actually exists
-            if (delta >= 0):
-                current_segment = current_segment.copy()
-                current_segment['expand_amount'] = delta
-                current_segment['end'] = next_segment['start']
-            
-            result.append(current_segment)
-
-        # Add last segment
-        last_segment = segments[-1]
-        result.append(last_segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None):
-            last_segment = result[-1]
-
-            if (last_segment['end'] < total_duration):
-                last_segment = last_segment.copy()
-                last_segment['end'] = total_duration
-                result[-1] = last_segment
-
-        return result
-
-    def fill_gaps(self, segments: List[Dict[str, Any]], total_duration: float, max_expand_size: float = None):
-        result = []
-
-        if len(segments) == 0:
-            return result
-
-        # Add gap at the beginning if needed
-        if (segments[0]['start'] > 0):
-            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
-        for i in range(len(segments) - 1):
-            expanded = False
-            current_segment = segments[i]
-            next_segment = segments[i + 1]
-
-            delta = next_segment['start'] - current_segment['end']
-
-            if (max_expand_size is not None and delta <= max_expand_size):
-                # Just expand the current segment
-                current_segment = current_segment.copy()
-                current_segment['expand_amount'] = delta
-                current_segment['end'] = next_segment['start']
-                expanded = True
-
-            result.append(current_segment)
-
-            # Add a gap to the next segment if needed
-            if (delta >= 0 and not expanded):
-                result.append({ 'start': current_segment['end'], 'end': next_segment['start'], 'gap': True } )
-            
-        # Add last segment
-        last_segment = segments[-1]
-        result.append(last_segment)
-
-        # Also include total duration if specified
-        if (total_duration is not None):
-            last_segment = result[-1]
-
-            delta = total_duration - last_segment['end']
-
-            if (delta > 0):
-                if (max_expand_size is not None and delta <= max_expand_size):
-                    # Expand the last segment
-                    last_segment = last_segment.copy()
-                    last_segment['expand_amount'] = delta
-                    last_segment['end'] = total_duration
-                    result[-1] = last_segment
-                else:
-                    result.append({ 'start': last_segment['end'], 'end': total_duration, 'gap': True } )
-
-        return result
-
-    def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None):
-        result = []
-
-        for segment in segments:
-            segment_start = float(segment['start'])
-            segment_end = float(segment['end'])
-
-            # Filter segments?
-            if (max_source_time is not None):
-                if (segment_start > max_source_time):
-                    print("Dropped {} because segment_start > max_source_time".format(format_timestamp(segment_start, True)))
-                    continue
-                if (segment_end > max_source_time):
-                    print("Warning. segment_end > max_source_time {}".format(format_timestamp(segment_end, True)))
-                    continue
-                segment_end = min(max_source_time, segment_end)
-
-                new_segment = segment.copy()
-
-            # Add to start and end
-            new_segment['start'] = segment_start + adjust_seconds
-            new_segment['end'] = segment_end + adjust_seconds
-            result.append(new_segment)
-        return result
-
-    def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float):
-        result = []
-
-        for entry in timestamps:
-            start = entry['start']
-            end = entry['end']
-
-            result.append({
-                'start': start * factor,
-                'end': end * factor
-            })
-        return result
-
-
-class VadSileroTranscription(AbstractTranscription):
-    def __init__(self, sampling_rate: int = 16000, cache: ModelCache = None):
-        super().__init__(sampling_rate=sampling_rate)
-        self.model = None
-        self.cache = cache
-        self._initialize_model()
-
-    def _initialize_model(self):
-        if (self.cache is not None):
-            model_key = "VadSileroTranscription"
-            self.model, self.get_speech_timestamps = self.cache.get(model_key, self._create_model)
-            print("Loaded Silerio model from cache.")
-        else:
-            self.model, self.get_speech_timestamps = self._create_model()
-            print("Created Silerio model")
-
-    def _create_model(self):
-        model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
-        
-        # Silero does not benefit from multi-threading
-        torch.set_num_threads(1) # JIT
-        (get_speech_timestamps, _, _, _, _) = utils
-
-        return model, get_speech_timestamps
-
-    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
-        result = []
-
-        print("Getting timestamps from audio file: {}, start: {}, duration: {}".format(audio, start_time, end_time))
-        perf_start_time = time.perf_counter()
-
-        # Divide procesisng of audio into chunks
-        chunk_start = start_time
-
-        while (chunk_start < end_time):
-            chunk_duration = min(end_time - chunk_start, VAD_MAX_PROCESSING_CHUNK)
-
-            print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration)))
-            wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration))
-
-            sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD)
-            seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate) 
-            adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration)
-
-            #pprint(adjusted)
-
-            result.extend(adjusted)
-            chunk_start += chunk_duration
-
-        perf_end_time = time.perf_counter()
-        print("VAD processing took {} seconds".format(perf_end_time - perf_start_time))
-
-        return result
-
-    def __getstate__(self):
-        # We only need the sampling rate
-        return { 'sampling_rate': self.sampling_rate }
-
-    def __setstate__(self, state):
-        self.sampling_rate = state['sampling_rate']
-        self.model = None
-        # Use the global cache
-        self.cache = GLOBAL_MODEL_CACHE
-        self._initialize_model()
-
-# A very simple VAD that just marks every N seconds as speech
-class VadPeriodicTranscription(AbstractTranscription):
-    def __init__(self, sampling_rate: int = 16000):
-        super().__init__(sampling_rate=sampling_rate)
-
-    def is_transcribe_timestamps_fast(self):
-        # This is a very fast VAD - no need to parallelize it
-        return True
-
-    def get_transcribe_timestamps(self, audio: str, config: PeriodicTranscriptionConfig, start_time: float, end_time: float):
-        result = []
-
-        # Generate a timestamp every N seconds
-        start_timestamp = start_time
-
-        while (start_timestamp < end_time):
-            end_timestamp = min(start_timestamp + config.periodic_duration, end_time)
-            segment_duration = end_timestamp - start_timestamp
-
-            # Minimum duration is 1 second
-            if (segment_duration >= 1):
-                result.append( {  'start': start_timestamp, 'end': end_timestamp } )
-
-            start_timestamp = end_timestamp
-
-        return result
-
-def get_audio_duration(file: str):
-    return float(ffmpeg.probe(file)["format"]["duration"])
-
-def load_audio(file: str, sample_rate: int = 16000, 
-               start_time: str = None, duration: str = None):
-    """
-    Open an audio file and read as mono waveform, resampling as necessary
-
-    Parameters
-    ----------
-    file: str
-        The audio file to open
-
-    sr: int
-        The sample rate to resample the audio if necessary
-
-    start_time: str
-        The start time, using the standard FFMPEG time duration syntax, or None to disable.
-    
-    duration: str
-        The duration, using the standard FFMPEG time duration syntax, or None to disable.
-
-    Returns
-    -------
-    A NumPy array containing the audio waveform, in float32 dtype.
-    """
-    try:
-        inputArgs = {'threads': 0}
-
-        if (start_time is not None):
-            inputArgs['ss'] = start_time
-        if (duration is not None):
-            inputArgs['t'] = duration
-
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        out, _ = (
-            ffmpeg.input(file, **inputArgs)
-            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate)
-            .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True)
-        )
-    except ffmpeg.Error as e:
-        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}")
-
-    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/HubertSoft.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/HubertSoft.py
deleted file mode 100644
index e540775d9b6336953ab8642fa424a5e7e3e38c3f..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/HubertSoft.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from vencoder.encoder import SpeechEncoder
-import torch
-from vencoder.hubert import hubert_model
-class HubertSoft(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/hubert-soft-0d54a1f4.pt",device=None):
-        print("load model(s) from {}".format(vec_path))
-        hubert_soft = hubert_model.hubert_soft(vec_path)
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.hidden_dim = 256
-        self.model = hubert_soft.to(self.dev)
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats[None,None,:]
-        with torch.inference_mode():
-          units = self.model.units(feats)
-          return units.transpose(1,2)
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-pcr/transforms.py b/spaces/FrankZxShen/vits-fast-finetuning-pcr/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-pcr/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/Fredithefish/PixelRevive/app.py b/spaces/Fredithefish/PixelRevive/app.py
deleted file mode 100644
index 8ee095f2328fc7f1d9470644843cb0098c5e5f82..0000000000000000000000000000000000000000
--- a/spaces/Fredithefish/PixelRevive/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-import cv2
-from PIL import Image
-import numpy as np
-
-def upscale_image(input_image, scale_factor):
-    image = Image.fromarray(input_image)
-
-    width, height = image.size
-    new_width = int(width * scale_factor)
-    new_height = int(height * scale_factor)
-
-    resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-    cv_image = cv2.cvtColor(np.array(resized_image), cv2.COLOR_RGB2BGR)
-    denoised_image = cv2.fastNlMeansDenoisingColored(cv_image, None, 10, 10, 7, 21)
-    denoised_pil_image = Image.fromarray(cv2.cvtColor(denoised_image, cv2.COLOR_BGR2RGB))
-    return denoised_pil_image
-
-def upscale_image_interface(input_image, scale_factor):
-    input_image_np = np.array(input_image)
-    output_image = upscale_image(input_image_np, scale_factor)
-    return output_image
-
-image_input = gr.inputs.Image(label="Input Image", type="pil")
-scale_factor_input = gr.inputs.Number(label="Scale Factor", default=2)
-output = gr.outputs.Image(label="Output Image", type="pil")
-
-title = "Image Upscaler"
-description = "Upscale an image using a scale factor."
-gr_interface = gr.Interface(fn=upscale_image_interface, inputs=[image_input, scale_factor_input], outputs=output, title=title, description=description)
-
-if __name__ == "__main__":
-    gr_interface.launch()
diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py b/spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
deleted file mode 100644
index f9664fb1f89ef068e923211179e1c7e1ce7fdbd2..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import numpy as np
-import pyworld
-
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-
-
-class HarvestF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.hop_length,
-            f0_ceil=self.f0_max,
-            f0_floor=self.f0_min,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/GT4SD/paccmann_rl/utils.py b/spaces/GT4SD/paccmann_rl/utils.py
deleted file mode 100644
index 34a817843a959d6ef46ae527efd08788f55a3196..0000000000000000000000000000000000000000
--- a/spaces/GT4SD/paccmann_rl/utils.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import logging
-from collections import defaultdict
-from typing import List
-
-import mols2grid
-import pandas as pd
-
-logger = logging.getLogger(__name__)
-logger.addHandler(logging.NullHandler())
-
-
-def draw_grid_generate(
-    samples: List[str],
-    seeds: List[str] = [],
-    n_cols: int = 3,
-    size=(140, 200),
-) -> str:
-    """
-    Uses mols2grid to draw a HTML grid for the generated molecules
-
-    Args:
-        samples: The generated samples.
-        n_cols: Number of columns in grid. Defaults to 5.
-        size: Size of molecule in grid. Defaults to (140, 200).
-
-    Returns:
-        HTML to display
-    """
-
-    result = defaultdict(list)
-    result.update(
-        {
-            "SMILES": seeds + samples,
-            "Name": [f"Seed_{i}" for i in range(len(seeds))]
-            + [f"Generated_{i}" for i in range(len(samples))],
-        },
-    )
-
-    result_df = pd.DataFrame(result)
-    obj = mols2grid.display(
-        result_df,
-        tooltip=list(result.keys()),
-        height=1100,
-        n_cols=n_cols,
-        name="Results",
-        size=size,
-    )
-    return obj.data
diff --git "a/spaces/Gmq-x/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py" "b/spaces/Gmq-x/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py"
deleted file mode 100644
index 2f4201438c4d8597c251726fe99c02d40f0cadf0..0000000000000000000000000000000000000000
--- "a/spaces/Gmq-x/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py"
+++ /dev/null
@@ -1,166 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-import re
-import unicodedata
-fast_debug = False
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-def is_paragraph_break(match):
-    """
-    根据给定的匹配结果来判断换行符是否表示段落分隔。
-    如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
-    也可以根据之前的内容长度来判断段落是否已经足够长。
-    """
-    prev_char, next_char = match.groups()
-
-    # 句子结束标志
-    sentence_endings = ".!?"
-
-    # 设定一个最小段落长度阈值
-    min_paragraph_length = 140
-
-    if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
-        return "\n\n" 
-    else:
-        return " "
-
-def normalize_text(text):
-    """
-    通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
-    例如，将连字 "fi" 转换为 "f" 和 "i"。
-    """
-    # 对文本进行归一化处理，分解连字
-    normalized_text = unicodedata.normalize("NFKD", text)
-
-    # 替换其他特殊字符
-    cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
-
-    return cleaned_text
-
-def clean_text(raw_text):
-    """
-    对从 PDF 提取出的原始文本进行清洗和格式化处理。
-    1. 对原始文本进行归一化处理。
-    2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
-    3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
-    """
-    # 对文本进行归一化处理
-    normalized_text = normalize_text(raw_text)
-
-    # 替换跨行的连词
-    text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
-
-    # 根据前后相邻字符的特点，找到原文本中的换行符
-    newlines = re.compile(r'(\S)\n(\S)')
-
-    # 根据 heuristic 规则，用空格或段落分隔符替换原换行符
-    final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
-
-    return final_text.strip()
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os, fitz
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with fitz.open(fp) as doc:
-            file_content = ""
-            for page in doc:
-                file_content += page.get_text()
-            file_content = clean_text(file_content)
-            print(file_content)
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )  # 带超时倒计时
-                
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, 
-            inputs_show_user=i_say, 
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, 
-            history=history,
-            sys_prompt="总结文章。"
-        )  # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-@CatchException
-def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/Waifu2x/model_check_points/CRAN_V2/ReadME.md b/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/Waifu2x/model_check_points/CRAN_V2/ReadME.md
deleted file mode 100644
index e432a1c7bd5de45f086ba1fd6b06ca712a1d806b..0000000000000000000000000000000000000000
--- a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/Waifu2x/model_check_points/CRAN_V2/ReadME.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Resume & Use Model Check Points 
-
-This folder contains check points for models and their weights.  They are generated from [PyTorch's pickle](https://pytorch.org/docs/master/notes/serialization.html). 
-
-Model specifications are in each folder's ReadME.  
-
-Pickle names with "model" contain the entire models, and they can be used as an freeze module by calling the "forward_checkpoint" function to generate images. 
-
-Example:
-```python
-import torch
-# No need to reconstruct the model 
-model = torch.load("./DCSCN/DCSCN_model_387epos_L12_noise_1.pt")
-x = torch.randn((1,3,10,10)), torch.randn((1,3,20,20))
-out = model.forward_checkpoint(a)
-```
-
-Pickle names with "weights" are model weights, and they are named dictionaries. 
-
-Example:
-```python
-model = DCSCN(*)    # the setting must be the same to load check points weights. 
-model.load_state_dict(torch.load("./DCSCN/DCSCN_weights_387epos_L12_noise_1.pt"))
-# then you can resume the model training 
-```
-
-Model check poins in Upconv_7 and vgg_7 are from [waifu2x's repo](https://github.com/nagadomi/waifu2x/tree/master/models). To load weights into a model, please use ```load_pre_train_weights``` function. 
-
-Example: 
-```python
-model = UpConv_7()
-model.load_pre_train_weights(json_file=...)
-# then the model is ready to use 
-```
diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/folding.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/folding.py
deleted file mode 100644
index 1faf5bd58377880107da119b4b65c96a2f1e728d..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/model/folding.py
+++ /dev/null
@@ -1,1009 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# 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.
-
-"""Modules and utilities for the structure module."""
-
-import functools
-from typing import Dict
-from alphafold.common import residue_constants
-from alphafold.model import all_atom
-from alphafold.model import common_modules
-from alphafold.model import prng
-from alphafold.model import quat_affine
-from alphafold.model import r3
-from alphafold.model import utils
-import haiku as hk
-import jax
-import jax.numpy as jnp
-import ml_collections
-import numpy as np
-
-
-def squared_difference(x, y):
-  return jnp.square(x - y)
-
-
-class InvariantPointAttention(hk.Module):
-  """Invariant Point attention module.
-
-  The high-level idea is that this attention module works over a set of points
-  and associated orientations in 3D space (e.g. protein residues).
-
-  Each residue outputs a set of queries and keys as points in their local
-  reference frame.  The attention is then defined as the euclidean distance
-  between the queries and keys in the global frame.
-
-  Jumper et al. (2021) Suppl. Alg. 22 "InvariantPointAttention"
-  """
-
-  def __init__(self,
-               config,
-               global_config,
-               dist_epsilon=1e-8,
-               name='invariant_point_attention'):
-    """Initialize.
-
-    Args:
-      config: Structure Module Config
-      global_config: Global Config of Model.
-      dist_epsilon: Small value to avoid NaN in distance calculation.
-      name: Haiku Module name.
-    """
-    super().__init__(name=name)
-
-    self._dist_epsilon = dist_epsilon
-    self._zero_initialize_last = global_config.zero_init
-
-    self.config = config
-
-    self.global_config = global_config
-
-  def __call__(self, inputs_1d, inputs_2d, mask, affine):
-    """Compute geometry-aware attention.
-
-    Given a set of query residues (defined by affines and associated scalar
-    features), this function computes geometry-aware attention between the
-    query residues and target residues.
-
-    The residues produce points in their local reference frame, which
-    are converted into the global frame in order to compute attention via
-    euclidean distance.
-
-    Equivalently, the target residues produce points in their local frame to be
-    used as attention values, which are converted into the query residues'
-    local frames.
-
-    Args:
-      inputs_1d: (N, C) 1D input embedding that is the basis for the
-        scalar queries.
-      inputs_2d: (N, M, C') 2D input embedding, used for biases and values.
-      mask: (N, 1) mask to indicate which elements of inputs_1d participate
-        in the attention.
-      affine: QuatAffine object describing the position and orientation of
-        every element in inputs_1d.
-
-    Returns:
-      Transformation of the input embedding.
-    """
-    num_residues, _ = inputs_1d.shape
-
-    # Improve readability by removing a large number of 'self's.
-    num_head = self.config.num_head
-    num_scalar_qk = self.config.num_scalar_qk
-    num_point_qk = self.config.num_point_qk
-    num_scalar_v = self.config.num_scalar_v
-    num_point_v = self.config.num_point_v
-    num_output = self.config.num_channel
-
-    assert num_scalar_qk > 0
-    assert num_point_qk > 0
-    assert num_point_v > 0
-
-    # Construct scalar queries of shape:
-    # [num_query_residues, num_head, num_points]
-    q_scalar = common_modules.Linear(
-        num_head * num_scalar_qk, name='q_scalar')(
-            inputs_1d)
-    q_scalar = jnp.reshape(
-        q_scalar, [num_residues, num_head, num_scalar_qk])
-
-    # Construct scalar keys/values of shape:
-    # [num_target_residues, num_head, num_points]
-    kv_scalar = common_modules.Linear(
-        num_head * (num_scalar_v + num_scalar_qk), name='kv_scalar')(
-            inputs_1d)
-    kv_scalar = jnp.reshape(kv_scalar,
-                            [num_residues, num_head,
-                             num_scalar_v + num_scalar_qk])
-    k_scalar, v_scalar = jnp.split(kv_scalar, [num_scalar_qk], axis=-1)
-
-    # Construct query points of shape:
-    # [num_residues, num_head, num_point_qk]
-
-    # First construct query points in local frame.
-    q_point_local = common_modules.Linear(
-        num_head * 3 * num_point_qk, name='q_point_local')(
-            inputs_1d)
-    q_point_local = jnp.split(q_point_local, 3, axis=-1)
-    # Project query points into global frame.
-    q_point_global = affine.apply_to_point(q_point_local, extra_dims=1)
-    # Reshape query point for later use.
-    q_point = [
-        jnp.reshape(x, [num_residues, num_head, num_point_qk])
-        for x in q_point_global]
-
-    # Construct key and value points.
-    # Key points have shape [num_residues, num_head, num_point_qk]
-    # Value points have shape [num_residues, num_head, num_point_v]
-
-    # Construct key and value points in local frame.
-    kv_point_local = common_modules.Linear(
-        num_head * 3 * (num_point_qk + num_point_v), name='kv_point_local')(
-            inputs_1d)
-    kv_point_local = jnp.split(kv_point_local, 3, axis=-1)
-    # Project key and value points into global frame.
-    kv_point_global = affine.apply_to_point(kv_point_local, extra_dims=1)
-    kv_point_global = [
-        jnp.reshape(x, [num_residues,
-                        num_head, (num_point_qk + num_point_v)])
-        for x in kv_point_global]
-    # Split key and value points.
-    k_point, v_point = list(
-        zip(*[
-            jnp.split(x, [num_point_qk,], axis=-1)
-            for x in kv_point_global
-        ]))
-
-    # We assume that all queries and keys come iid from N(0, 1) distribution
-    # and compute the variances of the attention logits.
-    # Each scalar pair (q, k) contributes Var q*k = 1
-    scalar_variance = max(num_scalar_qk, 1) * 1.
-    # Each point pair (q, k) contributes Var [0.5 ||q||^2 - <q, k>] = 9 / 2
-    point_variance = max(num_point_qk, 1) * 9. / 2
-
-    # Allocate equal variance to scalar, point and attention 2d parts so that
-    # the sum is 1.
-
-    num_logit_terms = 3
-
-    scalar_weights = np.sqrt(1.0 / (num_logit_terms * scalar_variance))
-    point_weights = np.sqrt(1.0 / (num_logit_terms * point_variance))
-    attention_2d_weights = np.sqrt(1.0 / (num_logit_terms))
-
-    # Trainable per-head weights for points.
-    trainable_point_weights = jax.nn.softplus(hk.get_parameter(
-        'trainable_point_weights', shape=[num_head],
-        # softplus^{-1} (1)
-        init=hk.initializers.Constant(np.log(np.exp(1.) - 1.))))
-    point_weights *= jnp.expand_dims(trainable_point_weights, axis=1)
-
-    v_point = [jnp.swapaxes(x, -2, -3) for x in v_point]
-
-    q_point = [jnp.swapaxes(x, -2, -3) for x in q_point]
-    k_point = [jnp.swapaxes(x, -2, -3) for x in k_point]
-    dist2 = [
-        squared_difference(qx[:, :, None, :], kx[:, None, :, :])
-        for qx, kx in zip(q_point, k_point)
-    ]
-    dist2 = sum(dist2)
-    attn_qk_point = -0.5 * jnp.sum(
-        point_weights[:, None, None, :] * dist2, axis=-1)
-
-    v = jnp.swapaxes(v_scalar, -2, -3)
-    q = jnp.swapaxes(scalar_weights * q_scalar, -2, -3)
-    k = jnp.swapaxes(k_scalar, -2, -3)
-    attn_qk_scalar = jnp.matmul(q, jnp.swapaxes(k, -2, -1))
-    attn_logits = attn_qk_scalar + attn_qk_point
-
-    attention_2d = common_modules.Linear(
-        num_head, name='attention_2d')(
-            inputs_2d)
-
-    attention_2d = jnp.transpose(attention_2d, [2, 0, 1])
-    attention_2d = attention_2d_weights * attention_2d
-    attn_logits += attention_2d
-
-    mask_2d = mask * jnp.swapaxes(mask, -1, -2)
-    attn_logits -= 1e5 * (1. - mask_2d)
-
-    # [num_head, num_query_residues, num_target_residues]
-    attn = jax.nn.softmax(attn_logits)
-
-    # [num_head, num_query_residues, num_head * num_scalar_v]
-    result_scalar = jnp.matmul(attn, v)
-
-    # For point result, implement matmul manually so that it will be a float32
-    # on TPU.  This is equivalent to
-    # result_point_global = [jnp.einsum('bhqk,bhkc->bhqc', attn, vx)
-    #                        for vx in v_point]
-    # but on the TPU, doing the multiply and reduce_sum ensures the
-    # computation happens in float32 instead of bfloat16.
-    result_point_global = [jnp.sum(
-        attn[:, :, :, None] * vx[:, None, :, :],
-        axis=-2) for vx in v_point]
-
-    # [num_query_residues, num_head, num_head * num_(scalar|point)_v]
-    result_scalar = jnp.swapaxes(result_scalar, -2, -3)
-    result_point_global = [
-        jnp.swapaxes(x, -2, -3)
-        for x in result_point_global]
-
-    # Features used in the linear output projection. Should have the size
-    # [num_query_residues, ?]
-    output_features = []
-
-    result_scalar = jnp.reshape(
-        result_scalar, [num_residues, num_head * num_scalar_v])
-    output_features.append(result_scalar)
-
-    result_point_global = [
-        jnp.reshape(r, [num_residues, num_head * num_point_v])
-        for r in result_point_global]
-    result_point_local = affine.invert_point(result_point_global, extra_dims=1)
-    output_features.extend(result_point_local)
-
-    output_features.append(jnp.sqrt(self._dist_epsilon +
-                                    jnp.square(result_point_local[0]) +
-                                    jnp.square(result_point_local[1]) +
-                                    jnp.square(result_point_local[2])))
-
-    # Dimensions: h = heads, i and j = residues,
-    # c = inputs_2d channels
-    # Contraction happens over the second residue dimension, similarly to how
-    # the usual attention is performed.
-    result_attention_over_2d = jnp.einsum('hij, ijc->ihc', attn, inputs_2d)
-    num_out = num_head * result_attention_over_2d.shape[-1]
-    output_features.append(
-        jnp.reshape(result_attention_over_2d,
-                    [num_residues, num_out]))
-
-    final_init = 'zeros' if self._zero_initialize_last else 'linear'
-
-    final_act = jnp.concatenate(output_features, axis=-1)
-
-    return common_modules.Linear(
-        num_output,
-        initializer=final_init,
-        name='output_projection')(final_act)
-
-
-class FoldIteration(hk.Module):
-  """A single iteration of the main structure module loop.
-
-  Jumper et al. (2021) Suppl. Alg. 20 "StructureModule" lines 6-21
-
-  First, each residue attends to all residues using InvariantPointAttention.
-  Then, we apply transition layers to update the hidden representations.
-  Finally, we use the hidden representations to produce an update to the
-  affine of each residue.
-  """
-
-  def __init__(self, config, global_config,
-               name='fold_iteration'):
-    super().__init__(name=name)
-    self.config = config
-    self.global_config = global_config
-
-  def __call__(self,
-               activations,
-               sequence_mask,
-               update_affine,
-               is_training,
-               initial_act,
-               safe_key=None,
-               static_feat_2d=None,
-               aatype=None):
-    c = self.config
-
-    if safe_key is None:
-      safe_key = prng.SafeKey(hk.next_rng_key())
-
-    def safe_dropout_fn(tensor, safe_key):
-      return prng.safe_dropout(
-          tensor=tensor,
-          safe_key=safe_key,
-          rate=c.dropout,
-          is_deterministic=self.global_config.deterministic,
-          is_training=is_training)
-
-    affine = quat_affine.QuatAffine.from_tensor(activations['affine'])
-
-    act = activations['act']
-    attention_module = InvariantPointAttention(self.config, self.global_config)
-    # Attention
-    attn = attention_module(
-        inputs_1d=act,
-        inputs_2d=static_feat_2d,
-        mask=sequence_mask,
-        affine=affine)
-    act += attn
-    safe_key, *sub_keys = safe_key.split(3)
-    sub_keys = iter(sub_keys)
-    act = safe_dropout_fn(act, next(sub_keys))
-    act = hk.LayerNorm(
-        axis=[-1],
-        create_scale=True,
-        create_offset=True,
-        name='attention_layer_norm')(
-            act)
-
-    final_init = 'zeros' if self.global_config.zero_init else 'linear'
-
-    # Transition
-    input_act = act
-    for i in range(c.num_layer_in_transition):
-      init = 'relu' if i < c.num_layer_in_transition - 1 else final_init
-      act = common_modules.Linear(
-          c.num_channel,
-          initializer=init,
-          name='transition')(
-              act)
-      if i < c.num_layer_in_transition - 1:
-        act = jax.nn.relu(act)
-    act += input_act
-    act = safe_dropout_fn(act, next(sub_keys))
-    act = hk.LayerNorm(
-        axis=[-1],
-        create_scale=True,
-        create_offset=True,
-        name='transition_layer_norm')(act)
-
-    if update_affine:
-      # This block corresponds to
-      # Jumper et al. (2021) Alg. 23 "Backbone update"
-      affine_update_size = 6
-
-      # Affine update
-      affine_update = common_modules.Linear(
-          affine_update_size,
-          initializer=final_init,
-          name='affine_update')(
-              act)
-
-      affine = affine.pre_compose(affine_update)
-
-    sc = MultiRigidSidechain(c.sidechain, self.global_config)(
-        affine.scale_translation(c.position_scale), [act, initial_act], aatype)
-
-    outputs = {'affine': affine.to_tensor(), 'sc': sc}
-
-    affine = affine.apply_rotation_tensor_fn(jax.lax.stop_gradient)
-
-    new_activations = {
-        'act': act,
-        'affine': affine.to_tensor()
-    }
-    return new_activations, outputs
-
-
-def generate_affines(representations, batch, config, global_config,
-                     is_training, safe_key):
-  """Generate predicted affines for a single chain.
-
-  Jumper et al. (2021) Suppl. Alg. 20 "StructureModule"
-
-  This is the main part of the structure module - it iteratively applies
-  folding to produce a set of predicted residue positions.
-
-  Args:
-    representations: Representations dictionary.
-    batch: Batch dictionary.
-    config: Config for the structure module.
-    global_config: Global config.
-    is_training: Whether the model is being trained.
-    safe_key: A prng.SafeKey object that wraps a PRNG key.
-
-  Returns:
-    A dictionary containing residue affines and sidechain positions.
-  """
-  c = config
-  sequence_mask = batch['seq_mask'][:, None]
-
-  act = hk.LayerNorm(
-      axis=[-1],
-      create_scale=True,
-      create_offset=True,
-      name='single_layer_norm')(
-          representations['single'])
-
-  initial_act = act
-  act = common_modules.Linear(
-      c.num_channel, name='initial_projection')(
-          act)
-
-  affine = generate_new_affine(sequence_mask)
-
-  fold_iteration = FoldIteration(
-      c, global_config, name='fold_iteration')
-
-  assert len(batch['seq_mask'].shape) == 1
-
-  activations = {'act': act,
-                 'affine': affine.to_tensor(),
-                 }
-
-  act_2d = hk.LayerNorm(
-      axis=[-1],
-      create_scale=True,
-      create_offset=True,
-      name='pair_layer_norm')(
-          representations['pair'])
-
-  outputs = []
-  safe_keys = safe_key.split(c.num_layer)
-  for sub_key in safe_keys:
-    activations, output = fold_iteration(
-        activations,
-        initial_act=initial_act,
-        static_feat_2d=act_2d,
-        safe_key=sub_key,
-        sequence_mask=sequence_mask,
-        update_affine=True,
-        is_training=is_training,
-        aatype=batch['aatype'])
-    outputs.append(output)
-
-  output = jax.tree_map(lambda *x: jnp.stack(x), *outputs)
-  # Include the activations in the output dict for use by the LDDT-Head.
-  output['act'] = activations['act']
-
-  return output
-
-
-class StructureModule(hk.Module):
-  """StructureModule as a network head.
-
-  Jumper et al. (2021) Suppl. Alg. 20 "StructureModule"
-  """
-
-  def __init__(self, config, global_config, compute_loss=True,
-               name='structure_module'):
-    super().__init__(name=name)
-    self.config = config
-    self.global_config = global_config
-    self.compute_loss = compute_loss
-
-  def __call__(self, representations, batch, is_training,
-               safe_key=None):
-    c = self.config
-    ret = {}
-
-    if safe_key is None:
-      safe_key = prng.SafeKey(hk.next_rng_key())
-
-    output = generate_affines(
-        representations=representations,
-        batch=batch,
-        config=self.config,
-        global_config=self.global_config,
-        is_training=is_training,
-        safe_key=safe_key)
-
-    ret['representations'] = {'structure_module': output['act']}
-
-    ret['traj'] = output['affine'] * jnp.array([1.] * 4 +
-                                               [c.position_scale] * 3)
-
-    ret['sidechains'] = output['sc']
-
-    atom14_pred_positions = r3.vecs_to_tensor(output['sc']['atom_pos'])[-1]
-    ret['final_atom14_positions'] = atom14_pred_positions  # (N, 14, 3)
-    ret['final_atom14_mask'] = batch['atom14_atom_exists']  # (N, 14)
-
-    atom37_pred_positions = all_atom.atom14_to_atom37(atom14_pred_positions,
-                                                      batch)
-    atom37_pred_positions *= batch['atom37_atom_exists'][:, :, None]
-    ret['final_atom_positions'] = atom37_pred_positions  # (N, 37, 3)
-
-    ret['final_atom_mask'] = batch['atom37_atom_exists']  # (N, 37)
-    ret['final_affines'] = ret['traj'][-1]
-
-    if self.compute_loss:
-      return ret
-    else:
-      no_loss_features = ['final_atom_positions', 'final_atom_mask',
-                          'representations']
-      no_loss_ret = {k: ret[k] for k in no_loss_features}
-      return no_loss_ret
-
-  def loss(self, value, batch):
-    ret = {'loss': 0.}
-
-    ret['metrics'] = {}
-    # If requested, compute in-graph metrics.
-    if self.config.compute_in_graph_metrics:
-      atom14_pred_positions = value['final_atom14_positions']
-      # Compute renaming and violations.
-      value.update(compute_renamed_ground_truth(batch, atom14_pred_positions))
-      value['violations'] = find_structural_violations(
-          batch, atom14_pred_positions, self.config)
-
-      # Several violation metrics:
-      violation_metrics = compute_violation_metrics(
-          batch=batch,
-          atom14_pred_positions=atom14_pred_positions,
-          violations=value['violations'])
-      ret['metrics'].update(violation_metrics)
-
-    backbone_loss(ret, batch, value, self.config)
-
-    if 'renamed_atom14_gt_positions' not in value:
-      value.update(compute_renamed_ground_truth(
-          batch, value['final_atom14_positions']))
-    sc_loss = sidechain_loss(batch, value, self.config)
-
-    ret['loss'] = ((1 - self.config.sidechain.weight_frac) * ret['loss'] +
-                   self.config.sidechain.weight_frac * sc_loss['loss'])
-    ret['sidechain_fape'] = sc_loss['fape']
-
-    supervised_chi_loss(ret, batch, value, self.config)
-
-    if self.config.structural_violation_loss_weight:
-      if 'violations' not in value:
-        value['violations'] = find_structural_violations(
-            batch, value['final_atom14_positions'], self.config)
-      structural_violation_loss(ret, batch, value, self.config)
-
-    return ret
-
-
-def compute_renamed_ground_truth(
-    batch: Dict[str, jnp.ndarray],
-    atom14_pred_positions: jnp.ndarray,
-    ) -> Dict[str, jnp.ndarray]:
-  """Find optimal renaming of ground truth based on the predicted positions.
-
-  Jumper et al. (2021) Suppl. Alg. 26 "renameSymmetricGroundTruthAtoms"
-
-  This renamed ground truth is then used for all losses,
-  such that each loss moves the atoms in the same direction.
-  Shape (N).
-
-  Args:
-    batch: Dictionary containing:
-      * atom14_gt_positions: Ground truth positions.
-      * atom14_alt_gt_positions: Ground truth positions with renaming swaps.
-      * atom14_atom_is_ambiguous: 1.0 for atoms that are affected by
-          renaming swaps.
-      * atom14_gt_exists: Mask for which atoms exist in ground truth.
-      * atom14_alt_gt_exists: Mask for which atoms exist in ground truth
-          after renaming.
-      * atom14_atom_exists: Mask for whether each atom is part of the given
-          amino acid type.
-    atom14_pred_positions: Array of atom positions in global frame with shape
-      (N, 14, 3).
-  Returns:
-    Dictionary containing:
-      alt_naming_is_better: Array with 1.0 where alternative swap is better.
-      renamed_atom14_gt_positions: Array of optimal ground truth positions
-        after renaming swaps are performed.
-      renamed_atom14_gt_exists: Mask after renaming swap is performed.
-  """
-  alt_naming_is_better = all_atom.find_optimal_renaming(
-      atom14_gt_positions=batch['atom14_gt_positions'],
-      atom14_alt_gt_positions=batch['atom14_alt_gt_positions'],
-      atom14_atom_is_ambiguous=batch['atom14_atom_is_ambiguous'],
-      atom14_gt_exists=batch['atom14_gt_exists'],
-      atom14_pred_positions=atom14_pred_positions,
-      atom14_atom_exists=batch['atom14_atom_exists'])
-
-  renamed_atom14_gt_positions = (
-      (1. - alt_naming_is_better[:, None, None])
-      * batch['atom14_gt_positions']
-      + alt_naming_is_better[:, None, None]
-      * batch['atom14_alt_gt_positions'])
-
-  renamed_atom14_gt_mask = (
-      (1. - alt_naming_is_better[:, None]) * batch['atom14_gt_exists']
-      + alt_naming_is_better[:, None] * batch['atom14_alt_gt_exists'])
-
-  return {
-      'alt_naming_is_better': alt_naming_is_better,  # (N)
-      'renamed_atom14_gt_positions': renamed_atom14_gt_positions,  # (N, 14, 3)
-      'renamed_atom14_gt_exists': renamed_atom14_gt_mask,  # (N, 14)
-  }
-
-
-def backbone_loss(ret, batch, value, config):
-  """Backbone FAPE Loss.
-
-  Jumper et al. (2021) Suppl. Alg. 20 "StructureModule" line 17
-
-  Args:
-    ret: Dictionary to write outputs into, needs to contain 'loss'.
-    batch: Batch, needs to contain 'backbone_affine_tensor',
-      'backbone_affine_mask'.
-    value: Dictionary containing structure module output, needs to contain
-      'traj', a trajectory of rigids.
-    config: Configuration of loss, should contain 'fape.clamp_distance' and
-      'fape.loss_unit_distance'.
-  """
-  affine_trajectory = quat_affine.QuatAffine.from_tensor(value['traj'])
-  rigid_trajectory = r3.rigids_from_quataffine(affine_trajectory)
-
-  gt_affine = quat_affine.QuatAffine.from_tensor(
-      batch['backbone_affine_tensor'])
-  gt_rigid = r3.rigids_from_quataffine(gt_affine)
-  backbone_mask = batch['backbone_affine_mask']
-
-  fape_loss_fn = functools.partial(
-      all_atom.frame_aligned_point_error,
-      l1_clamp_distance=config.fape.clamp_distance,
-      length_scale=config.fape.loss_unit_distance)
-
-  fape_loss_fn = jax.vmap(fape_loss_fn, (0, None, None, 0, None, None))
-  fape_loss = fape_loss_fn(rigid_trajectory, gt_rigid, backbone_mask,
-                           rigid_trajectory.trans, gt_rigid.trans,
-                           backbone_mask)
-
-  if 'use_clamped_fape' in batch:
-    # Jumper et al. (2021) Suppl. Sec. 1.11.5 "Loss clamping details"
-    use_clamped_fape = jnp.asarray(batch['use_clamped_fape'], jnp.float32)
-    unclamped_fape_loss_fn = functools.partial(
-        all_atom.frame_aligned_point_error,
-        l1_clamp_distance=None,
-        length_scale=config.fape.loss_unit_distance)
-    unclamped_fape_loss_fn = jax.vmap(unclamped_fape_loss_fn,
-                                      (0, None, None, 0, None, None))
-    fape_loss_unclamped = unclamped_fape_loss_fn(rigid_trajectory, gt_rigid,
-                                                 backbone_mask,
-                                                 rigid_trajectory.trans,
-                                                 gt_rigid.trans,
-                                                 backbone_mask)
-
-    fape_loss = (fape_loss * use_clamped_fape +
-                 fape_loss_unclamped * (1 - use_clamped_fape))
-
-  ret['fape'] = fape_loss[-1]
-  ret['loss'] += jnp.mean(fape_loss)
-
-
-def sidechain_loss(batch, value, config):
-  """All Atom FAPE Loss using renamed rigids."""
-  # Rename Frames
-  # Jumper et al. (2021) Suppl. Alg. 26 "renameSymmetricGroundTruthAtoms" line 7
-  alt_naming_is_better = value['alt_naming_is_better']
-  renamed_gt_frames = (
-      (1. - alt_naming_is_better[:, None, None])
-      * batch['rigidgroups_gt_frames']
-      + alt_naming_is_better[:, None, None]
-      * batch['rigidgroups_alt_gt_frames'])
-
-  flat_gt_frames = r3.rigids_from_tensor_flat12(
-      jnp.reshape(renamed_gt_frames, [-1, 12]))
-  flat_frames_mask = jnp.reshape(batch['rigidgroups_gt_exists'], [-1])
-
-  flat_gt_positions = r3.vecs_from_tensor(
-      jnp.reshape(value['renamed_atom14_gt_positions'], [-1, 3]))
-  flat_positions_mask = jnp.reshape(value['renamed_atom14_gt_exists'], [-1])
-
-  # Compute frame_aligned_point_error score for the final layer.
-  pred_frames = value['sidechains']['frames']
-  pred_positions = value['sidechains']['atom_pos']
-
-  def _slice_last_layer_and_flatten(x):
-    return jnp.reshape(x[-1], [-1])
-  flat_pred_frames = jax.tree_map(
-      _slice_last_layer_and_flatten, pred_frames)
-  flat_pred_positions = jax.tree_map(
-      _slice_last_layer_and_flatten, pred_positions)
-  # FAPE Loss on sidechains
-  fape = all_atom.frame_aligned_point_error(
-      pred_frames=flat_pred_frames,
-      target_frames=flat_gt_frames,
-      frames_mask=flat_frames_mask,
-      pred_positions=flat_pred_positions,
-      target_positions=flat_gt_positions,
-      positions_mask=flat_positions_mask,
-      l1_clamp_distance=config.sidechain.atom_clamp_distance,
-      length_scale=config.sidechain.length_scale)
-
-  return {
-      'fape': fape,
-      'loss': fape}
-
-
-def structural_violation_loss(ret, batch, value, config):
-  """Computes loss for structural violations."""
-  assert config.sidechain.weight_frac
-
-  # Put all violation losses together to one large loss.
-  violations = value['violations']
-  num_atoms = jnp.sum(batch['atom14_atom_exists']).astype(jnp.float32)
-  ret['loss'] += (config.structural_violation_loss_weight * (
-      violations['between_residues']['bonds_c_n_loss_mean'] +
-      violations['between_residues']['angles_ca_c_n_loss_mean'] +
-      violations['between_residues']['angles_c_n_ca_loss_mean'] +
-      jnp.sum(
-          violations['between_residues']['clashes_per_atom_loss_sum'] +
-          violations['within_residues']['per_atom_loss_sum']) /
-      (1e-6 + num_atoms)))
-
-
-def find_structural_violations(
-    batch: Dict[str, jnp.ndarray],
-    atom14_pred_positions: jnp.ndarray,  # (N, 14, 3)
-    config: ml_collections.ConfigDict
-    ):
-  """Computes several checks for structural violations."""
-
-  # Compute between residue backbone violations of bonds and angles.
-  connection_violations = all_atom.between_residue_bond_loss(
-      pred_atom_positions=atom14_pred_positions,
-      pred_atom_mask=batch['atom14_atom_exists'].astype(jnp.float32),
-      residue_index=batch['residue_index'].astype(jnp.float32),
-      aatype=batch['aatype'],
-      tolerance_factor_soft=config.violation_tolerance_factor,
-      tolerance_factor_hard=config.violation_tolerance_factor)
-
-  # Compute the Van der Waals radius for every atom
-  # (the first letter of the atom name is the element type).
-  # Shape: (N, 14).
-  atomtype_radius = jnp.array([
-      residue_constants.van_der_waals_radius[name[0]]
-      for name in residue_constants.atom_types
-  ])
-  atom14_atom_radius = batch['atom14_atom_exists'] * utils.batched_gather(
-      atomtype_radius, batch['residx_atom14_to_atom37'])
-
-  # Compute the between residue clash loss.
-  between_residue_clashes = all_atom.between_residue_clash_loss(
-      atom14_pred_positions=atom14_pred_positions,
-      atom14_atom_exists=batch['atom14_atom_exists'],
-      atom14_atom_radius=atom14_atom_radius,
-      residue_index=batch['residue_index'],
-      overlap_tolerance_soft=config.clash_overlap_tolerance,
-      overlap_tolerance_hard=config.clash_overlap_tolerance)
-
-  # Compute all within-residue violations (clashes,
-  # bond length and angle violations).
-  restype_atom14_bounds = residue_constants.make_atom14_dists_bounds(
-      overlap_tolerance=config.clash_overlap_tolerance,
-      bond_length_tolerance_factor=config.violation_tolerance_factor)
-  atom14_dists_lower_bound = utils.batched_gather(
-      restype_atom14_bounds['lower_bound'], batch['aatype'])
-  atom14_dists_upper_bound = utils.batched_gather(
-      restype_atom14_bounds['upper_bound'], batch['aatype'])
-  within_residue_violations = all_atom.within_residue_violations(
-      atom14_pred_positions=atom14_pred_positions,
-      atom14_atom_exists=batch['atom14_atom_exists'],
-      atom14_dists_lower_bound=atom14_dists_lower_bound,
-      atom14_dists_upper_bound=atom14_dists_upper_bound,
-      tighten_bounds_for_loss=0.0)
-
-  # Combine them to a single per-residue violation mask (used later for LDDT).
-  per_residue_violations_mask = jnp.max(jnp.stack([
-      connection_violations['per_residue_violation_mask'],
-      jnp.max(between_residue_clashes['per_atom_clash_mask'], axis=-1),
-      jnp.max(within_residue_violations['per_atom_violations'],
-              axis=-1)]), axis=0)
-
-  return {
-      'between_residues': {
-          'bonds_c_n_loss_mean':
-              connection_violations['c_n_loss_mean'],  # ()
-          'angles_ca_c_n_loss_mean':
-              connection_violations['ca_c_n_loss_mean'],  # ()
-          'angles_c_n_ca_loss_mean':
-              connection_violations['c_n_ca_loss_mean'],  # ()
-          'connections_per_residue_loss_sum':
-              connection_violations['per_residue_loss_sum'],  # (N)
-          'connections_per_residue_violation_mask':
-              connection_violations['per_residue_violation_mask'],  # (N)
-          'clashes_mean_loss':
-              between_residue_clashes['mean_loss'],  # ()
-          'clashes_per_atom_loss_sum':
-              between_residue_clashes['per_atom_loss_sum'],  # (N, 14)
-          'clashes_per_atom_clash_mask':
-              between_residue_clashes['per_atom_clash_mask'],  # (N, 14)
-      },
-      'within_residues': {
-          'per_atom_loss_sum':
-              within_residue_violations['per_atom_loss_sum'],  # (N, 14)
-          'per_atom_violations':
-              within_residue_violations['per_atom_violations'],  # (N, 14),
-      },
-      'total_per_residue_violations_mask':
-          per_residue_violations_mask,  # (N)
-  }
-
-
-def compute_violation_metrics(
-    batch: Dict[str, jnp.ndarray],
-    atom14_pred_positions: jnp.ndarray,  # (N, 14, 3)
-    violations: Dict[str, jnp.ndarray],
-    ) -> Dict[str, jnp.ndarray]:
-  """Compute several metrics to assess the structural violations."""
-
-  ret = {}
-  extreme_ca_ca_violations = all_atom.extreme_ca_ca_distance_violations(
-      pred_atom_positions=atom14_pred_positions,
-      pred_atom_mask=batch['atom14_atom_exists'].astype(jnp.float32),
-      residue_index=batch['residue_index'].astype(jnp.float32))
-  ret['violations_extreme_ca_ca_distance'] = extreme_ca_ca_violations
-  ret['violations_between_residue_bond'] = utils.mask_mean(
-      mask=batch['seq_mask'],
-      value=violations['between_residues'][
-          'connections_per_residue_violation_mask'])
-  ret['violations_between_residue_clash'] = utils.mask_mean(
-      mask=batch['seq_mask'],
-      value=jnp.max(
-          violations['between_residues']['clashes_per_atom_clash_mask'],
-          axis=-1))
-  ret['violations_within_residue'] = utils.mask_mean(
-      mask=batch['seq_mask'],
-      value=jnp.max(
-          violations['within_residues']['per_atom_violations'], axis=-1))
-  ret['violations_per_residue'] = utils.mask_mean(
-      mask=batch['seq_mask'],
-      value=violations['total_per_residue_violations_mask'])
-  return ret
-
-
-def supervised_chi_loss(ret, batch, value, config):
-  """Computes loss for direct chi angle supervision.
-
-  Jumper et al. (2021) Suppl. Alg. 27 "torsionAngleLoss"
-
-  Args:
-    ret: Dictionary to write outputs into, needs to contain 'loss'.
-    batch: Batch, needs to contain 'seq_mask', 'chi_mask', 'chi_angles'.
-    value: Dictionary containing structure module output, needs to contain
-      value['sidechains']['angles_sin_cos'] for angles and
-      value['sidechains']['unnormalized_angles_sin_cos'] for unnormalized
-      angles.
-    config: Configuration of loss, should contain 'chi_weight' and
-      'angle_norm_weight', 'angle_norm_weight' scales angle norm term,
-      'chi_weight' scales torsion term.
-  """
-  eps = 1e-6
-
-  sequence_mask = batch['seq_mask']
-  num_res = sequence_mask.shape[0]
-  chi_mask = batch['chi_mask'].astype(jnp.float32)
-  pred_angles = jnp.reshape(
-      value['sidechains']['angles_sin_cos'], [-1, num_res, 7, 2])
-  pred_angles = pred_angles[:, :, 3:]
-
-  residue_type_one_hot = jax.nn.one_hot(
-      batch['aatype'], residue_constants.restype_num + 1,
-      dtype=jnp.float32)[None]
-  chi_pi_periodic = jnp.einsum('ijk, kl->ijl', residue_type_one_hot,
-                               jnp.asarray(residue_constants.chi_pi_periodic))
-
-  true_chi = batch['chi_angles'][None]
-  sin_true_chi = jnp.sin(true_chi)
-  cos_true_chi = jnp.cos(true_chi)
-  sin_cos_true_chi = jnp.stack([sin_true_chi, cos_true_chi], axis=-1)
-
-  # This is -1 if chi is pi-periodic and +1 if it's 2pi-periodic
-  shifted_mask = (1 - 2 * chi_pi_periodic)[..., None]
-  sin_cos_true_chi_shifted = shifted_mask * sin_cos_true_chi
-
-  sq_chi_error = jnp.sum(
-      squared_difference(sin_cos_true_chi, pred_angles), -1)
-  sq_chi_error_shifted = jnp.sum(
-      squared_difference(sin_cos_true_chi_shifted, pred_angles), -1)
-  sq_chi_error = jnp.minimum(sq_chi_error, sq_chi_error_shifted)
-
-  sq_chi_loss = utils.mask_mean(mask=chi_mask[None], value=sq_chi_error)
-  ret['chi_loss'] = sq_chi_loss
-  ret['loss'] += config.chi_weight * sq_chi_loss
-  unnormed_angles = jnp.reshape(
-      value['sidechains']['unnormalized_angles_sin_cos'], [-1, num_res, 7, 2])
-  angle_norm = jnp.sqrt(jnp.sum(jnp.square(unnormed_angles), axis=-1) + eps)
-  norm_error = jnp.abs(angle_norm - 1.)
-  angle_norm_loss = utils.mask_mean(mask=sequence_mask[None, :, None],
-                                    value=norm_error)
-
-  ret['angle_norm_loss'] = angle_norm_loss
-  ret['loss'] += config.angle_norm_weight * angle_norm_loss
-
-
-def generate_new_affine(sequence_mask):
-  num_residues, _ = sequence_mask.shape
-  quaternion = jnp.tile(
-      jnp.reshape(jnp.asarray([1., 0., 0., 0.]), [1, 4]),
-      [num_residues, 1])
-
-  translation = jnp.zeros([num_residues, 3])
-  return quat_affine.QuatAffine(quaternion, translation, unstack_inputs=True)
-
-
-def l2_normalize(x, axis=-1, epsilon=1e-12):
-  return x / jnp.sqrt(
-      jnp.maximum(jnp.sum(x**2, axis=axis, keepdims=True), epsilon))
-
-
-class MultiRigidSidechain(hk.Module):
-  """Class to make side chain atoms."""
-
-  def __init__(self, config, global_config, name='rigid_sidechain'):
-    super().__init__(name=name)
-    self.config = config
-    self.global_config = global_config
-
-  def __call__(self, affine, representations_list, aatype):
-    """Predict side chains using multi-rigid representations.
-
-    Args:
-      affine: The affines for each residue (translations in angstroms).
-      representations_list: A list of activations to predict side chains from.
-      aatype: Amino acid types.
-
-    Returns:
-      Dict containing atom positions and frames (in angstroms).
-    """
-    act = [
-        common_modules.Linear(  # pylint: disable=g-complex-comprehension
-            self.config.num_channel,
-            name='input_projection')(jax.nn.relu(x))
-        for x in representations_list
-    ]
-    # Sum the activation list (equivalent to concat then Linear).
-    act = sum(act)
-
-    final_init = 'zeros' if self.global_config.zero_init else 'linear'
-
-    # Mapping with some residual blocks.
-    for _ in range(self.config.num_residual_block):
-      old_act = act
-      act = common_modules.Linear(
-          self.config.num_channel,
-          initializer='relu',
-          name='resblock1')(
-              jax.nn.relu(act))
-      act = common_modules.Linear(
-          self.config.num_channel,
-          initializer=final_init,
-          name='resblock2')(
-              jax.nn.relu(act))
-      act += old_act
-
-    # Map activations to torsion angles. Shape: (num_res, 14).
-    num_res = act.shape[0]
-    unnormalized_angles = common_modules.Linear(
-        14, name='unnormalized_angles')(
-            jax.nn.relu(act))
-    unnormalized_angles = jnp.reshape(
-        unnormalized_angles, [num_res, 7, 2])
-    angles = l2_normalize(unnormalized_angles, axis=-1)
-
-    outputs = {
-        'angles_sin_cos': angles,  # jnp.ndarray (N, 7, 2)
-        'unnormalized_angles_sin_cos':
-            unnormalized_angles,  # jnp.ndarray (N, 7, 2)
-    }
-
-    # Map torsion angles to frames.
-    backb_to_global = r3.rigids_from_quataffine(affine)
-
-    # Jumper et al. (2021) Suppl. Alg. 24 "computeAllAtomCoordinates"
-
-    # r3.Rigids with shape (N, 8).
-    all_frames_to_global = all_atom.torsion_angles_to_frames(
-        aatype,
-        backb_to_global,
-        angles)
-
-    # Use frames and literature positions to create the final atom coordinates.
-    # r3.Vecs with shape (N, 14).
-    pred_positions = all_atom.frames_and_literature_positions_to_atom14_pos(
-        aatype, all_frames_to_global)
-
-    outputs.update({
-        'atom_pos': pred_positions,  # r3.Vecs (N, 14)
-        'frames': all_frames_to_global,  # r3.Rigids (N, 8)
-    })
-    return outputs
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
deleted file mode 100644
index ad7b0346a63dfa3c3ca246b624155fc4fd331a3f..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
+++ /dev/null
@@ -1,12 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(
-                _delete_=True,
-                type='ModulatedDeformRoIPoolPack',
-                output_size=7,
-                output_channels=256),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32])))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py
deleted file mode 100644
index 27ab3e733bda1fb1c7c50cbd0f26597650b4c2e7..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,58 +0,0 @@
-_base_ = '../rpn/rpn_r50_fpn_1x_coco.py'
-model = dict(
-    rpn_head=dict(
-        _delete_=True,
-        type='GARPNHead',
-        in_channels=256,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=8,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[8],
-            strides=[4, 8, 16, 32, 64]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.14, 0.14]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.11, 0.11]),
-        loc_filter_thr=0.01,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            ga_assigner=dict(
-                type='ApproxMaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                ignore_iof_thr=-1),
-            ga_sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            center_ratio=0.2,
-            ignore_ratio=0.5)),
-    test_cfg=dict(rpn=dict(nms_post=1000)))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py
deleted file mode 100644
index 6fe6bd660230eedf70f87072e5abec66036d865f..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,50 +0,0 @@
-_base_ = [
-    '../_base_/models/retinanet_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-# model settings
-model = dict(
-    bbox_head=dict(
-        _delete_=True,
-        type='SABLRetinaHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=4,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[8, 16, 32, 64, 128]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[4],
-            strides=[8, 16, 32, 64, 128]),
-        bbox_coder=dict(
-            type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
-        loss_bbox_reg=dict(
-            type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
-    # training and testing settings
-    train_cfg=dict(
-        assigner=dict(
-            type='ApproxMaxIoUAssigner',
-            pos_iou_thr=0.5,
-            neg_iou_thr=0.4,
-            min_pos_iou=0.0,
-            ignore_iof_thr=-1),
-        allowed_border=-1,
-        pos_weight=-1,
-        debug=False))
-# optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/demodata.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/demodata.py
deleted file mode 100644
index feecb693745a47d9f2bebd8af9a217ff4f5cc92b..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/demodata.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import numpy as np
-import torch
-
-from mmdet.utils.util_random import ensure_rng
-
-
-def random_boxes(num=1, scale=1, rng=None):
-    """Simple version of ``kwimage.Boxes.random``
-
-    Returns:
-        Tensor: shape (n, 4) in x1, y1, x2, y2 format.
-
-    References:
-        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
-
-    Example:
-        >>> num = 3
-        >>> scale = 512
-        >>> rng = 0
-        >>> boxes = random_boxes(num, scale, rng)
-        >>> print(boxes)
-        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
-                [216.9113, 330.6978, 224.0446, 456.5878],
-                [405.3632, 196.3221, 493.3953, 270.7942]])
-    """
-    rng = ensure_rng(rng)
-
-    tlbr = rng.rand(num, 4).astype(np.float32)
-
-    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
-    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
-    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
-    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
-
-    tlbr[:, 0] = tl_x * scale
-    tlbr[:, 1] = tl_y * scale
-    tlbr[:, 2] = br_x * scale
-    tlbr[:, 3] = br_y * scale
-
-    boxes = torch.from_numpy(tlbr)
-    return boxes
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/__init__.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/__init__.py
deleted file mode 100644
index 0b06303fe1000e11c5486c40c70606a34a5208e3..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/samplers/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from .base_sampler import BaseSampler
-from .combined_sampler import CombinedSampler
-from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
-from .iou_balanced_neg_sampler import IoUBalancedNegSampler
-from .ohem_sampler import OHEMSampler
-from .pseudo_sampler import PseudoSampler
-from .random_sampler import RandomSampler
-from .sampling_result import SamplingResult
-from .score_hlr_sampler import ScoreHLRSampler
-
-__all__ = [
-    'BaseSampler', 'PseudoSampler', 'RandomSampler',
-    'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler',
-    'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler'
-]
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
deleted file mode 100644
index 5c623eb56836760694b50f3e4e66aa0f1fc069df..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './danet_r50-d8_512x512_40k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/diffusion/__init__.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/diffusion/__init__.py
deleted file mode 100644
index e5737294ae16c0de52085b8dcf6825c348f617e4..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/diffusion/__init__.py
+++ /dev/null
@@ -1,6 +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.
-"""Diffusion grids."""
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/musicgen.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/musicgen.py
deleted file mode 100644
index bb615abf448f9dd07490aaabf3fff9b861a1b2cb..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/musicgen.py
+++ /dev/null
@@ -1,699 +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.
-
-from pathlib import Path
-import time
-import typing as tp
-
-import flashy
-import math
-import omegaconf
-import torch
-from torch.nn import functional as F
-
-from . import base, builders
-from .compression import CompressionSolver
-from .. import metrics as eval_metrics
-from .. import models
-from ..data.audio_dataset import AudioDataset
-from ..data.music_dataset import MusicDataset, MusicInfo, AudioInfo
-from ..data.audio_utils import normalize_audio
-from ..modules.conditioners import JointEmbedCondition, SegmentWithAttributes, WavCondition
-from ..utils.cache import CachedBatchWriter, CachedBatchLoader
-from ..utils.samples.manager import SampleManager
-from ..utils.utils import get_dataset_from_loader, is_jsonable, warn_once
-
-
-class MusicGenSolver(base.StandardSolver):
-    """Solver for MusicGen training task.
-
-    Used in: https://arxiv.org/abs/2306.05284
-    """
-    DATASET_TYPE: builders.DatasetType = builders.DatasetType.MUSIC
-
-    def __init__(self, cfg: omegaconf.DictConfig):
-        super().__init__(cfg)
-        # easier access to sampling parameters
-        self.generation_params = {
-            'use_sampling': self.cfg.generate.lm.use_sampling,
-            'temp': self.cfg.generate.lm.temp,
-            'top_k': self.cfg.generate.lm.top_k,
-            'top_p': self.cfg.generate.lm.top_p,
-        }
-        self._best_metric_name: tp.Optional[str] = 'ce'
-
-        self._cached_batch_writer = None
-        self._cached_batch_loader = None
-        if cfg.cache.path:
-            if cfg.cache.write:
-                self._cached_batch_writer = CachedBatchWriter(Path(cfg.cache.path))
-                if self.cfg.cache.write_num_shards:
-                    self.logger.warning("Multiple shard cache, best_metric_name will be set to None.")
-                    self._best_metric_name = None
-            else:
-                self._cached_batch_loader = CachedBatchLoader(
-                    Path(cfg.cache.path), cfg.dataset.batch_size, cfg.dataset.num_workers,
-                    min_length=self.cfg.optim.updates_per_epoch or 1)
-                self.dataloaders['original_train'] = self.dataloaders['train']
-                self.dataloaders['train'] = self._cached_batch_loader  # type: ignore
-
-    @staticmethod
-    def get_eval_solver_from_sig(sig: str, dtype: tp.Optional[str] = None,
-                                 device: tp.Optional[str] = None, autocast: bool = True,
-                                 batch_size: tp.Optional[int] = None,
-                                 override_cfg: tp.Optional[tp.Union[dict, omegaconf.DictConfig]] = None,
-                                 **kwargs):
-        """Mostly a convenience function around magma.train.get_solver_from_sig,
-        populating all the proper param, deactivating EMA, FSDP, loading the best state,
-        basically all you need to get a solver ready to "play" with in single GPU mode
-        and with minimal memory overhead.
-
-        Args:
-            sig (str): signature to load.
-            dtype (str or None): potential dtype, as a string, i.e. 'float16'.
-            device (str or None): potential device, as a string, i.e. 'cuda'.
-            override_cfg (dict or omegaconf.DictConfig or None): potential device, as a string, i.e. 'cuda'.
-        """
-        from audiocraft import train
-        our_override_cfg: tp.Dict[str, tp.Any] = {'optim': {'ema': {'use': False}}}
-        our_override_cfg['autocast'] = autocast
-        if dtype is not None:
-            our_override_cfg['dtype'] = dtype
-        if device is not None:
-            our_override_cfg['device'] = device
-        if batch_size is not None:
-            our_override_cfg['dataset'] = {'batch_size': batch_size}
-        if override_cfg is None:
-            override_cfg = {}
-        override_cfg = omegaconf.OmegaConf.merge(
-            omegaconf.DictConfig(override_cfg), omegaconf.DictConfig(our_override_cfg))  # type: ignore
-        solver = train.get_solver_from_sig(
-            sig, override_cfg=override_cfg,
-            load_best=True, disable_fsdp=True,
-            ignore_state_keys=['optimizer', 'ema'], **kwargs)
-        solver.model.eval()
-        return solver
-
-    def get_formatter(self, stage_name: str) -> flashy.Formatter:
-        return flashy.Formatter({
-            'lr': '.2E',
-            'ce': '.3f',
-            'ppl': '.3f',
-            'grad_norm': '.3E',
-        }, exclude_keys=['ce_q*', 'ppl_q*'])
-
-    @property
-    def best_metric_name(self) -> tp.Optional[str]:
-        return self._best_metric_name
-
-    def build_model(self) -> None:
-        """Instantiate models and optimizer."""
-        # we can potentially not use all quantizers with which the EnCodec model was trained
-        # (e.g. we trained the model with quantizers dropout)
-        self.compression_model = CompressionSolver.wrapped_model_from_checkpoint(
-            self.cfg, self.cfg.compression_model_checkpoint, device=self.device)
-        assert self.compression_model.sample_rate == self.cfg.sample_rate, (
-            f"Compression model sample rate is {self.compression_model.sample_rate} but "
-            f"Solver sample rate is {self.cfg.sample_rate}."
-            )
-        # ensure we have matching configuration between LM and compression model
-        assert self.cfg.transformer_lm.card == self.compression_model.cardinality, (
-            "Cardinalities of the LM and compression model don't match: ",
-            f"LM cardinality is {self.cfg.transformer_lm.card} vs ",
-            f"compression model cardinality is {self.compression_model.cardinality}"
-        )
-        assert self.cfg.transformer_lm.n_q == self.compression_model.num_codebooks, (
-            "Numbers of codebooks of the LM and compression models don't match: ",
-            f"LM number of codebooks is {self.cfg.transformer_lm.n_q} vs ",
-            f"compression model numer of codebooks is {self.compression_model.num_codebooks}"
-        )
-        self.logger.info("Compression model has %d codebooks with %d cardinality, and a framerate of %d",
-                         self.compression_model.num_codebooks, self.compression_model.cardinality,
-                         self.compression_model.frame_rate)
-        # instantiate LM model
-        self.model: models.LMModel = models.builders.get_lm_model(self.cfg).to(self.device)
-        if self.cfg.fsdp.use:
-            assert not self.cfg.autocast, "Cannot use autocast with fsdp"
-            self.model = self.wrap_with_fsdp(self.model)
-        self.register_ema('model')
-        # initialize optimization
-        self.optimizer = builders.get_optimizer(builders.get_optim_parameter_groups(self.model), self.cfg.optim)
-        self.lr_scheduler = builders.get_lr_scheduler(self.optimizer, self.cfg.schedule, self.total_updates)
-        self.register_stateful('compression_model', 'model', 'optimizer', 'lr_scheduler')
-        self.register_best_state('model')
-        self.autocast_dtype = {
-            'float16': torch.float16, 'bfloat16': torch.bfloat16
-        }[self.cfg.autocast_dtype]
-        self.scaler: tp.Optional[torch.cuda.amp.GradScaler] = None
-        if self.cfg.fsdp.use:
-            need_scaler = self.cfg.fsdp.param_dtype == 'float16'
-        else:
-            need_scaler = self.cfg.autocast and self.autocast_dtype is torch.float16
-        if need_scaler:
-            if self.cfg.fsdp.use:
-                from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
-                self.scaler = ShardedGradScaler()  # type: ignore
-            else:
-                self.scaler = torch.cuda.amp.GradScaler()
-            self.register_stateful('scaler')
-
-    def build_dataloaders(self) -> None:
-        """Instantiate audio dataloaders for each stage."""
-        self.dataloaders = builders.get_audio_datasets(self.cfg, dataset_type=self.DATASET_TYPE)
-
-    def show(self) -> None:
-        """Show the compression model and LM model."""
-        self.logger.info("Compression model:")
-        self.log_model_summary(self.compression_model)
-        self.logger.info("LM model:")
-        self.log_model_summary(self.model)
-
-    def load_state_dict(self, state: dict) -> None:
-        if 'condition_provider' in state:
-            model_state = state['model']
-            condition_provider_state = state.pop('condition_provider')
-            prefix = 'condition_provider.'
-            for key, value in condition_provider_state.items():
-                key = prefix + key
-                assert key not in model_state
-                model_state[key] = value
-        super().load_state_dict(state)
-
-    def load_from_pretrained(self, name: str):
-        # TODO: support native HF versions of MusicGen.
-        lm_pkg = models.loaders.load_lm_model_ckpt(name)
-        state: dict = {
-            'best_state': {
-                'model': lm_pkg['best_state'],
-            },
-        }
-        return state
-
-    def _compute_cross_entropy(
-        self, logits: torch.Tensor, targets: torch.Tensor, mask: torch.Tensor
-    ) -> tp.Tuple[torch.Tensor, tp.List[torch.Tensor]]:
-        """Compute cross entropy between multi-codebook targets and model's logits.
-        The cross entropy is computed per codebook to provide codebook-level cross entropy.
-        Valid timesteps for each of the codebook are pulled from the mask, where invalid
-        timesteps are set to 0.
-
-        Args:
-            logits (torch.Tensor): Model's logits of shape [B, K, T, card].
-            targets (torch.Tensor): Target codes, of shape [B, K, T].
-            mask (torch.Tensor): Mask for valid target codes, of shape [B, K, T].
-        Returns:
-            ce (torch.Tensor): Cross entropy averaged over the codebooks
-            ce_per_codebook (list of torch.Tensor): Cross entropy per codebook (detached).
-        """
-        B, K, T = targets.shape
-        assert logits.shape[:-1] == targets.shape
-        assert mask.shape == targets.shape
-        ce = torch.zeros([], device=targets.device)
-        ce_per_codebook: tp.List[torch.Tensor] = []
-        for k in range(K):
-            logits_k = logits[:, k, ...].contiguous().view(-1, logits.size(-1))  # [B x T, card]
-            targets_k = targets[:, k, ...].contiguous().view(-1)  # [B x T]
-            mask_k = mask[:, k, ...].contiguous().view(-1)  # [B x T]
-            ce_targets = targets_k[mask_k]
-            ce_logits = logits_k[mask_k]
-            q_ce = F.cross_entropy(ce_logits, ce_targets)
-            ce += q_ce
-            ce_per_codebook.append(q_ce.detach())
-        # average cross entropy across codebooks
-        ce = ce / K
-        return ce, ce_per_codebook
-
-    @torch.no_grad()
-    def _prepare_tokens_and_attributes(
-        self, batch: tp.Tuple[torch.Tensor, tp.List[SegmentWithAttributes]],
-        check_synchronization_points: bool = False
-    ) -> tp.Tuple[dict, torch.Tensor, torch.Tensor]:
-        """Prepare input batchs for language model training.
-
-        Args:
-            batch (tuple[torch.Tensor, list[SegmentWithAttributes]]): Input batch with audio tensor of shape [B, C, T]
-                and corresponding metadata as SegmentWithAttributes (with B items).
-            check_synchronization_points (bool): Whether to check for synchronization points slowing down training.
-        Returns:
-            Condition tensors (dict[str, any]): Preprocessed condition attributes.
-            Tokens (torch.Tensor): Audio tokens from compression model, of shape [B, K, T_s],
-                with B the batch size, K the number of codebooks, T_s the token timesteps.
-            Padding mask (torch.Tensor): Mask with valid positions in the tokens tensor, of shape [B, K, T_s].
-        """
-        if self._cached_batch_loader is None or self.current_stage != "train":
-            audio, infos = batch
-            audio = audio.to(self.device)
-            audio_tokens = None
-            assert audio.size(0) == len(infos), (
-                f"Mismatch between number of items in audio batch ({audio.size(0)})",
-                f" and in metadata ({len(infos)})"
-            )
-        else:
-            audio = None
-            # In that case the batch will be a tuple coming from the _cached_batch_writer bit below.
-            infos, = batch  # type: ignore
-            assert all([isinstance(info, AudioInfo) for info in infos])
-            assert all([info.audio_tokens is not None for info in infos])  # type: ignore
-            audio_tokens = torch.stack([info.audio_tokens for info in infos]).to(self.device)  # type: ignore
-            audio_tokens = audio_tokens.long()
-            for info in infos:
-                if isinstance(info, MusicInfo):
-                    # Careful here, if you want to use this condition_wav (e.b. chroma conditioning),
-                    # then you must be using the chroma cache! otherwise the code will try
-                    # to use this segment and fail (by that I mean you will see NaN everywhere).
-                    info.self_wav = WavCondition(
-                        torch.full([1, info.channels, info.total_frames], float('NaN')),
-                        length=torch.tensor([info.n_frames]),
-                        sample_rate=[info.sample_rate],
-                        path=[info.meta.path],
-                        seek_time=[info.seek_time])
-                    dataset = get_dataset_from_loader(self.dataloaders['original_train'])
-                    assert isinstance(dataset, MusicDataset), type(dataset)
-                    if dataset.paraphraser is not None and info.description is not None:
-                        # Hackingly reapplying paraphraser when using cache.
-                        info.description = dataset.paraphraser.sample_paraphrase(
-                            info.meta.path, info.description)
-        # prepare attributes
-        attributes = [info.to_condition_attributes() for info in infos]
-        attributes = self.model.cfg_dropout(attributes)
-        attributes = self.model.att_dropout(attributes)
-        tokenized = self.model.condition_provider.tokenize(attributes)
-
-        # Now we should be synchronization free.
-        if self.device == "cuda" and check_synchronization_points:
-            torch.cuda.set_sync_debug_mode("warn")
-
-        if audio_tokens is None:
-            with torch.no_grad():
-                audio_tokens, scale = self.compression_model.encode(audio)
-                assert scale is None, "Scaled compression model not supported with LM."
-
-        with self.autocast:
-            condition_tensors = self.model.condition_provider(tokenized)
-
-        # create a padding mask to hold valid vs invalid positions
-        padding_mask = torch.ones_like(audio_tokens, dtype=torch.bool, device=audio_tokens.device)
-        # replace encodec tokens from padded audio with special_token_id
-        if self.cfg.tokens.padding_with_special_token:
-            audio_tokens = audio_tokens.clone()
-            padding_mask = padding_mask.clone()
-            token_sample_rate = self.compression_model.frame_rate
-            B, K, T_s = audio_tokens.shape
-            for i in range(B):
-                n_samples = infos[i].n_frames
-                audio_sample_rate = infos[i].sample_rate
-                # take the last token generated from actual audio frames (non-padded audio)
-                valid_tokens = math.floor(float(n_samples) / audio_sample_rate * token_sample_rate)
-                audio_tokens[i, :, valid_tokens:] = self.model.special_token_id
-                padding_mask[i, :, valid_tokens:] = 0
-
-        if self.device == "cuda" and check_synchronization_points:
-            torch.cuda.set_sync_debug_mode("default")
-
-        if self._cached_batch_writer is not None and self.current_stage == 'train':
-            assert self._cached_batch_loader is None
-            assert audio_tokens is not None
-            for info, one_audio_tokens in zip(infos, audio_tokens):
-                assert isinstance(info, AudioInfo)
-                if isinstance(info, MusicInfo):
-                    assert not info.joint_embed, "joint_embed and cache not supported yet."
-                    info.self_wav = None
-                assert one_audio_tokens.max() < 2**15, one_audio_tokens.max().item()
-                info.audio_tokens = one_audio_tokens.short().cpu()
-            self._cached_batch_writer.save(infos)
-
-        return condition_tensors, audio_tokens, padding_mask
-
-    def run_step(self, idx: int, batch: tp.Tuple[torch.Tensor, tp.List[SegmentWithAttributes]], metrics: dict) -> dict:
-        """Perform one training or valid step on a given batch."""
-        check_synchronization_points = idx == 1 and self.device == 'cuda'
-
-        condition_tensors, audio_tokens, padding_mask = self._prepare_tokens_and_attributes(
-            batch, check_synchronization_points)
-
-        self.deadlock_detect.update('tokens_and_conditions')
-
-        if check_synchronization_points:
-            torch.cuda.set_sync_debug_mode('warn')
-
-        with self.autocast:
-            model_output = self.model.compute_predictions(audio_tokens, [], condition_tensors)  # type: ignore
-            logits = model_output.logits
-            mask = padding_mask & model_output.mask
-            ce, ce_per_codebook = self._compute_cross_entropy(logits, audio_tokens, mask)
-            loss = ce
-        self.deadlock_detect.update('loss')
-
-        if check_synchronization_points:
-            torch.cuda.set_sync_debug_mode('default')
-
-        if self.is_training:
-            metrics['lr'] = self.optimizer.param_groups[0]['lr']
-            if self.scaler is not None:
-                loss = self.scaler.scale(loss)
-            self.deadlock_detect.update('scale')
-            if self.cfg.fsdp.use:
-                loss.backward()
-                flashy.distrib.average_tensors(self.model.buffers())
-            elif self.cfg.optim.eager_sync:
-                with flashy.distrib.eager_sync_model(self.model):
-                    loss.backward()
-            else:
-                # this should always be slower but can be useful
-                # for weird use cases like multiple backwards.
-                loss.backward()
-                flashy.distrib.sync_model(self.model)
-            self.deadlock_detect.update('backward')
-
-            if self.scaler is not None:
-                self.scaler.unscale_(self.optimizer)
-            if self.cfg.optim.max_norm:
-                if self.cfg.fsdp.use:
-                    metrics['grad_norm'] = self.model.clip_grad_norm_(self.cfg.optim.max_norm)  # type: ignore
-                else:
-                    metrics['grad_norm'] = torch.nn.utils.clip_grad_norm_(
-                        self.model.parameters(), self.cfg.optim.max_norm
-                    )
-            if self.scaler is None:
-                self.optimizer.step()
-            else:
-                self.scaler.step(self.optimizer)
-                self.scaler.update()
-            if self.lr_scheduler:
-                self.lr_scheduler.step()
-            self.optimizer.zero_grad()
-            self.deadlock_detect.update('optim')
-            if self.scaler is not None:
-                scale = self.scaler.get_scale()
-                metrics['grad_scale'] = scale
-            if not loss.isfinite().all():
-                raise RuntimeError("Model probably diverged.")
-
-        metrics['ce'] = ce
-        metrics['ppl'] = torch.exp(ce)
-        for k, ce_q in enumerate(ce_per_codebook):
-            metrics[f'ce_q{k + 1}'] = ce_q
-            metrics[f'ppl_q{k + 1}'] = torch.exp(ce_q)
-
-        return metrics
-
-    @torch.no_grad()
-    def run_generate_step(self, batch: tp.Tuple[torch.Tensor, tp.List[SegmentWithAttributes]],
-                          gen_duration: float, prompt_duration: tp.Optional[float] = None,
-                          remove_prompt: bool = False,
-                          **generation_params) -> dict:
-        """Run generate step on a batch of optional audio tensor and corresponding attributes.
-
-        Args:
-            batch (tuple[torch.Tensor, list[SegmentWithAttributes]]):
-            use_prompt (bool): Whether to do audio continuation generation with prompt from audio batch.
-            gen_duration (float): Target audio duration for the generation.
-            prompt_duration (float, optional): Duration for the audio prompt to use for continuation.
-            remove_prompt (bool, optional): Whether to remove the prompt from the generated audio.
-            generation_params: Additional generation parameters.
-        Returns:
-            gen_outputs (dict): Generation outputs, consisting in audio, audio tokens from both the generation
-                and the prompt along with additional information.
-        """
-        bench_start = time.time()
-        audio, meta = batch
-        assert audio.size(0) == len(meta), (
-            f"Mismatch between number of items in audio batch ({audio.size(0)})",
-            f" and in metadata ({len(meta)})"
-        )
-        # prepare attributes
-        attributes = [x.to_condition_attributes() for x in meta]
-        # TODO: Add dropout for chroma?
-
-        # prepare audio prompt
-        if prompt_duration is None:
-            prompt_audio = None
-        else:
-            assert prompt_duration < gen_duration, "Prompt duration must be lower than target generation duration"
-            prompt_audio_frames = int(prompt_duration * self.compression_model.sample_rate)
-            prompt_audio = audio[..., :prompt_audio_frames]
-
-        # get audio tokens from compression model
-        if prompt_audio is None or prompt_audio.nelement() == 0:
-            num_samples = len(attributes)
-            prompt_tokens = None
-        else:
-            num_samples = None
-            prompt_audio = prompt_audio.to(self.device)
-            prompt_tokens, scale = self.compression_model.encode(prompt_audio)
-            assert scale is None, "Compression model in MusicGen should not require rescaling."
-
-        # generate by sampling from the LM
-        with self.autocast:
-            total_gen_len = math.ceil(gen_duration * self.compression_model.frame_rate)
-            gen_tokens = self.model.generate(
-                prompt_tokens, attributes, max_gen_len=total_gen_len,
-                num_samples=num_samples, **self.generation_params)
-
-        # generate audio from tokens
-        assert gen_tokens.dim() == 3
-        gen_audio = self.compression_model.decode(gen_tokens, None)
-
-        bench_end = time.time()
-        gen_outputs = {
-            'rtf': (bench_end - bench_start) / gen_duration,
-            'ref_audio': audio,
-            'gen_audio': gen_audio,
-            'gen_tokens': gen_tokens,
-            'prompt_audio': prompt_audio,
-            'prompt_tokens': prompt_tokens,
-        }
-        return gen_outputs
-
-    def generate_audio(self) -> dict:
-        """Audio generation stage."""
-        generate_stage_name = f'{self.current_stage}'
-        sample_manager = SampleManager(self.xp)
-        self.logger.info(f"Generating samples in {sample_manager.base_folder}")
-        loader = self.dataloaders['generate']
-        updates = len(loader)
-        lp = self.log_progress(generate_stage_name, loader, total=updates, updates=self.log_updates)
-
-        dataset = get_dataset_from_loader(loader)
-        dataset_duration = dataset.segment_duration
-        assert dataset_duration is not None
-        assert isinstance(dataset, AudioDataset)
-        target_duration = self.cfg.generate.lm.gen_duration
-        prompt_duration = self.cfg.generate.lm.prompt_duration
-        if target_duration is None:
-            target_duration = dataset_duration
-        if prompt_duration is None:
-            prompt_duration = dataset_duration / 4
-        assert prompt_duration < dataset_duration, (
-            f"Specified prompt duration ({prompt_duration}s) is longer",
-            f" than reference audio duration ({dataset_duration}s)"
-        )
-
-        def get_hydrated_conditions(meta: tp.List[SegmentWithAttributes]):
-            hydrated_conditions = []
-            for sample in [x.to_condition_attributes() for x in meta]:
-                cond_dict = {}
-                for cond_type in sample.__annotations__.keys():
-                    for cond_key, cond_val in getattr(sample, cond_type).items():
-                        if cond_key not in self.model.condition_provider.conditioners.keys():
-                            continue
-                        if is_jsonable(cond_val):
-                            cond_dict[cond_key] = cond_val
-                        elif isinstance(cond_val, WavCondition):
-                            cond_dict[cond_key] = cond_val.path
-                        elif isinstance(cond_val, JointEmbedCondition):
-                            cond_dict[cond_key] = cond_val.text  # only support text at inference for now
-                        else:
-                            # if we reached this point, it is not clear how to log the condition
-                            # so we just log the type.
-                            cond_dict[cond_key] = str(type(cond_val))
-                            continue
-                hydrated_conditions.append(cond_dict)
-            return hydrated_conditions
-
-        metrics: dict = {}
-        average = flashy.averager()
-        for batch in lp:
-            audio, meta = batch
-            # metadata for sample manager
-            hydrated_conditions = get_hydrated_conditions(meta)
-            sample_generation_params = {
-                **{f'classifier_free_guidance_{k}': v for k, v in self.cfg.classifier_free_guidance.items()},
-                **self.generation_params
-            }
-            if self.cfg.generate.lm.unprompted_samples:
-                if self.cfg.generate.lm.gen_gt_samples:
-                    # get the ground truth instead of generation
-                    self.logger.warn(
-                        "Use ground truth instead of audio generation as generate.lm.gen_gt_samples=true")
-                    gen_unprompted_audio = audio
-                    rtf = 1.
-                else:
-                    gen_unprompted_outputs = self.run_generate_step(
-                        batch, gen_duration=target_duration, prompt_duration=prompt_duration,
-                        **self.generation_params)
-                    gen_unprompted_audio = gen_unprompted_outputs['gen_audio'].cpu()
-                    rtf = gen_unprompted_outputs['rtf']
-                sample_manager.add_samples(
-                    gen_unprompted_audio, self.epoch, hydrated_conditions,
-                    ground_truth_wavs=audio, generation_args=sample_generation_params)
-
-            if self.cfg.generate.lm.prompted_samples:
-                gen_outputs = self.run_generate_step(
-                    batch, gen_duration=target_duration, prompt_duration=prompt_duration,
-                    **self.generation_params)
-                gen_audio = gen_outputs['gen_audio'].cpu()
-                prompt_audio = gen_outputs['prompt_audio'].cpu()
-                sample_manager.add_samples(
-                    gen_audio, self.epoch, hydrated_conditions,
-                    prompt_wavs=prompt_audio, ground_truth_wavs=audio,
-                    generation_args=sample_generation_params)
-
-            metrics['rtf'] = rtf
-            metrics = average(metrics)
-
-        flashy.distrib.barrier()
-        return metrics
-
-    def generate(self) -> dict:
-        """Generate stage."""
-        self.model.eval()
-        with torch.no_grad():
-            return self.generate_audio()
-
-    def run_epoch(self):
-        if self.cfg.cache.write:
-            if ((self.epoch - 1) % self.cfg.cache.write_num_shards) != self.cfg.cache.write_shard:
-                return
-        super().run_epoch()
-
-    def train(self):
-        """Train stage.
-        """
-        if self._cached_batch_writer is not None:
-            self._cached_batch_writer.start_epoch(self.epoch)
-        if self._cached_batch_loader is None:
-            dataset = get_dataset_from_loader(self.dataloaders['train'])
-            assert isinstance(dataset, AudioDataset)
-            dataset.current_epoch = self.epoch
-        else:
-            self._cached_batch_loader.start_epoch(self.epoch)
-        return super().train()
-
-    def evaluate_audio_generation(self) -> dict:
-        """Evaluate audio generation with off-the-shelf metrics."""
-        evaluate_stage_name = f'{self.current_stage}_generation'
-        # instantiate evaluation metrics, if at least one metric is defined, run audio generation evaluation
-        fad: tp.Optional[eval_metrics.FrechetAudioDistanceMetric] = None
-        kldiv: tp.Optional[eval_metrics.KLDivergenceMetric] = None
-        text_consistency: tp.Optional[eval_metrics.TextConsistencyMetric] = None
-        chroma_cosine: tp.Optional[eval_metrics.ChromaCosineSimilarityMetric] = None
-        should_run_eval = False
-        eval_chroma_wavs: tp.Optional[torch.Tensor] = None
-        if self.cfg.evaluate.metrics.fad:
-            fad = builders.get_fad(self.cfg.metrics.fad).to(self.device)
-            should_run_eval = True
-        if self.cfg.evaluate.metrics.kld:
-            kldiv = builders.get_kldiv(self.cfg.metrics.kld).to(self.device)
-            should_run_eval = True
-        if self.cfg.evaluate.metrics.text_consistency:
-            text_consistency = builders.get_text_consistency(self.cfg.metrics.text_consistency).to(self.device)
-            should_run_eval = True
-        if self.cfg.evaluate.metrics.chroma_cosine:
-            chroma_cosine = builders.get_chroma_cosine_similarity(self.cfg.metrics.chroma_cosine).to(self.device)
-            # if we have predefind wavs for chroma we should purge them for computing the cosine metric
-            has_predefined_eval_chromas = 'self_wav' in self.model.condition_provider.conditioners and \
-                                          self.model.condition_provider.conditioners['self_wav'].has_eval_wavs()
-            if has_predefined_eval_chromas:
-                warn_once(self.logger, "Attempting to run cosine eval for config with pre-defined eval chromas! "
-                                       'Resetting eval chromas to None for evaluation.')
-                eval_chroma_wavs = self.model.condition_provider.conditioners.self_wav.eval_wavs  # type: ignore
-                self.model.condition_provider.conditioners.self_wav.reset_eval_wavs(None)  # type: ignore
-            should_run_eval = True
-
-        def get_compressed_audio(audio: torch.Tensor) -> torch.Tensor:
-            audio_tokens, scale = self.compression_model.encode(audio.to(self.device))
-            compressed_audio = self.compression_model.decode(audio_tokens, scale)
-            return compressed_audio[..., :audio.shape[-1]]
-
-        metrics: dict = {}
-        if should_run_eval:
-            loader = self.dataloaders['evaluate']
-            updates = len(loader)
-            lp = self.log_progress(f'{evaluate_stage_name} inference', loader, total=updates, updates=self.log_updates)
-            average = flashy.averager()
-            dataset = get_dataset_from_loader(loader)
-            assert isinstance(dataset, AudioDataset)
-            self.logger.info(f"Computing evaluation metrics on {len(dataset)} samples")
-
-            for idx, batch in enumerate(lp):
-                audio, meta = batch
-                assert all([self.cfg.sample_rate == m.sample_rate for m in meta])
-
-                target_duration = audio.shape[-1] / self.cfg.sample_rate
-                if self.cfg.evaluate.fixed_generation_duration:
-                    target_duration = self.cfg.evaluate.fixed_generation_duration
-
-                gen_outputs = self.run_generate_step(
-                    batch, gen_duration=target_duration,
-                    **self.generation_params
-                )
-                y_pred = gen_outputs['gen_audio'].detach()
-                y_pred = y_pred[..., :audio.shape[-1]]
-
-                normalize_kwargs = dict(self.cfg.generate.audio)
-                normalize_kwargs.pop('format', None)
-                y_pred = torch.stack([normalize_audio(w, **normalize_kwargs) for w in y_pred], dim=0).cpu()
-                y = audio.cpu()  # should already be on CPU but just in case
-                sizes = torch.tensor([m.n_frames for m in meta])  # actual sizes without padding
-                sample_rates = torch.tensor([m.sample_rate for m in meta])  # sample rates for audio samples
-                audio_stems = [Path(m.meta.path).stem + f"_{m.seek_time}" for m in meta]
-
-                if fad is not None:
-                    if self.cfg.metrics.fad.use_gt:
-                        y_pred = get_compressed_audio(y).cpu()
-                    fad.update(y_pred, y, sizes, sample_rates, audio_stems)
-                if kldiv is not None:
-                    if self.cfg.metrics.kld.use_gt:
-                        y_pred = get_compressed_audio(y).cpu()
-                    kldiv.update(y_pred, y, sizes, sample_rates)
-                if text_consistency is not None:
-                    texts = [m.description for m in meta]
-                    if self.cfg.metrics.text_consistency.use_gt:
-                        y_pred = y
-                    text_consistency.update(y_pred, texts, sizes, sample_rates)
-                if chroma_cosine is not None:
-                    if self.cfg.metrics.chroma_cosine.use_gt:
-                        y_pred = get_compressed_audio(y).cpu()
-                    chroma_cosine.update(y_pred, y, sizes, sample_rates)
-                    # restore chroma conditioner's eval chroma wavs
-                    if eval_chroma_wavs is not None:
-                        self.model.condition_provider.conditioners['self_wav'].reset_eval_wavs(eval_chroma_wavs)
-
-            flashy.distrib.barrier()
-            if fad is not None:
-                metrics['fad'] = fad.compute()
-            if kldiv is not None:
-                kld_metrics = kldiv.compute()
-                metrics.update(kld_metrics)
-            if text_consistency is not None:
-                metrics['text_consistency'] = text_consistency.compute()
-            if chroma_cosine is not None:
-                metrics['chroma_cosine'] = chroma_cosine.compute()
-            metrics = average(metrics)
-            metrics = flashy.distrib.average_metrics(metrics, len(loader))
-
-        return metrics
-
-    def evaluate(self) -> dict:
-        """Evaluate stage."""
-        self.model.eval()
-        with torch.no_grad():
-            metrics: dict = {}
-            if self.cfg.evaluate.metrics.base:
-                metrics.update(self.common_train_valid('evaluate'))
-            gen_metrics = self.evaluate_audio_generation()
-            return {**metrics, **gen_metrics}
diff --git a/spaces/Grezz/generate_human_motion/VQ-Trans/render_final.py b/spaces/Grezz/generate_human_motion/VQ-Trans/render_final.py
deleted file mode 100644
index 41b3bfdb2e6bff74aeaceb8f1a7ebac9dc1acaba..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/VQ-Trans/render_final.py
+++ /dev/null
@@ -1,194 +0,0 @@
-from models.rotation2xyz import Rotation2xyz
-import numpy as np
-from trimesh import Trimesh
-import os
-os.environ['PYOPENGL_PLATFORM'] = "osmesa"
-
-import torch
-from visualize.simplify_loc2rot import joints2smpl
-import pyrender
-import matplotlib.pyplot as plt
-
-import io
-import imageio
-from shapely import geometry
-import trimesh
-from pyrender.constants import RenderFlags
-import math
-# import ffmpeg
-from PIL import Image
-
-class WeakPerspectiveCamera(pyrender.Camera):
-    def __init__(self,
-                 scale,
-                 translation,
-                 znear=pyrender.camera.DEFAULT_Z_NEAR,
-                 zfar=None,
-                 name=None):
-        super(WeakPerspectiveCamera, self).__init__(
-            znear=znear,
-            zfar=zfar,
-            name=name,
-        )
-        self.scale = scale
-        self.translation = translation
-
-    def get_projection_matrix(self, width=None, height=None):
-        P = np.eye(4)
-        P[0, 0] = self.scale[0]
-        P[1, 1] = self.scale[1]
-        P[0, 3] = self.translation[0] * self.scale[0]
-        P[1, 3] = -self.translation[1] * self.scale[1]
-        P[2, 2] = -1
-        return P
-
-def render(motions, outdir='test_vis', device_id=0, name=None, pred=True):
-    frames, njoints, nfeats = motions.shape
-    MINS = motions.min(axis=0).min(axis=0)
-    MAXS = motions.max(axis=0).max(axis=0)
-
-    height_offset = MINS[1]
-    motions[:, :, 1] -= height_offset
-    trajec = motions[:, 0, [0, 2]]
-
-    j2s = joints2smpl(num_frames=frames, device_id=0, cuda=True)
-    rot2xyz = Rotation2xyz(device=torch.device("cuda:0"))
-    faces = rot2xyz.smpl_model.faces
-
-    if (not os.path.exists(outdir + name+'_pred.pt') and pred) or (not os.path.exists(outdir + name+'_gt.pt') and not pred): 
-        print(f'Running SMPLify, it may take a few minutes.')
-        motion_tensor, opt_dict = j2s.joint2smpl(motions)  # [nframes, njoints, 3]
-
-        vertices = rot2xyz(torch.tensor(motion_tensor).clone(), mask=None,
-                                        pose_rep='rot6d', translation=True, glob=True,
-                                        jointstype='vertices',
-                                        vertstrans=True)
-
-        if pred:
-            torch.save(vertices, outdir + name+'_pred.pt')
-        else:
-            torch.save(vertices, outdir + name+'_gt.pt')
-    else:
-        if pred:
-            vertices = torch.load(outdir + name+'_pred.pt')
-        else:
-            vertices = torch.load(outdir + name+'_gt.pt')
-    frames = vertices.shape[3] # shape: 1, nb_frames, 3, nb_joints
-    print (vertices.shape)
-    MINS = torch.min(torch.min(vertices[0], axis=0)[0], axis=1)[0]
-    MAXS = torch.max(torch.max(vertices[0], axis=0)[0], axis=1)[0]
-    # vertices[:,:,1,:] -= MINS[1] + 1e-5
-
-
-    out_list = []
-    
-    minx = MINS[0] - 0.5
-    maxx = MAXS[0] + 0.5
-    minz = MINS[2] - 0.5 
-    maxz = MAXS[2] + 0.5
-    polygon = geometry.Polygon([[minx, minz], [minx, maxz], [maxx, maxz], [maxx, minz]])
-    polygon_mesh = trimesh.creation.extrude_polygon(polygon, 1e-5)
-
-    vid = []
-    for i in range(frames):
-        if i % 10 == 0:
-            print(i)
-
-        mesh = Trimesh(vertices=vertices[0, :, :, i].squeeze().tolist(), faces=faces)
-
-        base_color = (0.11, 0.53, 0.8, 0.5)
-        ## OPAQUE rendering without alpha
-        ## BLEND rendering consider alpha 
-        material = pyrender.MetallicRoughnessMaterial(
-            metallicFactor=0.7,
-            alphaMode='OPAQUE',
-            baseColorFactor=base_color
-        )
-
-
-        mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
-
-        polygon_mesh.visual.face_colors = [0, 0, 0, 0.21]
-        polygon_render = pyrender.Mesh.from_trimesh(polygon_mesh, smooth=False)
-
-        bg_color = [1, 1, 1, 0.8]
-        scene = pyrender.Scene(bg_color=bg_color, ambient_light=(0.4, 0.4, 0.4))
-        
-        sx, sy, tx, ty = [0.75, 0.75, 0, 0.10]
-
-        camera = pyrender.PerspectiveCamera(yfov=(np.pi / 3.0))
-
-        light = pyrender.DirectionalLight(color=[1,1,1], intensity=300)
-
-        scene.add(mesh)
-
-        c = np.pi / 2
-
-        scene.add(polygon_render, pose=np.array([[ 1, 0, 0, 0],
-
-        [ 0, np.cos(c), -np.sin(c), MINS[1].cpu().numpy()],
-
-        [ 0, np.sin(c), np.cos(c), 0],
-
-        [ 0, 0, 0, 1]]))
-
-        light_pose = np.eye(4)
-        light_pose[:3, 3] = [0, -1, 1]
-        scene.add(light, pose=light_pose.copy())
-
-        light_pose[:3, 3] = [0, 1, 1]
-        scene.add(light, pose=light_pose.copy())
-
-        light_pose[:3, 3] = [1, 1, 2]
-        scene.add(light, pose=light_pose.copy())
-
-
-        c = -np.pi / 6
-
-        scene.add(camera, pose=[[ 1, 0, 0, (minx+maxx).cpu().numpy()/2],
-
-                                [ 0, np.cos(c), -np.sin(c), 1.5],
-
-                                [ 0, np.sin(c), np.cos(c), max(4, minz.cpu().numpy()+(1.5-MINS[1].cpu().numpy())*2, (maxx-minx).cpu().numpy())],
-
-                                [ 0, 0, 0, 1]
-                                ])
-        
-        # render scene
-        r = pyrender.OffscreenRenderer(960, 960)
-
-        color, _ = r.render(scene, flags=RenderFlags.RGBA)
-        # Image.fromarray(color).save(outdir+'/'+name+'_'+str(i)+'.png')
-
-        vid.append(color)
-
-        r.delete()
-
-    out = np.stack(vid, axis=0)
-    if pred:
-        imageio.mimsave(outdir + name+'_pred.gif', out, fps=20)
-    else:
-        imageio.mimsave(outdir + name+'_gt.gif', out, fps=20)
-    
-
-
-
-
-if __name__ == "__main__":
-    import argparse
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--filedir", type=str, default=None, help='motion npy file dir')
-    parser.add_argument('--motion-list', default=None, nargs="+", type=str, help="motion name list")
-    args = parser.parse_args()
-
-    filename_list = args.motion_list
-    filedir = args.filedir
-    
-    for filename in filename_list:
-        motions = np.load(filedir + filename+'_pred.npy')
-        print('pred', motions.shape, filename)
-        render(motions[0], outdir=filedir, device_id=0, name=filename, pred=True)
-
-        motions = np.load(filedir + filename+'_gt.npy')
-        print('gt', motions.shape, filename)
-        render(motions[0], outdir=filedir, device_id=0, name=filename, pred=False)
diff --git a/spaces/GroveStreet/GTA_SOVITS/vencoder/dphubert/hardconcrete.py b/spaces/GroveStreet/GTA_SOVITS/vencoder/dphubert/hardconcrete.py
deleted file mode 100644
index 468a30d1eccdf20ee7493e71792c46e48449c4e3..0000000000000000000000000000000000000000
--- a/spaces/GroveStreet/GTA_SOVITS/vencoder/dphubert/hardconcrete.py
+++ /dev/null
@@ -1,122 +0,0 @@
-"""Implementation of the hard Concrete distribution.
-
-Originally from:
-https://github.com/asappresearch/flop/blob/master/flop/hardconcrete.py
-
-"""
-
-import math
-
-import torch
-import torch.nn as nn
-
-
-class HardConcrete(nn.Module):
-    """A HarcConcrete module.
-    Use this module to create a mask of size N, which you can
-    then use to perform L0 regularization.
-
-    To obtain a mask, simply run a forward pass through the module
-    with no input data. The mask is sampled in training mode, and
-    fixed during evaluation mode, e.g.:
-
-    >>> module = HardConcrete(n_in=100)
-    >>> mask = module()
-    >>> norm = module.l0_norm()
-    """
-
-    def __init__(
-        self,
-        n_in: int,
-        init_mean: float = 0.5,
-        init_std: float = 0.01,
-        temperature: float = 2/3,     # from CoFi
-        stretch: float = 0.1,
-        eps: float = 1e-6
-    ) -> None:
-        """Initialize the HardConcrete module.
-        Parameters
-        ----------
-        n_in : int
-            The number of hard concrete variables in this mask.
-        init_mean : float, optional
-            Initial drop rate for hard concrete parameter,
-            by default 0.5.,
-        init_std: float, optional
-            Used to initialize the hard concrete parameters,
-            by default 0.01.
-        temperature : float, optional
-            Temperature used to control the sharpness of the
-            distribution, by default 1.0
-        stretch : float, optional
-            Stretch the sampled value from [0, 1] to the interval
-            [-stretch, 1 + stretch], by default 0.1.
-        """
-        super().__init__()
-
-        self.n_in = n_in
-        self.limit_l = -stretch
-        self.limit_r = 1.0 + stretch
-        self.log_alpha = nn.Parameter(torch.zeros(n_in))
-        self.beta = temperature
-        self.init_mean = init_mean
-        self.init_std = init_std
-        self.bias = -self.beta * math.log(-self.limit_l / self.limit_r)
-
-        self.eps = eps
-        self.compiled_mask = None
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        """Reset the parameters of this module."""
-        self.compiled_mask = None
-        mean = math.log(1 - self.init_mean) - math.log(self.init_mean)
-        self.log_alpha.data.normal_(mean, self.init_std)
-
-    def l0_norm(self) -> torch.Tensor:
-        """Compute the expected L0 norm of this mask.
-        Returns
-        -------
-        torch.Tensor
-            The expected L0 norm.
-        """
-        return (self.log_alpha + self.bias).sigmoid().sum()
-
-    def forward(self) -> torch.Tensor:
-        """Sample a hard concrete mask.
-        Returns
-        -------
-        torch.Tensor
-            The sampled binary mask
-        """
-        if self.training:
-            # Reset the compiled mask
-            self.compiled_mask = None
-            # Sample mask dynamically
-            u = self.log_alpha.new(self.n_in).uniform_(self.eps, 1 - self.eps)
-            s = torch.sigmoid((torch.log(u / (1 - u)) + self.log_alpha) / self.beta)
-            s = s * (self.limit_r - self.limit_l) + self.limit_l
-            mask = s.clamp(min=0., max=1.)
-
-        else:
-            # Compile new mask if not cached
-            if self.compiled_mask is None:
-                # Get expected sparsity
-                expected_num_zeros = self.n_in - self.l0_norm().item()
-                num_zeros = round(expected_num_zeros)
-                # Approximate expected value of each mask variable z;
-                # We use an empirically validated magic number 0.8
-                soft_mask = torch.sigmoid(self.log_alpha / self.beta * 0.8)
-                # Prune small values to set to 0
-                _, indices = torch.topk(soft_mask, k=num_zeros, largest=False)
-                soft_mask[indices] = 0.
-                self.compiled_mask = soft_mask
-            mask = self.compiled_mask
-
-        return mask
-
-    def extra_repr(self) -> str:
-        return str(self.n_in)
-
-    def __repr__(self) -> str:
-        return "{}({})".format(self.__class__.__name__, self.extra_repr())
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/taskonomy/task_configs.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/taskonomy/task_configs.py
deleted file mode 100644
index c6886d969775f3f36eca94fd19ca7fa936fd44e1..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/taskonomy/task_configs.py
+++ /dev/null
@@ -1,105 +0,0 @@
-####################
-# Tasks
-####################
-
-task_parameters = {
-    'class_object':{
-        'num_classes': 1000,
-        'ext': 'npy',
-        'domain_id': 'class_object',
-    },
-    'class_scene':{
-        'num_classes': 365,
-        'ext': 'npy',
-        'domain_id': 'class_scene',
-    },
-    'depth_zbuffer':{
-        'num_channels': 1,
-        'mask_val': 1.0,
-        'clamp_to': (0.0, 8000.0 / (2**16 - 1)), # Same as consistency
-        'ext': 'png',
-        'domain_id': 'depth_zbuffer',
-    },
-    'depth_euclidean':{
-        'num_channels': 1,
-        'clamp_to': (0.0, 8000.0 / (2**16 - 1)), # Same as consistency
-#         'mask_val': 1.0,
-        'ext': 'png',
-        'domain_id': 'depth_euclidean',
-    },
-    'edge_texture': {
-        'num_channels': 1,
-        'clamp_to': (0.0, 0.25),
-        #'threshold_min': 0.01,
-        'ext': 'png',
-        'domain_id': 'edge_texture',
-    },
-    'edge_occlusion': {
-        'num_channels': 1,
-        #'clamp_to': (0.0, 0.04),
-        #'threshold_min': 0.0017,
-        'ext': 'png',
-        'domain_id': 'edge_occlusion',
-    },
-    'keypoints3d': {
-        'num_channels': 1,
-        'ext': 'png',
-        'domain_id': 'keypoints3d',
-    },
-    'keypoints2d':{
-        'num_channels': 1,
-        #'clamp_to': (0.0, 0.025),
-        #'threshold_min': 0.002,
-        'ext': 'png',
-        'domain_id': 'keypoints2d',
-    },
-    'principal_curvature':{
-        'num_channels': 3,
-        'mask_val': 0.0,
-        'ext': 'png',
-        'domain_id': 'principal_curvature',
-    },
-    'reshading':{
-        'num_channels': 1,
-        'ext': 'png',
-        'domain_id': 'reshading',
-    }, 
-    'normal':{
-        'num_channels': 3,
-        'mask_val': 0.502,
-        'ext': 'png',
-        'domain_id': 'normal',
-    },
-    'mask_valid':{
-        'num_channels': 1,
-        'mask_val': 0.0,
-        'ext': 'png',
-        'domain_id': 'depth_zbuffer',
-    },
-    'rgb':{
-        'num_channels': 3,
-        'ext': 'png',
-        'domain_id': 'rgb',
-    },
-    'segment_semantic': {
-        'num_channels': 18,
-        'ext': 'png',
-        'domain_id': 'segmentsemantic',
-    },
-    'segment_unsup2d':{
-        'num_channels': 64,
-        'ext': 'png',
-        'domain_id': 'segment_unsup2d',
-    },
-    'segment_unsup25d':{
-        'num_channels': 64,
-        'ext': 'png',
-        'domain_id': 'segment_unsup25d',
-    },
-}
-
-        
-PIX_TO_PIX_TASKS = ['colorization', 'edge_texture', 'edge_occlusion',  'keypoints3d', 'keypoints2d', 'reshading', 'depth_zbuffer', 'depth_euclidean', 'curvature', 'autoencoding', 'denoising', 'normal', 'inpainting', 'segment_unsup2d', 'segment_unsup25d', 'segment_semantic', ]
-FEED_FORWARD_TASKS = ['class_object', 'class_scene', 'room_layout', 'vanishing_point']
-SINGLE_IMAGE_TASKS = PIX_TO_PIX_TASKS + FEED_FORWARD_TASKS
-SIAMESE_TASKS = ['fix_pose', 'jigsaw', 'ego_motion', 'point_match', 'non_fixated_pose']
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/scoring/tokenizer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/scoring/tokenizer.py
deleted file mode 100644
index 61cf6d4a7cc698258caad9f68f2e8559dd510eee..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/scoring/tokenizer.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unicodedata
-
-from fairseq.dataclass import ChoiceEnum
-
-
-class EvaluationTokenizer(object):
-    """A generic evaluation-time tokenizer, which leverages built-in tokenizers
-    in sacreBLEU (https://github.com/mjpost/sacrebleu). It additionally provides
-    lowercasing, punctuation removal and character tokenization, which are
-    applied after sacreBLEU tokenization.
-
-    Args:
-        tokenizer_type (str): the type of sacreBLEU tokenizer to apply.
-        lowercase (bool): lowercase the text.
-        punctuation_removal (bool): remove punctuation (based on unicode
-        category) from text.
-        character_tokenization (bool): tokenize the text to characters.
-    """
-
-    SPACE = chr(32)
-    SPACE_ESCAPE = chr(9601)
-    ALL_TOKENIZER_TYPES = ChoiceEnum(["none", "13a", "intl", "zh", "ja-mecab"])
-
-    def __init__(
-        self,
-        tokenizer_type: str = "13a",
-        lowercase: bool = False,
-        punctuation_removal: bool = False,
-        character_tokenization: bool = False,
-    ):
-        from sacrebleu.tokenizers import TOKENIZERS
-
-        assert tokenizer_type in TOKENIZERS, f"{tokenizer_type}, {TOKENIZERS}"
-        self.lowercase = lowercase
-        self.punctuation_removal = punctuation_removal
-        self.character_tokenization = character_tokenization
-        self.tokenizer = TOKENIZERS[tokenizer_type]
-
-    @classmethod
-    def remove_punctuation(cls, sent: str):
-        """Remove punctuation based on Unicode category."""
-        return cls.SPACE.join(
-            t
-            for t in sent.split(cls.SPACE)
-            if not all(unicodedata.category(c)[0] == "P" for c in t)
-        )
-
-    def tokenize(self, sent: str):
-        tokenized = self.tokenizer()(sent)
-
-        if self.punctuation_removal:
-            tokenized = self.remove_punctuation(tokenized)
-
-        if self.character_tokenization:
-            tokenized = self.SPACE.join(
-                list(tokenized.replace(self.SPACE, self.SPACE_ESCAPE))
-            )
-
-        if self.lowercase:
-            tokenized = tokenized.lower()
-
-        return tokenized
diff --git a/spaces/Hello-SimpleAI/chatgpt-detector-ling/app.py b/spaces/Hello-SimpleAI/chatgpt-detector-ling/app.py
deleted file mode 100644
index 63a5a89c2b7bdd3f666e30e607f7c88d45715c0d..0000000000000000000000000000000000000000
--- a/spaces/Hello-SimpleAI/chatgpt-detector-ling/app.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Copyright (c) Hello-SimpleAI Org. 2023.
-# Licensed under the Apache License, Version 2.0.
-
-import os
-import pickle
-import re
-from typing import Callable, List, Tuple
-
-import gradio as gr
-from nltk.data import load as nltk_load
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-import torch
-from transformers.utils import cached_file
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-
-AUTH_TOKEN = os.environ.get("access_token")
-DET_LING_ID = 'Hello-SimpleAI/chatgpt-detector-ling'
-
-
-def download_file(filename):
-    return cached_file(DET_LING_ID, filename, use_auth_token=AUTH_TOKEN)
-
-
-NLTK = nltk_load(download_file('english.pickle'))
-sent_cut_en = NLTK.tokenize
-LR_GLTR_EN, LR_PPL_EN, LR_GLTR_ZH, LR_PPL_ZH = [
-    pickle.load(open(download_file(f'{lang}-gpt2-{name}.pkl'), 'rb'))
-    for lang, name in [('en', 'gltr'), ('en', 'ppl'), ('zh', 'gltr'), ('zh', 'ppl')]
-]
-
-NAME_EN = 'gpt2'
-TOKENIZER_EN = GPT2Tokenizer.from_pretrained(NAME_EN)
-MODEL_EN = GPT2LMHeadModel.from_pretrained(NAME_EN)
-
-NAME_ZH = 'IDEA-CCNL/Wenzhong-GPT2-110M'
-TOKENIZER_ZH = GPT2Tokenizer.from_pretrained(NAME_ZH)
-MODEL_ZH = GPT2LMHeadModel.from_pretrained(NAME_ZH)
-
-
-# code borrowed from https://github.com/blmoistawinde/HarvestText
-def sent_cut_zh(para: str) -> List[str]:
-    para = re.sub('([。！？\?!])([^”’)\]）】])', r"\1\n\2", para)  # 单字符断句符
-    para = re.sub('(\.{3,})([^”’)\]）】….])', r"\1\n\2", para)  # 英文省略号
-    para = re.sub('(\…+)([^”’)\]）】….])', r"\1\n\2", para)  # 中文省略号
-    para = re.sub('([。！？\?!]|\.{3,}|\…+)([”’)\]）】])([^，。！？\?….])', r'\1\2\n\3', para)
-    # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\n放到双引号后，注意前面的几句都小心保留了双引号
-    para = para.rstrip()  # 段尾如果有多余的\n就去掉它
-    # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。
-    sentences = para.split("\n")
-    sentences = [sent.strip() for sent in sentences]
-    sentences = [sent for sent in sentences if len(sent.strip()) > 0]
-    return sentences
-
-
-CROSS_ENTROPY = torch.nn.CrossEntropyLoss(reduction='none')
-
-
-def gpt2_features(
-    text: str, tokenizer: GPT2Tokenizer, model: GPT2LMHeadModel, sent_cut: Callable
-) -> Tuple[List[int], List[float]]:
-    # Tokenize
-    input_max_length = tokenizer.model_max_length - 2
-    token_ids, offsets = list(), list()
-    sentences = sent_cut(text)
-    for s in sentences:
-        tokens = tokenizer.tokenize(s)
-        ids = tokenizer.convert_tokens_to_ids(tokens)
-        difference = len(token_ids) + len(ids) - input_max_length
-        if difference > 0:
-            ids = ids[:-difference]
-        offsets.append((len(token_ids), len(token_ids) + len(ids)))  # 左开右闭
-        token_ids.extend(ids)
-        if difference >= 0:
-            break
-
-    input_ids = torch.tensor([tokenizer.bos_token_id] + token_ids)
-    logits = model(input_ids).logits
-    # Shift so that n-1 predict n
-    shift_logits = logits[:-1].contiguous()
-    shift_target = input_ids[1:].contiguous()
-    loss = CROSS_ENTROPY(shift_logits, shift_target)
-
-    all_probs = torch.softmax(shift_logits, dim=-1)
-    sorted_ids = torch.argsort(all_probs, dim=-1, descending=True)  # stable=True
-    expanded_tokens = shift_target.unsqueeze(-1).expand_as(sorted_ids)
-    indices = torch.where(sorted_ids == expanded_tokens)
-    rank = indices[-1]
-    counter = [
-        rank < 10,
-        (rank >= 10) & (rank < 100),
-        (rank >= 100) & (rank < 1000),
-        rank >= 1000
-    ]
-    counter = [c.long().sum(-1).item() for c in counter]
-
-
-    # compute different-level ppl
-    text_ppl = loss.mean().exp().item()
-    sent_ppl = list()
-    for start, end in offsets:
-        nll = loss[start: end].sum() / (end - start)
-        sent_ppl.append(nll.exp().item())
-    max_sent_ppl = max(sent_ppl)
-    sent_ppl_avg = sum(sent_ppl) / len(sent_ppl)
-    if len(sent_ppl) > 1:
-        sent_ppl_std = torch.std(torch.tensor(sent_ppl)).item()
-    else:
-        sent_ppl_std = 0
-
-    mask = torch.tensor([1] * loss.size(0))
-    step_ppl = loss.cumsum(dim=-1).div(mask.cumsum(dim=-1)).exp()
-    max_step_ppl = step_ppl.max(dim=-1)[0].item()
-    step_ppl_avg = step_ppl.sum(dim=-1).div(loss.size(0)).item()
-    if step_ppl.size(0) > 1:
-        step_ppl_std = step_ppl.std().item()
-    else:
-        step_ppl_std = 0
-    ppls = [
-        text_ppl, max_sent_ppl, sent_ppl_avg, sent_ppl_std,
-        max_step_ppl, step_ppl_avg, step_ppl_std
-    ]
-    return counter, ppls  # type: ignore
-
-
-def lr_predict(
-    f_gltr: List[int], f_ppl: List[float], lr_gltr: LogisticRegression, lr_ppl: LogisticRegression,
-    id_to_label: List[str]
-) -> List:
-    x_gltr = np.asarray([f_gltr])
-    gltr_label = lr_gltr.predict(x_gltr)[0]
-    gltr_prob = lr_gltr.predict_proba(x_gltr)[0, gltr_label]
-    x_ppl = np.asarray([f_ppl])
-    ppl_label = lr_ppl.predict(x_ppl)[0]
-    ppl_prob = lr_ppl.predict_proba(x_ppl)[0, ppl_label]
-    return [id_to_label[gltr_label], gltr_prob, id_to_label[ppl_label], ppl_prob]
-
-
-def predict_en(text: str) -> List:
-    with torch.no_grad():
-        feat = gpt2_features(text, TOKENIZER_EN, MODEL_EN, sent_cut_en)
-    out = lr_predict(*feat, LR_GLTR_EN, LR_PPL_EN, ['Human', 'ChatGPT'])
-    return out
-
-
-def predict_zh(text: str) -> List:
-    with torch.no_grad():
-        feat = gpt2_features(text, TOKENIZER_ZH, MODEL_ZH, sent_cut_zh)
-    out = lr_predict(*feat, LR_GLTR_ZH, LR_PPL_ZH, ['人类', 'ChatGPT'])
-    return out
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        ## ChatGPT Detector 🔬 (Linguistic version / 语言学版)
-
-        Visit our project on Github: [chatgpt-comparison-detection project](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)<br>
-        欢迎在 Github 上关注我们的 [ChatGPT 对比与检测项目](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)<br>
-        We provide three kinds of detectors, all in Bilingual / 我们提供了三个版本的检测器，且都支持中英文:
-        - [QA version / 问答版](https://huggingface.co/spaces/Hello-SimpleAI/chatgpt-detector-qa)<br>
-            detect whether an **answer** is generated by ChatGPT for certain **question**, using PLM-based classifiers / 判断某个**问题的回答**是否由ChatGPT生成，使用基于PTM的分类器来开发;
-        - [Sinlge-text version / 独立文本版](https://huggingface.co/spaces/Hello-SimpleAI/chatgpt-detector-single)<br>
-            detect whether a piece of text is ChatGPT generated, using PLM-based classifiers / 判断**单条文本**是否由ChatGPT生成，使用基于PTM的分类器来开发;
-        - [**Linguistic version / 语言学版** (👈 Current / 当前使用)](https://huggingface.co/spaces/Hello-SimpleAI/chatgpt-detector-ling)<br>
-            detect whether a piece of text is ChatGPT generated, using linguistic features / 判断**单条文本**是否由ChatGPT生成，使用基于语言学特征的模型来开发;
-
-        """
-    )
-
-    with gr.Tab("English"):
-        gr.Markdown(
-            """
-            ## Introduction:
-            Two Logistic regression models trained with two kinds of features:
-            1. [GLTR](https://aclanthology.org/P19-3019) Test-2, Language model predict token rank top-k buckets, top 10, 10-100, 100-1000, 1000+.
-            2. PPL-based, text ppl, sentence ppl, etc.
-
-            English LM is [GPT2-small](https://huggingface.co/gpt2).
-
-            Note: Providing more text to the `Text` box can make the prediction more accurate!
-            """
-        )
-        a1 = gr.Textbox(
-            lines=5, label='Text',
-            value="There are a few things that can help protect your credit card information from being misused when you give it to a restaurant or any other business:\n\nEncryption: Many businesses use encryption to protect your credit card information when it is being transmitted or stored. This means that the information is transformed into a code that is difficult for anyone to read without the right key."
-        )
-        button1 = gr.Button("🤖 Predict!")
-        gr.Markdown("GLTR")
-        label1_gltr = gr.Textbox(lines=1, label='GLTR Predicted Label 🎃')
-        score1_gltr = gr.Textbox(lines=1, label='GLTR Probability')
-        gr.Markdown("PPL")
-        label1_ppl = gr.Textbox(lines=1, label='PPL Predicted Label 🎃')
-        score1_ppl = gr.Textbox(lines=1, label='PPL Probability')
-
-    with gr.Tab("中文版"):
-        gr.Markdown(
-            """
-            ## 介绍:
-            两个逻辑回归模型, 分别使用以下两种特征:
-            1. [GLTR](https://aclanthology.org/P19-3019) Test-2, 每个词的语言模型预测排名分桶, top 10, 10-100, 100-1000, 1000+.
-            2. 基于语言模型困惑度 (PPL), 整个文本的PPL、单个句子的PPL等特征.
-
-            中文语言模型使用 闻仲 [Wenzhong-GPT2-110M](https://huggingface.co/IDEA-CCNL/Wenzhong-GPT2-110M).
-
-            注意: 在`文本`栏中输入更多的文本，可以让预测更准确哦！
-            """
-        )
-        a2 = gr.Textbox(
-            lines=5, label='文本',
-            value="对于OpenAI大力出奇迹的工作，自然每个人都有自己的看点。我自己最欣赏的地方是ChatGPT如何解决 “AI校正(Alignment)“这个问题。这个问题也是我们课题组这两年在探索的学术问题之一。"
-        )
-        button2 = gr.Button("🤖 预测!")
-        gr.Markdown("GLTR (中文测试集准确率 86.39%)")
-        label2_gltr = gr.Textbox(lines=1, label='预测结果 🎃')
-        score2_gltr = gr.Textbox(lines=1, label='模型概率')
-        gr.Markdown("PPL (中文测试集准确率 59.04%, 持续优化中...)")
-        label2_ppl = gr.Textbox(lines=1, label='PPL 预测结果 🎃')
-        score2_ppl = gr.Textbox(lines=1, label='PPL 模型概率')
-
-    button1.click(predict_en, inputs=[a1], outputs=[label1_gltr, score1_gltr, label1_ppl, score1_ppl])
-    button2.click(predict_zh, inputs=[a2], outputs=[label2_gltr, score2_gltr, label2_ppl, score2_ppl])
-
-    # Page Count
-    gr.Markdown("""
-                <center><a href="https://clustrmaps.com/site/1bsdd" title="Visit tracker"><img src="//clustrmaps.com/map_v2.png?cl=080808&w=a&t=tt&d=NvxUHBTxY0ECXEuebgz8Ym8ynpVtduq59ENXoQpFh74&co=ffffff&ct=808080"/></a></center>
-                """)
-
-demo.launch()
-# To create a public link, set `share=True` in `launch()`.
diff --git a/spaces/Heshwa/html-code-generation-from-images-with-deep-neural-networks/classes/__init__.py b/spaces/Heshwa/html-code-generation-from-images-with-deep-neural-networks/classes/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HuggingFaceH4/open_llm_leaderboard/src/assets/css_html_js.py b/spaces/HuggingFaceH4/open_llm_leaderboard/src/assets/css_html_js.py
deleted file mode 100644
index 8215be3b3547c57f4d75a1448a2407334e16fb6d..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceH4/open_llm_leaderboard/src/assets/css_html_js.py
+++ /dev/null
@@ -1,111 +0,0 @@
-custom_css = """
-
-.markdown-text {
-    font-size: 16px !important;
-}
-
-#models-to-add-text {
-    font-size: 18px !important;
-}
-
-#citation-button span {
-    font-size: 16px !important;
-}
-
-#citation-button textarea {
-    font-size: 16px !important;
-}
-
-#citation-button > label > button {
-    margin: 6px;
-    transform: scale(1.3);
-}
-
-#leaderboard-table {
-    margin-top: 15px
-}
-
-#leaderboard-table-lite {
-    margin-top: 15px
-}
-
-#search-bar-table-box > div:first-child {
-    background: none;
-    border: none;
-}
- 
-#search-bar {
-    padding: 0px;
-}
-
-/* Hides the final AutoEvalColumn */
-#llm-benchmark-tab-table table td:last-child,
-#llm-benchmark-tab-table table th:last-child {
-    display: none;
-}
-
-/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
-table td:first-child,
-table th:first-child {
-    max-width: 400px;
-    overflow: auto;
-    white-space: nowrap;
-}
-
-.tab-buttons button {
-    font-size: 20px;
-}
-
-#scale-logo {
-    border-style: none !important;
-    box-shadow: none;
-    display: block;
-    margin-left: auto;
-    margin-right: auto;
-    max-width: 600px;
-}
-
-#scale-logo .download {
-    display: none;
-}
-#filter_type{
-    border: 0;
-    padding-left: 0;
-    padding-top: 0;
-}
-#filter_type label {
-    display: flex;
-}
-#filter_type label > span{
-    margin-top: var(--spacing-lg);
-    margin-right: 0.5em;
-}
-#filter_type label > .wrap{
-    width: 103px;
-}
-#filter_type label > .wrap .wrap-inner{  
-    padding: 2px;
-}
-#filter_type label > .wrap .wrap-inner input{
-    width: 1px
-}
-#filter-columns-type{
-    border:0;
-    padding:0.5;
-}
-#filter-columns-size{
-    border:0;
-    padding:0.5;
-}
-#box-filter > .form{
-    border: 0
-}
-"""
-
-get_window_url_params = """
-    function(url_params) {
-        const params = new URLSearchParams(window.location.search);
-        url_params = Object.fromEntries(params);
-        return url_params;
-    }
-    """
diff --git a/spaces/ICML2022/OFA/data/mm_data/__init__.py b/spaces/ICML2022/OFA/data/mm_data/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Illia56/llama-2-7b-chat/run-app.sh b/spaces/Illia56/llama-2-7b-chat/run-app.sh
deleted file mode 100644
index a63a8e06f941fc702fd223ea3d4de2e28692824e..0000000000000000000000000000000000000000
--- a/spaces/Illia56/llama-2-7b-chat/run-app.sh
+++ /dev/null
@@ -1 +0,0 @@
-nodemon -w app.py -x python app.py
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
deleted file mode 100644
index 346f5f727bb87c66e4777894fc4f6726fe82b6f3..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
+++ /dev/null
@@ -1,601 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import torch
-
-import PIL
-from diffusers.utils import is_accelerate_available
-from packaging import version
-from transformers import CLIPFeatureExtractor, XLMRobertaTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, deprecate, logging
-from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from . import AltDiffusionPipelineOutput, RobertaSeriesModelWithTransformation
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
-def preprocess(image):
-    w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
-class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-guided image to image generation using Alt Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`RobertaSeriesModelWithTransformation`]):
-            Frozen text-encoder. Alt Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.RobertaSeriesModelWithTransformation),
-            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`XLMRobertaTokenizer`):
-            Tokenizer of class
-            [XLMRobertaTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.XLMRobertaTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: RobertaSeriesModelWithTransformation,
-        tokenizer: XLMRobertaTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Alt Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
-        Args:
-            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
-                `attention_head_dim` must be a multiple of `slice_size`.
-        """
-        if slice_size == "auto":
-            if isinstance(self.unet.config.attention_head_dim, int):
-                # half the attention head size is usually a good trade-off between
-                # speed and memory
-                slice_size = self.unet.config.attention_head_dim // 2
-            else:
-                # if `attention_head_dim` is a list, take the smallest head size
-                slice_size = min(self.unet.config.attention_head_dim)
-
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-        if self.safety_checker is not None:
-            # TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate
-            # fix by only offloading self.safety_checker for now
-            cpu_offload(self.safety_checker.vision_model, device)
-
-    @property
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `list(int)`):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-        """
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
-
-        if not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-            attention_mask = text_inputs.attention_mask.to(device)
-        else:
-            attention_mask = None
-
-        text_embeddings = self.text_encoder(
-            text_input_ids.to(device),
-            attention_mask=attention_mask,
-        )
-        text_embeddings = text_embeddings[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
-        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            uncond_embeddings = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            uncond_embeddings = uncond_embeddings[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
-            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        return text_embeddings
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    def decode_latents(self, latents):
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(self, prompt, strength, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [1.0, 1.0] but is {strength}")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
-        image = image.to(device=device, dtype=dtype)
-        init_latent_dist = self.vae.encode(image).latent_dist
-        init_latents = init_latent_dist.sample(generator=generator)
-        init_latents = 0.18215 * init_latents
-
-        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
-            # expand init_latents for batch_size
-            deprecation_message = (
-                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
-                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
-                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
-                " your script to pass as many initial images as text prompts to suppress this warning."
-            )
-            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
-            additional_image_per_prompt = batch_size // init_latents.shape[0]
-            init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)
-        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
-            )
-        else:
-            init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
-
-        # add noise to latents using the timesteps
-        noise = torch.randn(init_latents.shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        latents = init_latents
-
-        return latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        image: Union[torch.FloatTensor, PIL.Image.Image],
-        strength: float = 0.8,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: Optional[float] = 0.0,
-        generator: Optional[torch.Generator] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            image (`torch.FloatTensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter will be modulated by `strength`.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        message = "Please use `image` instead of `init_image`."
-        init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs)
-        image = init_image or image
-
-        # 1. Check inputs
-        self.check_inputs(prompt, strength, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # 4. Preprocess image
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
-
-        # 5. set timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
-        # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, device, generator
-        )
-
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 8. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 9. Post-processing
-        image = self.decode_latents(latents)
-
-        # 10. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
-
-        # 11. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return AltDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/Jamel887/Rvc-tio887/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py b/spaces/Jamel887/Rvc-tio887/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
deleted file mode 100644
index b412ba2814e114ca7bb00b6fd6ef217f63d788a3..0000000000000000000000000000000000000000
--- a/spaces/Jamel887/Rvc-tio887/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class HarvestF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.hop_length,
-            f0_ceil=self.f0_max,
-            f0_floor=self.f0_min,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/Jerimee/HelloWorld/app.css b/spaces/Jerimee/HelloWorld/app.css
deleted file mode 100644
index 814e812ac5abc11ea4367cadd6313860f6fbbeae..0000000000000000000000000000000000000000
--- a/spaces/Jerimee/HelloWorld/app.css
+++ /dev/null
@@ -1,6 +0,0 @@
-button.gr-button-primary {
-    background-color: greenyellow;
-    border-color: yellowgreen;
-    border-width: 3px;
-    color: darkgreen;
-}
diff --git a/spaces/KPatrick/PaddleSpeechTTS/README.md b/spaces/KPatrick/PaddleSpeechTTS/README.md
deleted file mode 100644
index 402cbe42ac523ee466c6e34b3140b4b1961923e0..0000000000000000000000000000000000000000
--- a/spaces/KPatrick/PaddleSpeechTTS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PaddleSpeechTTS
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 2.8.10
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/KarmaCST/Dzongkha-To-English-Translation-NLLB-Fine-tuning/app.py b/spaces/KarmaCST/Dzongkha-To-English-Translation-NLLB-Fine-tuning/app.py
deleted file mode 100644
index 37f401baae4df8666a0f2393d7f5f7241cb8691e..0000000000000000000000000000000000000000
--- a/spaces/KarmaCST/Dzongkha-To-English-Translation-NLLB-Fine-tuning/app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-import torch
-
-model = AutoModelForSeq2SeqLM.from_pretrained("KarmaCST/nllb-200-distilled-600M-dz-to-en")
-tokenizer = AutoTokenizer.from_pretrained("KarmaCST/nllb-200-distilled-600M-dz-to-en")
-
-
-src_lang = "dzo_Tibt"
-tgt_lang = "eng_Latn"
-
-
-def translate(text):
-    translation_pipeline = pipeline("translation",
-                                    model=model,
-                                    tokenizer=tokenizer,
-                                    src_lang=src_lang,
-                                    tgt_lang=tgt_lang)
-
-    result = translation_pipeline(text)
-    return result[0]['translation_text']
-
-
-gr.Interface(
-    translate,
-    [
-        gr.components.Textbox(label="Input Sentence", placeholder = " Enter Dzongkha sentence here ...")
-    ],
-    ["text"],
-).launch()
\ No newline at end of file
diff --git a/spaces/KenjieDec/GPEN/face_model/op/__init__.py b/spaces/KenjieDec/GPEN/face_model/op/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/KenjieDec/GPEN/face_model/op/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/Kimata/Sanskrit-TTS/app.py b/spaces/Kimata/Sanskrit-TTS/app.py
deleted file mode 100644
index cb418fd0247437a6b147fc0a53386206ff57703c..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/app.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import commons
-import utils
-import re
-
-from models import SynthesizerTrn
-from text import text_to_sequence
-import numpy as np
-from mel_processing import spectrogram_torch
-import gradio as gr
-import soundfile as sf
-from indic_transliteration import sanscript
-from cleaner_utils import pipeline, voice_smoothening, g2p_pipeline
-
-SCRIPT_DICT={
-    'Devanagari':sanscript.DEVANAGARI
-}
-DEFAULT_TEXT='संस्कृतम् जगतः एकतमा अतिप्राचीना समृद्धा शास्त्रीया च भाषासु वर्तते'
-
-DEPENDENT_VOWELS = ["ा", "ि", "ी", "ु", "ू", "े", "ै", "ो", "ौ", "ं", "ः", "ृ", "ॄ"]
-
-
-def get_text(text, hps, cleaned=False):
-    if cleaned:
-        text_norm = text_to_sequence(text, hps.symbols, [])
-    else:
-        text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-def default_text(script):
-    if script=='Devanagari':
-        return DEFAULT_TEXT
-    else:
-        return sanscript.transliterate(DEFAULT_TEXT,sanscript.DEVANAGARI,SCRIPT_DICT[script])
-
-def speech_synthesize(text,script, speaker_id, length_scale):
-    text=text.replace('\n','')
-    text = g2p_pipeline(text)
-    print(f"G2P pipeline text is: {text}")
-    if script!='Devanagari':
-        text=sanscript.transliterate(text,SCRIPT_DICT[script],sanscript.DEVANAGARI)
-    print(text, type(text))
-    stn_tst = get_text(text, hps_ms)
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-        sid = torch.LongTensor([speaker_id])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-
-    sf.write(f'audio_outputs/{text[:4]}.mp3', audio, hps_ms.data.sampling_rate)
-    return (hps_ms.data.sampling_rate, audio)
-
-def speech_synthesize_smoothened(text,script, speaker_id, length_scale):
-    '''Synthesizes smoothened voice.'''
-    text = text.replace('\n', ' ')
-    text = voice_smoothening(text)
-    print(f"Smoothened text is: {text}")
-    if script!='Devanagari':
-        text=sanscript.transliterate(text,SCRIPT_DICT[script],sanscript.DEVANAGARI)
-    print(text, type(text))
-    stn_tst = get_text(text, hps_ms)
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-        sid = torch.LongTensor([speaker_id])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-
-    sf.write(f'audio_outputs/{text[:5]}.mp3', audio, hps_ms.data.sampling_rate)
-    return (hps_ms.data.sampling_rate, audio)
-
-
-
-if __name__=='__main__':
-    hps_ms = utils.get_hparams_from_file('model/config.json')
-    n_speakers = hps_ms.data.n_speakers
-    n_symbols = len(hps_ms.symbols)
-    speakers = hps_ms.speakers
-
-    net_g_ms = SynthesizerTrn(
-        n_symbols,
-        hps_ms.data.filter_length // 2 + 1,
-        hps_ms.train.segment_size // hps_ms.data.hop_length,
-        n_speakers=n_speakers,
-        **hps_ms.model)
-    
-    _ = net_g_ms.eval()
-    utils.load_checkpoint('model/model.pth', net_g_ms)
-
-    with gr.Blocks() as app:
-        gr.Markdown('# Sanskrit Text to Speech\n')
-        
-        with gr.Tabs():
-            # Tab for cleaning and printing input text.
-            with gr.TabItem('Text processing'):
-                with gr.Row():
-                    text_script=gr.Radio(['Devanagari'],label='Script', value='Devanagari')
-                    text_input = gr.TextArea(label='Text', value = DEFAULT_TEXT )
-                    text_output = gr.Textbox(label="Output", interactive = True)
-
-                text_button = gr.Button('Clean text')
-                text_button.click(pipeline, inputs = text_input, outputs = text_output)
-
-            with gr.TabItem('Grapheme to phoneme'):
-                    #Utilities for speech synthesis.
-                text_script=gr.Radio(['Devanagari'],label='Script',interactive=True,value='Devanagari')
-                text_input = gr.TextArea(label='Text', placeholder='संस्कृत')
-                speaker_id=gr.Dropdown(speakers,label='Speaker',type='index',interactive=True,value=speakers[0])
-                length_scale=gr.Slider(minimum = 1, maximum = 2, step=0.2, label='Speaking Speed',interactive=False)
-                
-                tts_button = gr.Button('Synthesize')
-                audio_output = gr.Audio(label='Speech Synthesized')
-            text_script.change(default_text,[text_script],[text_input])
-            tts_button.click(speech_synthesize,[text_input,text_script,speaker_id, length_scale],[audio_output])
-
-
-            with gr.TabItem('Text to speech'):
-                #Utilities for g2p sanskrit
-                    #Utilities for speech synthesis.
-                text_script=gr.Radio(['Devanagari'],label='Script',interactive=True,value='Devanagari')
-                text_input = gr.TextArea(label='Text', placeholder='संस्कृत')
-                speaker_id=gr.Dropdown(speakers,label='Speaker',type='index',interactive=True,value=speakers[0])
-                length_scale=gr.Slider(minimum = 1, maximum = 2, step=0.2, label='Speaking Speed',interactive=False)
-                tts_button = gr.Button('Synthesize')
-                audio_output = gr.Audio(label='Speech Synthesized')
-            text_script.change(voice_smoothening,[text_script],[text_input])
-            tts_button.click(speech_synthesize_smoothened,[text_input,text_script,speaker_id, length_scale],[audio_output])
-
-            
-
-    app.launch(share = False)
diff --git a/spaces/Lamai/LAMAIGPT/autogpt/commands/execute_code.py b/spaces/Lamai/LAMAIGPT/autogpt/commands/execute_code.py
deleted file mode 100644
index 11266f852727f2f8aedbc995b1e504a17acbfb77..0000000000000000000000000000000000000000
--- a/spaces/Lamai/LAMAIGPT/autogpt/commands/execute_code.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""Execute code in a Docker container"""
-import os
-import subprocess
-
-import docker
-from docker.errors import ImageNotFound
-
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-
-def execute_python_file(file: str) -> str:
-    """Execute a Python file in a Docker container and return the output
-
-    Args:
-        file (str): The name of the file to execute
-
-    Returns:
-        str: The output of the file
-    """
-
-    print(f"Executing file '{file}' in workspace '{WORKSPACE_PATH}'")
-
-    if not file.endswith(".py"):
-        return "Error: Invalid file type. Only .py files are allowed."
-
-    file_path = path_in_workspace(file)
-
-    if not os.path.isfile(file_path):
-        return f"Error: File '{file}' does not exist."
-
-    if we_are_running_in_a_docker_container():
-        result = subprocess.run(
-            f"python {file_path}", capture_output=True, encoding="utf8", shell=True
-        )
-        if result.returncode == 0:
-            return result.stdout
-        else:
-            return f"Error: {result.stderr}"
-
-    try:
-        client = docker.from_env()
-
-        # You can replace this with the desired Python image/version
-        # You can find available Python images on Docker Hub:
-        # https://hub.docker.com/_/python
-        image_name = "python:3-alpine"
-        try:
-            client.images.get(image_name)
-            print(f"Image '{image_name}' found locally")
-        except ImageNotFound:
-            print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
-            # Use the low-level API to stream the pull response
-            low_level_client = docker.APIClient()
-            for line in low_level_client.pull(image_name, stream=True, decode=True):
-                # Print the status and progress, if available
-                status = line.get("status")
-                progress = line.get("progress")
-                if status and progress:
-                    print(f"{status}: {progress}")
-                elif status:
-                    print(status)
-
-        container = client.containers.run(
-            image_name,
-            f"python {file}",
-            volumes={
-                os.path.abspath(WORKSPACE_PATH): {
-                    "bind": "/workspace",
-                    "mode": "ro",
-                }
-            },
-            working_dir="/workspace",
-            stderr=True,
-            stdout=True,
-            detach=True,
-        )
-
-        container.wait()
-        logs = container.logs().decode("utf-8")
-        container.remove()
-
-        # print(f"Execution complete. Output: {output}")
-        # print(f"Logs: {logs}")
-
-        return logs
-
-    except docker.errors.DockerException as e:
-        print(
-            "Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/"
-        )
-        return f"Error: {str(e)}"
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def execute_shell(command_line: str) -> str:
-    """Execute a shell command and return the output
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: The output of the command
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    result = subprocess.run(command_line, capture_output=True, shell=True)
-    output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return output
-
-
-def execute_shell_popen(command_line) -> str:
-    """Execute a shell command with Popen and returns an english description
-    of the event and the process id
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: Description of the fact that the process started and its id
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    do_not_show_output = subprocess.DEVNULL
-    process = subprocess.Popen(
-        command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output
-    )
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return f"Subprocess started with PID:'{str(process.pid)}'"
-
-
-def we_are_running_in_a_docker_container() -> bool:
-    """Check if we are running in a Docker container
-
-    Returns:
-        bool: True if we are running in a Docker container, False otherwise
-    """
-    return os.path.exists("/.dockerenv")
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/layers_33966KB.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/layers_33966KB.py
deleted file mode 100644
index a38b7bb3ae3136b07eadfc2db445fef4c2de186b..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/layers_33966KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/LearnableAI/FinTextSummaryDemo/app.py b/spaces/LearnableAI/FinTextSummaryDemo/app.py
deleted file mode 100644
index 934dd4b38452506938851c7d177c7b560ba49dc9..0000000000000000000000000000000000000000
--- a/spaces/LearnableAI/FinTextSummaryDemo/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import streamlit as st
-from model import GPT2LMHeadModel
-from transformers import BertTokenizer
-import argparse
-import os
-import torch
-import time
-from generate_title import predict_one_sample
-
-st.set_page_config(page_title="Demo", initial_sidebar_state="auto", layout="wide")
-
-
-# @st.cache_data(allow_output_mutation=True)
-def get_model(device, vocab_path, model_path):
-    tokenizer = BertTokenizer.from_pretrained(vocab_path, do_lower_case=True)
-    model = GPT2LMHeadModel.from_pretrained(model_path)
-    model.to(device)
-    model.eval()
-    return tokenizer, model
-
-
-device_ids = 0
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICE"] = str(device_ids)
-device = torch.device("cuda:1" if torch.cuda.is_available() and int(device_ids) >= 0 else "cpu")
-tokenizer, model = get_model(device, "vocab.txt", "checkpoint-55922")
-
-
-def writer():
-    st.markdown(
-        """
-        ## Text Summary DEMO
-        """
-    )
-    st.sidebar.subheader("配置参数")
-    batch_size = st.sidebar.slider("batch_size", min_value=0, max_value=10, value=3)
-    generate_max_len = st.sidebar.number_input("generate_max_len", min_value=0, max_value=64, value=32, step=1)
-    repetition_penalty = st.sidebar.number_input("repetition_penalty", min_value=0.0, max_value=10.0, value=1.2,
-                                                 step=0.1)
-    top_k = st.sidebar.slider("top_k", min_value=0, max_value=10, value=3, step=1)
-    top_p = st.sidebar.number_input("top_p", min_value=0.0, max_value=1.0, value=0.95, step=0.01)
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--batch_size', default=batch_size, type=int, help='生成标题的个数')
-    parser.add_argument('--generate_max_len', default=generate_max_len, type=int, help='生成标题的最大长度')
-    parser.add_argument('--repetition_penalty', default=repetition_penalty, type=float, help='重复处罚率')
-    parser.add_argument('--top_k', default=top_k, type=float, help='解码时保留概率最高的多少个标记')
-    parser.add_argument('--top_p', default=top_p, type=float, help='解码时保留概率累加大于多少的标记')
-    parser.add_argument('--max_len', type=int, default=512, help='输入模型的最大长度，要比config中n_ctx小')
-    args = parser.parse_args()
-
-    content = st.text_area("输入正文", value="近期美元指数大幅攀升，一度升至二十年高位，对此，市场人士认为，主要投资者对全球经济形势悲观预期升温，加上近日美联储决议鹰派基调，导致美元避险需求抬头。", max_chars=512)
-    if st.button("一键生成摘要"):
-        start_message = st.empty()
-        start_message.write("正在抽取，请等待...")
-        start_time = time.time()
-        titles = predict_one_sample(model, tokenizer, device, args, content)
-        end_time = time.time()
-        start_message.write("抽取完成，耗时{}s".format(end_time - start_time))
-        for i, title in enumerate(titles):
-            st.text_input("第{}个结果".format(i + 1), title)
-    else:
-        st.stop()
-
-
-if __name__ == '__main__':
-    writer()
diff --git a/spaces/Liberian/jtr8ukj8sk/Dockerfile b/spaces/Liberian/jtr8ukj8sk/Dockerfile
deleted file mode 100644
index 5e1b3443ea4b4de50e0fccf28b27ee9b2fa4b36c..0000000000000000000000000000000000000000
--- a/spaces/Liberian/jtr8ukj8sk/Dockerfile
+++ /dev/null
@@ -1,29 +0,0 @@
-# This is Lechcher's VPS on Hugging face or OpenXLab! (Lxde version)
-FROM debian:sid
-
-# This is user's part:
-RUN useradd -m -u 1000 user
-
-# Upgrade and update your system:
-RUN apt update
-RUN apt install software-properties-common apt-transport-https curl -y
-
-# This is desktop environment and dependents part:
-RUN apt install xfce4-terminal lxde aqemu sudo curl wget aria2 qemu-system-x86 htop chromium screen tigervnc-standalone-server python3-pip python3-websockify python3 git -y && rm -rf /var/lib/apt/lists/*
-
-# This is set default graphical target:
-RUN systemctl get-default
-RUN systemctl set-default graphical.target
-
-# Emable 32bit support:
-RUN dpkg --add-architecture i386
-
-# This is part that you can install your apps by: RUN apt install <repositories>:
-
-# This is VNC viewer part:
-RUN git clone https://github.com/novnc/noVNC.git noVNC
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-
-# This is the command to run VPS:
-CMD vncserver -SecurityTypes None -geometry 1280x600 && ./noVNC/utils/novnc_proxy --vnc localhost:5901 --listen 0.0.0.0:7860
diff --git a/spaces/LinkSoul/LLaSM/static/js/waveview.js b/spaces/LinkSoul/LLaSM/static/js/waveview.js
deleted file mode 100644
index f46f2d7ef676cce50bda9b016ae5c38a5083d57b..0000000000000000000000000000000000000000
--- a/spaces/LinkSoul/LLaSM/static/js/waveview.js
+++ /dev/null
@@ -1,6 +0,0 @@
-/*
-录音
-https://github.com/xiangyuecn/Recorder
-src: extensions/waveview.js
-*/
-!function(){var e=function(e){return new t(e)},g="WaveView",t=function(e){var t=this,a={scale:2,speed:9,phase:21.8,fps:20,keep:!0,lineWidth:3,linear1:[0,"rgba(150,96,238,1)",.2,"rgba(170,79,249,1)",1,"rgba(53,199,253,1)"],linear2:[0,"rgba(209,130,255,0.6)",1,"rgba(53,199,255,0.6)"],linearBg:[0,"rgba(255,255,255,0.2)",1,"rgba(54,197,252,0.2)"]};for(var i in e)a[i]=e[i];t.set=e=a;var r=e.elem;r&&("string"==typeof r?r=document.querySelector(r):r.length&&(r=r[0])),r&&(e.width=r.offsetWidth,e.height=r.offsetHeight);var n=e.scale,h=e.width*n,o=e.height*n;if(!h||!o)throw new Error(g+"无宽高");var l=t.elem=document.createElement("div"),s=["","transform-origin:0 0;","transform:scale("+1/n+");"];l.innerHTML='<div style="width:'+e.width+"px;height:"+e.height+'px;overflow:hidden"><div style="width:'+h+"px;height:"+o+"px;"+s.join("-webkit-")+s.join("-ms-")+s.join("-moz-")+s.join("")+'"><canvas/></div></div>';var c=t.canvas=l.querySelector("canvas"),d=t.ctx=c.getContext("2d");c.width=h,c.height=o,t.linear1=t.genLinear(d,h,e.linear1),t.linear2=t.genLinear(d,h,e.linear2),t.linearBg=t.genLinear(d,o,e.linearBg,!0),r&&(r.innerHTML="",r.appendChild(l)),t._phase=0};t.prototype=e.prototype={genLinear:function(e,t,a,i){for(var r=e.createLinearGradient(0,0,i?0:t,i?t:0),n=0;n<a.length;)r.addColorStop(a[n++],a[n++]);return r},genPath:function(e,t,a){for(var i=[],r=this.set,n=r.scale,h=r.width*n,o=r.height*n/2,l=0;l<h;l+=n){var s=(1+Math.cos(Math.PI+l/h*2*Math.PI))/2*o*t*Math.sin(2*Math.PI*(l/h)*e+a)+o;i.push(s)}return i},input:function(e,t,a){var i=this;i.sampleRate=a,i.pcmData=e,i.pcmPos=0,i.inputTime=Date.now(),i.schedule()},schedule:function(){var e=this,t=e.set,a=Math.floor(1e3/t.fps);e.timer||(e.timer=setInterval(function(){e.schedule()},a));var i=Date.now();if(!(i-(e.drawTime||0)<a)){e.drawTime=i;for(var r=e.sampleRate/t.fps,n=e.pcmData,h=e.pcmPos,o=Math.max(0,Math.min(r,n.length-h)),l=0,s=0;s<o;s++,h++)l+=Math.abs(n[h]);e.pcmPos=h,!o&&t.keep||e.draw(Recorder.PowerLevel(l,o)),!o&&1300<i-e.inputTime&&(clearInterval(e.timer),e.timer=0)}},draw:function(e){var t=this,a=t.set,i=t.ctx,r=a.scale,n=a.width*r,h=a.height*r,o=a.speed/a.fps,l=t._phase-=o,s=l+o*a.phase,c=e/100,d=t.genPath(2,c,l),g=t.genPath(1.8,c,s);i.clearRect(0,0,n,h),i.beginPath();for(var p=0,f=0;f<n;p++,f+=r)0==f?i.moveTo(f,d[p]):i.lineTo(f,d[p]);p--;for(f=n-1;0<=f;p--,f-=r)i.lineTo(f,g[p]);i.closePath(),i.fillStyle=t.linearBg,i.fill(),t.drawPath(g,t.linear2),t.drawPath(d,t.linear1)},drawPath:function(e,t){var a=this.set,i=this.ctx,r=a.scale,n=a.width*r;i.beginPath();for(var h=0,o=0;o<n;h++,o+=r)0==o?i.moveTo(o,e[h]):i.lineTo(o,e[h]);i.lineWidth=a.lineWidth*r,i.strokeStyle=t,i.stroke()}},Recorder[g]=e}();
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diff --git a/spaces/Liu-LAB/GPT-academic/core_functional.py b/spaces/Liu-LAB/GPT-academic/core_functional.py
deleted file mode 100644
index c4519ef8a73e3c01386a48ef3a26bf4560f3a2fc..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/core_functional.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# 'primary' 颜色对应 theme.py 中的 primary_hue
-# 'secondary' 颜色对应 theme.py 中的 neutral_hue
-# 'stop' 颜色对应 theme.py 中的 color_er
-import importlib
-from toolbox import clear_line_break
-
-
-def get_core_functions():
-    return {
-        "英语学术润色": {
-            # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
-            "Prefix":   r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " +
-                        r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " +
-                        r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
-            # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来
-            "Suffix":   r"",
-            # 按钮颜色 (默认 secondary)
-            "Color":    r"secondary",
-            # 按钮是否可见 (默认 True，即可见)
-            "Visible": True,
-            # 是否在触发时清除历史 (默认 False，即不处理之前的对话历史)
-            "AutoClearHistory": False
-        },
-        "中文学术润色": {
-            "Prefix":   r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
-                        r"同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本" + "\n\n",
-            "Suffix":   r"",
-        },
-        "查找语法错误": {
-            "Prefix":   r"Can you help me ensure that the grammar and the spelling is correct? " +
-                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good." +
-                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, " +
-                        r"put the original text the first column, " +
-                        r"put the corrected text in the second column and highlight the key words you fixed.""\n"
-                        r"Example:""\n"
-                        r"Paragraph: How is you? Do you knows what is it?""\n"
-                        r"| Original sentence | Corrected sentence |""\n"
-                        r"| :--- | :--- |""\n"
-                        r"| How **is** you? | How **are** you? |""\n"
-                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n"
-                        r"Below is a paragraph from an academic paper. "
-                        r"You need to report all grammar and spelling mistakes as the example before."
-                        + "\n\n",
-            "Suffix":   r"",
-            "PreProcess": clear_line_break,    # 预处理：清除换行符
-        },
-        "中译英": {
-            "Prefix":   r"Please translate following sentence to English:" + "\n\n",
-            "Suffix":   r"",
-        },
-        "学术中英互译": {
-            "Prefix":   r"I want you to act as a scientific English-Chinese translator, " +
-                        r"I will provide you with some paragraphs in one language " +
-                        r"and your task is to accurately and academically translate the paragraphs only into the other language. " +
-                        r"Do not repeat the original provided paragraphs after translation. " +
-                        r"You should use artificial intelligence tools, " +
-                        r"such as natural language processing, and rhetorical knowledge " +
-                        r"and experience about effective writing techniques to reply. " +
-                        r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n",
-            "Suffix": "",
-            "Color": "secondary",
-        },
-        "英译中": {
-            "Prefix":   r"翻译成地道的中文：" + "\n\n",
-            "Suffix":   r"",
-            "Visible": False,
-        },
-        "找图片": {
-            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，" +
-                        r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
-            "Suffix":   r"",
-            "Visible": False,
-        },
-        "解释代码": {
-            "Prefix":   r"请解释以下代码：" + "\n```\n",
-            "Suffix":   "\n```\n",
-        },
-        "参考文献转Bib": {
-            "Prefix":   r"Here are some bibliography items, please transform them into bibtex style." +
-                        r"Note that, reference styles maybe more than one kind, you should transform each item correctly." +
-                        r"Items need to be transformed:",
-            "Visible": False,
-            "Suffix":   r"",
-        }
-    }
-
-
-def handle_core_functionality(additional_fn, inputs, history, chatbot):
-    import core_functional
-    importlib.reload(core_functional)    # 热更新prompt
-    core_functional = core_functional.get_core_functions()
-    if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-    inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-    if core_functional[additional_fn].get("AutoClearHistory", False):
-        history = []
-    return inputs, history
diff --git a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs4/demucs.py b/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs4/demucs.py
deleted file mode 100644
index 967c8337e7be45cd22d07bba56b93f36469f99fa..0000000000000000000000000000000000000000
--- a/spaces/Ma5onic/MVSEP-MDX23-music-separation-model/demucs4/demucs.py
+++ /dev/null
@@ -1,447 +0,0 @@
-# Copyright (c) Meta, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import julius
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .states import capture_init
-from .utils import center_trim, unfold
-from .transformer import LayerScale
-
-
-class BLSTM(nn.Module):
-    """
-    BiLSTM with same hidden units as input dim.
-    If `max_steps` is not None, input will be splitting in overlapping
-    chunks and the LSTM applied separately on each chunk.
-    """
-    def __init__(self, dim, layers=1, max_steps=None, skip=False):
-        super().__init__()
-        assert max_steps is None or max_steps % 4 == 0
-        self.max_steps = max_steps
-        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
-        self.linear = nn.Linear(2 * dim, dim)
-        self.skip = skip
-
-    def forward(self, x):
-        B, C, T = x.shape
-        y = x
-        framed = False
-        if self.max_steps is not None and T > self.max_steps:
-            width = self.max_steps
-            stride = width // 2
-            frames = unfold(x, width, stride)
-            nframes = frames.shape[2]
-            framed = True
-            x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)
-
-        x = x.permute(2, 0, 1)
-
-        x = self.lstm(x)[0]
-        x = self.linear(x)
-        x = x.permute(1, 2, 0)
-        if framed:
-            out = []
-            frames = x.reshape(B, -1, C, width)
-            limit = stride // 2
-            for k in range(nframes):
-                if k == 0:
-                    out.append(frames[:, k, :, :-limit])
-                elif k == nframes - 1:
-                    out.append(frames[:, k, :, limit:])
-                else:
-                    out.append(frames[:, k, :, limit:-limit])
-            out = torch.cat(out, -1)
-            out = out[..., :T]
-            x = out
-        if self.skip:
-            x = x + y
-        return x
-
-
-def rescale_conv(conv, reference):
-    """Rescale initial weight scale. It is unclear why it helps but it certainly does.
-    """
-    std = conv.weight.std().detach()
-    scale = (std / reference)**0.5
-    conv.weight.data /= scale
-    if conv.bias is not None:
-        conv.bias.data /= scale
-
-
-def rescale_module(module, reference):
-    for sub in module.modules():
-        if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)):
-            rescale_conv(sub, reference)
-
-
-class DConv(nn.Module):
-    """
-    New residual branches in each encoder layer.
-    This alternates dilated convolutions, potentially with LSTMs and attention.
-    Also before entering each residual branch, dimension is projected on a smaller subspace,
-    e.g. of dim `channels // compress`.
-    """
-    def __init__(self, channels: int, compress: float = 4, depth: int = 2, init: float = 1e-4,
-                 norm=True, attn=False, heads=4, ndecay=4, lstm=False, gelu=True,
-                 kernel=3, dilate=True):
-        """
-        Args:
-            channels: input/output channels for residual branch.
-            compress: amount of channel compression inside the branch.
-            depth: number of layers in the residual branch. Each layer has its own
-                projection, and potentially LSTM and attention.
-            init: initial scale for LayerNorm.
-            norm: use GroupNorm.
-            attn: use LocalAttention.
-            heads: number of heads for the LocalAttention.
-            ndecay: number of decay controls in the LocalAttention.
-            lstm: use LSTM.
-            gelu: Use GELU activation.
-            kernel: kernel size for the (dilated) convolutions.
-            dilate: if true, use dilation, increasing with the depth.
-        """
-
-        super().__init__()
-        assert kernel % 2 == 1
-        self.channels = channels
-        self.compress = compress
-        self.depth = abs(depth)
-        dilate = depth > 0
-
-        norm_fn: tp.Callable[[int], nn.Module]
-        norm_fn = lambda d: nn.Identity()  # noqa
-        if norm:
-            norm_fn = lambda d: nn.GroupNorm(1, d)  # noqa
-
-        hidden = int(channels / compress)
-
-        act: tp.Type[nn.Module]
-        if gelu:
-            act = nn.GELU
-        else:
-            act = nn.ReLU
-
-        self.layers = nn.ModuleList([])
-        for d in range(self.depth):
-            dilation = 2 ** d if dilate else 1
-            padding = dilation * (kernel // 2)
-            mods = [
-                nn.Conv1d(channels, hidden, kernel, dilation=dilation, padding=padding),
-                norm_fn(hidden), act(),
-                nn.Conv1d(hidden, 2 * channels, 1),
-                norm_fn(2 * channels), nn.GLU(1),
-                LayerScale(channels, init),
-            ]
-            if attn:
-                mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))
-            if lstm:
-                mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))
-            layer = nn.Sequential(*mods)
-            self.layers.append(layer)
-
-    def forward(self, x):
-        for layer in self.layers:
-            x = x + layer(x)
-        return x
-
-
-class LocalState(nn.Module):
-    """Local state allows to have attention based only on data (no positional embedding),
-    but while setting a constraint on the time window (e.g. decaying penalty term).
-
-    Also a failed experiments with trying to provide some frequency based attention.
-    """
-    def __init__(self, channels: int, heads: int = 4, nfreqs: int = 0, ndecay: int = 4):
-        super().__init__()
-        assert channels % heads == 0, (channels, heads)
-        self.heads = heads
-        self.nfreqs = nfreqs
-        self.ndecay = ndecay
-        self.content = nn.Conv1d(channels, channels, 1)
-        self.query = nn.Conv1d(channels, channels, 1)
-        self.key = nn.Conv1d(channels, channels, 1)
-        if nfreqs:
-            self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)
-        if ndecay:
-            self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
-            # Initialize decay close to zero (there is a sigmoid), for maximum initial window.
-            self.query_decay.weight.data *= 0.01
-            assert self.query_decay.bias is not None  # stupid type checker
-            self.query_decay.bias.data[:] = -2
-        self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)
-
-    def forward(self, x):
-        B, C, T = x.shape
-        heads = self.heads
-        indexes = torch.arange(T, device=x.device, dtype=x.dtype)
-        # left index are keys, right index are queries
-        delta = indexes[:, None] - indexes[None, :]
-
-        queries = self.query(x).view(B, heads, -1, T)
-        keys = self.key(x).view(B, heads, -1, T)
-        # t are keys, s are queries
-        dots = torch.einsum("bhct,bhcs->bhts", keys, queries)
-        dots /= keys.shape[2]**0.5
-        if self.nfreqs:
-            periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)
-            freq_kernel = torch.cos(2 * math.pi * delta / periods.view(-1, 1, 1))
-            freq_q = self.query_freqs(x).view(B, heads, -1, T) / self.nfreqs ** 0.5
-            dots += torch.einsum("fts,bhfs->bhts", freq_kernel, freq_q)
-        if self.ndecay:
-            decays = torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype)
-            decay_q = self.query_decay(x).view(B, heads, -1, T)
-            decay_q = torch.sigmoid(decay_q) / 2
-            decay_kernel = - decays.view(-1, 1, 1) * delta.abs() / self.ndecay**0.5
-            dots += torch.einsum("fts,bhfs->bhts", decay_kernel, decay_q)
-
-        # Kill self reference.
-        dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)
-        weights = torch.softmax(dots, dim=2)
-
-        content = self.content(x).view(B, heads, -1, T)
-        result = torch.einsum("bhts,bhct->bhcs", weights, content)
-        if self.nfreqs:
-            time_sig = torch.einsum("bhts,fts->bhfs", weights, freq_kernel)
-            result = torch.cat([result, time_sig], 2)
-        result = result.reshape(B, -1, T)
-        return x + self.proj(result)
-
-
-class Demucs(nn.Module):
-    @capture_init
-    def __init__(self,
-                 sources,
-                 # Channels
-                 audio_channels=2,
-                 channels=64,
-                 growth=2.,
-                 # Main structure
-                 depth=6,
-                 rewrite=True,
-                 lstm_layers=0,
-                 # Convolutions
-                 kernel_size=8,
-                 stride=4,
-                 context=1,
-                 # Activations
-                 gelu=True,
-                 glu=True,
-                 # Normalization
-                 norm_starts=4,
-                 norm_groups=4,
-                 # DConv residual branch
-                 dconv_mode=1,
-                 dconv_depth=2,
-                 dconv_comp=4,
-                 dconv_attn=4,
-                 dconv_lstm=4,
-                 dconv_init=1e-4,
-                 # Pre/post processing
-                 normalize=True,
-                 resample=True,
-                 # Weight init
-                 rescale=0.1,
-                 # Metadata
-                 samplerate=44100,
-                 segment=4 * 10):
-        """
-        Args:
-            sources (list[str]): list of source names
-            audio_channels (int): stereo or mono
-            channels (int): first convolution channels
-            depth (int): number of encoder/decoder layers
-            growth (float): multiply (resp divide) number of channels by that
-                for each layer of the encoder (resp decoder)
-            depth (int): number of layers in the encoder and in the decoder.
-            rewrite (bool): add 1x1 convolution to each layer.
-            lstm_layers (int): number of lstm layers, 0 = no lstm. Deactivated
-                by default, as this is now replaced by the smaller and faster small LSTMs
-                in the DConv branches.
-            kernel_size (int): kernel size for convolutions
-            stride (int): stride for convolutions
-            context (int): kernel size of the convolution in the
-                decoder before the transposed convolution. If > 1,
-                will provide some context from neighboring time steps.
-            gelu: use GELU activation function.
-            glu (bool): use glu instead of ReLU for the 1x1 rewrite conv.
-            norm_starts: layer at which group norm starts being used.
-                decoder layers are numbered in reverse order.
-            norm_groups: number of groups for group norm.
-            dconv_mode: if 1: dconv in encoder only, 2: decoder only, 3: both.
-            dconv_depth: depth of residual DConv branch.
-            dconv_comp: compression of DConv branch.
-            dconv_attn: adds attention layers in DConv branch starting at this layer.
-            dconv_lstm: adds a LSTM layer in DConv branch starting at this layer.
-            dconv_init: initial scale for the DConv branch LayerScale.
-            normalize (bool): normalizes the input audio on the fly, and scales back
-                the output by the same amount.
-            resample (bool): upsample x2 the input and downsample /2 the output.
-            rescale (int): rescale initial weights of convolutions
-                to get their standard deviation closer to `rescale`.
-            samplerate (int): stored as meta information for easing
-                future evaluations of the model.
-            segment (float): duration of the chunks of audio to ideally evaluate the model on.
-                This is used by `demucs.apply.apply_model`.
-        """
-
-        super().__init__()
-        self.audio_channels = audio_channels
-        self.sources = sources
-        self.kernel_size = kernel_size
-        self.context = context
-        self.stride = stride
-        self.depth = depth
-        self.resample = resample
-        self.channels = channels
-        self.normalize = normalize
-        self.samplerate = samplerate
-        self.segment = segment
-        self.encoder = nn.ModuleList()
-        self.decoder = nn.ModuleList()
-        self.skip_scales = nn.ModuleList()
-
-        if glu:
-            activation = nn.GLU(dim=1)
-            ch_scale = 2
-        else:
-            activation = nn.ReLU()
-            ch_scale = 1
-        if gelu:
-            act2 = nn.GELU
-        else:
-            act2 = nn.ReLU
-
-        in_channels = audio_channels
-        padding = 0
-        for index in range(depth):
-            norm_fn = lambda d: nn.Identity()  # noqa
-            if index >= norm_starts:
-                norm_fn = lambda d: nn.GroupNorm(norm_groups, d)  # noqa
-
-            encode = []
-            encode += [
-                nn.Conv1d(in_channels, channels, kernel_size, stride),
-                norm_fn(channels),
-                act2(),
-            ]
-            attn = index >= dconv_attn
-            lstm = index >= dconv_lstm
-            if dconv_mode & 1:
-                encode += [DConv(channels, depth=dconv_depth, init=dconv_init,
-                                 compress=dconv_comp, attn=attn, lstm=lstm)]
-            if rewrite:
-                encode += [
-                    nn.Conv1d(channels, ch_scale * channels, 1),
-                    norm_fn(ch_scale * channels), activation]
-            self.encoder.append(nn.Sequential(*encode))
-
-            decode = []
-            if index > 0:
-                out_channels = in_channels
-            else:
-                out_channels = len(self.sources) * audio_channels
-            if rewrite:
-                decode += [
-                    nn.Conv1d(channels, ch_scale * channels, 2 * context + 1, padding=context),
-                    norm_fn(ch_scale * channels), activation]
-            if dconv_mode & 2:
-                decode += [DConv(channels, depth=dconv_depth, init=dconv_init,
-                                 compress=dconv_comp, attn=attn, lstm=lstm)]
-            decode += [nn.ConvTranspose1d(channels, out_channels,
-                       kernel_size, stride, padding=padding)]
-            if index > 0:
-                decode += [norm_fn(out_channels), act2()]
-            self.decoder.insert(0, nn.Sequential(*decode))
-            in_channels = channels
-            channels = int(growth * channels)
-
-        channels = in_channels
-        if lstm_layers:
-            self.lstm = BLSTM(channels, lstm_layers)
-        else:
-            self.lstm = None
-
-        if rescale:
-            rescale_module(self, reference=rescale)
-
-    def valid_length(self, length):
-        """
-        Return the nearest valid length to use with the model so that
-        there is no time steps left over in a convolution, e.g. for all
-        layers, size of the input - kernel_size % stride = 0.
-
-        Note that input are automatically padded if necessary to ensure that the output
-        has the same length as the input.
-        """
-        if self.resample:
-            length *= 2
-
-        for _ in range(self.depth):
-            length = math.ceil((length - self.kernel_size) / self.stride) + 1
-            length = max(1, length)
-
-        for idx in range(self.depth):
-            length = (length - 1) * self.stride + self.kernel_size
-
-        if self.resample:
-            length = math.ceil(length / 2)
-        return int(length)
-
-    def forward(self, mix):
-        x = mix
-        length = x.shape[-1]
-
-        if self.normalize:
-            mono = mix.mean(dim=1, keepdim=True)
-            mean = mono.mean(dim=-1, keepdim=True)
-            std = mono.std(dim=-1, keepdim=True)
-            x = (x - mean) / (1e-5 + std)
-        else:
-            mean = 0
-            std = 1
-
-        delta = self.valid_length(length) - length
-        x = F.pad(x, (delta // 2, delta - delta // 2))
-
-        if self.resample:
-            x = julius.resample_frac(x, 1, 2)
-
-        saved = []
-        for encode in self.encoder:
-            x = encode(x)
-            saved.append(x)
-
-        if self.lstm:
-            x = self.lstm(x)
-
-        for decode in self.decoder:
-            skip = saved.pop(-1)
-            skip = center_trim(skip, x)
-            x = decode(x + skip)
-
-        if self.resample:
-            x = julius.resample_frac(x, 2, 1)
-        x = x * std + mean
-        x = center_trim(x, length)
-        x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1))
-        return x
-
-    def load_state_dict(self, state, strict=True):
-        # fix a mismatch with previous generation Demucs models.
-        for idx in range(self.depth):
-            for a in ['encoder', 'decoder']:
-                for b in ['bias', 'weight']:
-                    new = f'{a}.{idx}.3.{b}'
-                    old = f'{a}.{idx}.2.{b}'
-                    if old in state and new not in state:
-                        state[new] = state.pop(old)
-        super().load_state_dict(state, strict=strict)
diff --git a/spaces/Manjushri/MusicGen/audiocraft/utils/export.py b/spaces/Manjushri/MusicGen/audiocraft/utils/export.py
deleted file mode 100644
index b513b52267f7bf5aae09282c15b0a2e20c8a8fee..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/audiocraft/utils/export.py
+++ /dev/null
@@ -1,56 +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.
-
-"""
-Utility to export a training checkpoint to a lightweight release checkpoint.
-"""
-
-from pathlib import Path
-import typing as tp
-
-from omegaconf import OmegaConf, DictConfig
-import torch
-
-
-def _clean_lm_cfg(cfg: DictConfig):
-    OmegaConf.set_struct(cfg, False)
-    # This used to be set automatically in the LM solver, need a more robust solution
-    # for the future.
-    cfg['transformer_lm']['card'] = 2048
-    cfg['transformer_lm']['n_q'] = 4
-    # Experimental params no longer supported.
-    bad_params = ['spectral_norm_attn_iters', 'spectral_norm_ff_iters',
-                  'residual_balancer_attn', 'residual_balancer_ff', 'layer_drop']
-    for name in bad_params:
-        del cfg['transformer_lm'][name]
-    OmegaConf.set_struct(cfg, True)
-    return cfg
-
-
-def export_encodec(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['ema']['state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']),
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file
-
-
-def export_lm(checkpoint_path: tp.Union[Path, str], out_folder: tp.Union[Path, str]):
-    sig = Path(checkpoint_path).parent.name
-    assert len(sig) == 8, "Not a valid Dora signature"
-    pkg = torch.load(checkpoint_path, 'cpu')
-    new_pkg = {
-        'best_state': pkg['fsdp_best_state']['model'],
-        'xp.cfg': OmegaConf.to_yaml(_clean_lm_cfg(pkg['xp.cfg']))
-    }
-    out_file = Path(out_folder) / f'{sig}.th'
-    torch.save(new_pkg, out_file)
-    return out_file
diff --git a/spaces/Mattdoc99/CollisonGPTChat/app.py b/spaces/Mattdoc99/CollisonGPTChat/app.py
deleted file mode 100644
index f9a8a91fa2f7847c80a40f224ab346818a25bec2..0000000000000000000000000000000000000000
--- a/spaces/Mattdoc99/CollisonGPTChat/app.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from langchain.chains import llm 
-from ast import Index
-from gpt_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
-from langchain import OpenAI
-import openai
-import os 
-import streamlit as st
-
-OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
-os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
-
-def ask_patrick():
-    # Get the user's query
-    query = st.text_input("Tell me about Patrick Collison?")
-    
-    # Use OpenAI to get a response to the query
-    response = openai.Completion.create(
-        engine="text-davinci-002",
-        prompt=f"Patrick Collison is a tech entrepreneur. {query}",
-        temperature=0.5,
-        max_tokens=1024,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0
-    )
-
-    # Print the response
-    st.write(response.choices[0].text)
-
-if __name__ == "__main__":
-    ask_patrick()
\ No newline at end of file
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/speech/brian.py b/spaces/MetaWabbit/Auto-GPT/autogpt/speech/brian.py
deleted file mode 100644
index 821fdf2f482a9cfa928e5c9680152ad6766d8326..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/speech/brian.py
+++ /dev/null
@@ -1,40 +0,0 @@
-""" Brian speech module for autogpt """
-import os
-
-import requests
-from playsound import playsound
-
-from autogpt.speech.base import VoiceBase
-
-
-class BrianSpeech(VoiceBase):
-    """Brian speech module for autogpt"""
-
-    def _setup(self) -> None:
-        """Setup the voices, API key, etc."""
-        pass
-
-    def _speech(self, text: str, _: int = 0) -> bool:
-        """Speak text using Brian with the streamelements API
-
-        Args:
-            text (str): The text to speak
-
-        Returns:
-            bool: True if the request was successful, False otherwise
-        """
-        tts_url = (
-            f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}"
-        )
-        response = requests.get(tts_url)
-
-        if response.status_code == 200:
-            with open("speech.mp3", "wb") as f:
-                f.write(response.content)
-            playsound("speech.mp3")
-            os.remove("speech.mp3")
-            return True
-        else:
-            print("Request failed with status code:", response.status_code)
-            print("Response content:", response.content)
-            return False
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/nrtr_decoder.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/nrtr_decoder.py
deleted file mode 100644
index dc986c48807e696b2001d3d91ae33a0312ae9044..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textrecog/decoders/nrtr_decoder.py
+++ /dev/null
@@ -1,257 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-from typing import Dict, List, Optional, Sequence, Union
-
-import torch
-import torch.nn as nn
-from mmengine.model import ModuleList
-
-from mmocr.models.common import PositionalEncoding, TFDecoderLayer
-from mmocr.models.common.dictionary import Dictionary
-from mmocr.registry import MODELS
-from mmocr.structures import TextRecogDataSample
-from .base import BaseDecoder
-
-
-@MODELS.register_module()
-class NRTRDecoder(BaseDecoder):
-    """Transformer Decoder block with self attention mechanism.
-
-    Args:
-        n_layers (int): Number of attention layers. Defaults to 6.
-        d_embedding (int): Language embedding dimension. Defaults to 512.
-        n_head (int): Number of parallel attention heads. Defaults to 8.
-        d_k (int): Dimension of the key vector. Defaults to 64.
-        d_v (int): Dimension of the value vector. Defaults to 64
-        d_model (int): Dimension :math:`D_m` of the input from previous model.
-            Defaults to 512.
-        d_inner (int): Hidden dimension of feedforward layers. Defaults to 256.
-        n_position (int): Length of the positional encoding vector. Must be
-            greater than ``max_seq_len``. Defaults to 200.
-        dropout (float): Dropout rate for text embedding, MHSA, FFN. Defaults
-            to 0.1.
-        module_loss (dict, optional): Config to build module_loss. Defaults
-            to None.
-        postprocessor (dict, optional): Config to build postprocessor.
-            Defaults to None.
-        dictionary (dict or :obj:`Dictionary`): The config for `Dictionary` or
-            the instance of `Dictionary`.
-        max_seq_len (int): Maximum output sequence length :math:`T`. Defaults
-            to 30.
-        init_cfg (dict or list[dict], optional): Initialization configs.
-    """
-
-    def __init__(self,
-                 n_layers: int = 6,
-                 d_embedding: int = 512,
-                 n_head: int = 8,
-                 d_k: int = 64,
-                 d_v: int = 64,
-                 d_model: int = 512,
-                 d_inner: int = 256,
-                 n_position: int = 200,
-                 dropout: float = 0.1,
-                 module_loss: Optional[Dict] = None,
-                 postprocessor: Optional[Dict] = None,
-                 dictionary: Optional[Union[Dict, Dictionary]] = None,
-                 max_seq_len: int = 30,
-                 init_cfg: Optional[Union[Dict, List[Dict]]] = None) -> None:
-        super().__init__(
-            module_loss=module_loss,
-            postprocessor=postprocessor,
-            dictionary=dictionary,
-            init_cfg=init_cfg,
-            max_seq_len=max_seq_len)
-
-        self.padding_idx = self.dictionary.padding_idx
-        self.start_idx = self.dictionary.start_idx
-        self.max_seq_len = max_seq_len
-
-        self.trg_word_emb = nn.Embedding(
-            self.dictionary.num_classes,
-            d_embedding,
-            padding_idx=self.padding_idx)
-
-        self.position_enc = PositionalEncoding(
-            d_embedding, n_position=n_position)
-        self.dropout = nn.Dropout(p=dropout)
-
-        self.layer_stack = ModuleList([
-            TFDecoderLayer(
-                d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
-            for _ in range(n_layers)
-        ])
-        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
-
-        pred_num_class = self.dictionary.num_classes
-        self.classifier = nn.Linear(d_model, pred_num_class)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def _get_target_mask(self, trg_seq: torch.Tensor) -> torch.Tensor:
-        """Generate mask for target sequence.
-
-        Args:
-            trg_seq (torch.Tensor): Input text sequence. Shape :math:`(N, T)`.
-
-        Returns:
-            Tensor: Target mask. Shape :math:`(N, T, T)`.
-            E.g.:
-            seq = torch.Tensor([[1, 2, 0, 0]]), pad_idx = 0, then
-            target_mask =
-            torch.Tensor([[[True, False, False, False],
-            [True, True, False, False],
-            [True, True, False, False],
-            [True, True, False, False]]])
-        """
-
-        pad_mask = (trg_seq != self.padding_idx).unsqueeze(-2)
-
-        len_s = trg_seq.size(1)
-        subsequent_mask = 1 - torch.triu(
-            torch.ones((len_s, len_s), device=trg_seq.device), diagonal=1)
-        subsequent_mask = subsequent_mask.unsqueeze(0).bool()
-
-        return pad_mask & subsequent_mask
-
-    def _get_source_mask(self, src_seq: torch.Tensor,
-                         valid_ratios: Sequence[float]) -> torch.Tensor:
-        """Generate mask for source sequence.
-
-        Args:
-            src_seq (torch.Tensor): Image sequence. Shape :math:`(N, T, C)`.
-            valid_ratios (list[float]): The valid ratio of input image. For
-                example, if the width of the original image is w1 and the width
-                after padding is w2, then valid_ratio = w1/w2. Source mask is
-                used to cover the area of the padding region.
-
-        Returns:
-            Tensor or None: Source mask. Shape :math:`(N, T)`. The region of
-            padding area are False, and the rest are True.
-        """
-
-        N, T, _ = src_seq.size()
-        mask = None
-        if len(valid_ratios) > 0:
-            mask = src_seq.new_zeros((N, T), device=src_seq.device)
-            for i, valid_ratio in enumerate(valid_ratios):
-                valid_width = min(T, math.ceil(T * valid_ratio))
-                mask[i, :valid_width] = 1
-
-        return mask
-
-    def _attention(self,
-                   trg_seq: torch.Tensor,
-                   src: torch.Tensor,
-                   src_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
-        """A wrapped process for transformer based decoder including text
-        embedding, position embedding, N x transformer decoder and a LayerNorm
-        operation.
-
-        Args:
-            trg_seq (Tensor): Target sequence in. Shape :math:`(N, T)`.
-            src (Tensor): Source sequence from encoder in shape
-                Shape :math:`(N, T, D_m)` where :math:`D_m` is ``d_model``.
-            src_mask (Tensor, Optional): Mask for source sequence.
-                Shape :math:`(N, T)`. Defaults to None.
-
-        Returns:
-            Tensor: Output sequence from transformer decoder.
-            Shape :math:`(N, T, D_m)` where :math:`D_m` is ``d_model``.
-        """
-
-        trg_embedding = self.trg_word_emb(trg_seq)
-        trg_pos_encoded = self.position_enc(trg_embedding)
-        trg_mask = self._get_target_mask(trg_seq)
-        tgt_seq = self.dropout(trg_pos_encoded)
-
-        output = tgt_seq
-        for dec_layer in self.layer_stack:
-            output = dec_layer(
-                output,
-                src,
-                self_attn_mask=trg_mask,
-                dec_enc_attn_mask=src_mask)
-        output = self.layer_norm(output)
-
-        return output
-
-    def forward_train(self,
-                      feat: Optional[torch.Tensor] = None,
-                      out_enc: torch.Tensor = None,
-                      data_samples: Sequence[TextRecogDataSample] = None
-                      ) -> torch.Tensor:
-        """Forward for training. Source mask will be used here.
-
-        Args:
-            feat (Tensor, optional): Unused.
-            out_enc (Tensor): Encoder output of shape : math:`(N, T, D_m)`
-                where :math:`D_m` is ``d_model``. Defaults to None.
-            data_samples (list[TextRecogDataSample]): Batch of
-                TextRecogDataSample, containing gt_text and valid_ratio
-                information. Defaults to None.
-
-        Returns:
-            Tensor: The raw logit tensor. Shape :math:`(N, T, C)` where
-            :math:`C` is ``num_classes``.
-        """
-        valid_ratios = []
-        for data_sample in data_samples:
-            valid_ratios.append(data_sample.get('valid_ratio'))
-        src_mask = self._get_source_mask(out_enc, valid_ratios)
-        trg_seq = []
-        for data_sample in data_samples:
-            trg_seq.append(
-                data_sample.gt_text.padded_indexes.to(out_enc.device))
-        trg_seq = torch.stack(trg_seq, dim=0)
-        attn_output = self._attention(trg_seq, out_enc, src_mask=src_mask)
-        outputs = self.classifier(attn_output)
-
-        return outputs
-
-    def forward_test(self,
-                     feat: Optional[torch.Tensor] = None,
-                     out_enc: torch.Tensor = None,
-                     data_samples: Sequence[TextRecogDataSample] = None
-                     ) -> torch.Tensor:
-        """Forward for testing.
-
-        Args:
-            feat (Tensor, optional): Unused.
-            out_enc (Tensor): Encoder output of shape:
-                math:`(N, T, D_m)` where :math:`D_m` is ``d_model``.
-                Defaults to None.
-            data_samples (list[TextRecogDataSample]): Batch of
-                TextRecogDataSample, containing gt_text and valid_ratio
-                information. Defaults to None.
-
-        Returns:
-            Tensor: Character probabilities. of shape
-            :math:`(N, self.max_seq_len, C)` where :math:`C` is
-            ``num_classes``.
-        """
-        valid_ratios = []
-        for data_sample in data_samples:
-            valid_ratios.append(data_sample.get('valid_ratio'))
-        src_mask = self._get_source_mask(out_enc, valid_ratios)
-        N = out_enc.size(0)
-        init_target_seq = torch.full((N, self.max_seq_len + 1),
-                                     self.padding_idx,
-                                     device=out_enc.device,
-                                     dtype=torch.long)
-        # bsz * seq_len
-        init_target_seq[:, 0] = self.start_idx
-
-        outputs = []
-        for step in range(0, self.max_seq_len):
-            decoder_output = self._attention(
-                init_target_seq, out_enc, src_mask=src_mask)
-            # bsz * seq_len * C
-            step_result = self.classifier(decoder_output[:, step, :])
-            # bsz * num_classes
-            outputs.append(step_result)
-            _, step_max_index = torch.max(step_result, dim=-1)
-            init_target_seq[:, step + 1] = step_max_index
-
-        outputs = torch.stack(outputs, dim=1)
-
-        return self.softmax(outputs)
diff --git a/spaces/NATSpeech/PortaSpeech/tasks/vocoder/hifigan.py b/spaces/NATSpeech/PortaSpeech/tasks/vocoder/hifigan.py
deleted file mode 100644
index a07370ab84f2d5ba6b20cc37db9773c1c2879b73..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/tasks/vocoder/hifigan.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import torch.nn.functional as F
-from torch import nn
-
-from modules.vocoder.hifigan.hifigan import HifiGanGenerator, MultiPeriodDiscriminator, MultiScaleDiscriminator, \
-    generator_loss, feature_loss, discriminator_loss
-from modules.vocoder.hifigan.mel_utils import mel_spectrogram
-from modules.vocoder.hifigan.stft_loss import MultiResolutionSTFTLoss
-from tasks.vocoder.vocoder_base import VocoderBaseTask
-from utils.commons.hparams import hparams
-from utils.nn.model_utils import print_arch
-
-
-class HifiGanTask(VocoderBaseTask):
-    def build_model(self):
-        self.model_gen = HifiGanGenerator(hparams)
-        self.model_disc = nn.ModuleDict()
-        self.model_disc['mpd'] = MultiPeriodDiscriminator()
-        self.model_disc['msd'] = MultiScaleDiscriminator()
-        self.stft_loss = MultiResolutionSTFTLoss()
-        print_arch(self.model_gen)
-        if hparams['load_ckpt'] != '':
-            self.load_ckpt(hparams['load_ckpt'], 'model_gen', 'model_gen', force=True, strict=True)
-            self.load_ckpt(hparams['load_ckpt'], 'model_disc', 'model_disc', force=True, strict=True)
-        return self.model_gen
-
-    def _training_step(self, sample, batch_idx, optimizer_idx):
-        mel = sample['mels']
-        y = sample['wavs']
-        f0 = sample['f0']
-        loss_output = {}
-        if optimizer_idx == 0:
-            #######################
-            #      Generator      #
-            #######################
-            y_ = self.model_gen(mel, f0)
-            y_mel = mel_spectrogram(y.squeeze(1), hparams).transpose(1, 2)
-            y_hat_mel = mel_spectrogram(y_.squeeze(1), hparams).transpose(1, 2)
-            loss_output['mel'] = F.l1_loss(y_hat_mel, y_mel) * hparams['lambda_mel']
-            _, y_p_hat_g, fmap_f_r, fmap_f_g = self.model_disc['mpd'](y, y_, mel)
-            _, y_s_hat_g, fmap_s_r, fmap_s_g = self.model_disc['msd'](y, y_, mel)
-            loss_output['a_p'] = generator_loss(y_p_hat_g) * hparams['lambda_adv']
-            loss_output['a_s'] = generator_loss(y_s_hat_g) * hparams['lambda_adv']
-            if hparams['use_fm_loss']:
-                loss_output['fm_f'] = feature_loss(fmap_f_r, fmap_f_g)
-                loss_output['fm_s'] = feature_loss(fmap_s_r, fmap_s_g)
-            if hparams['use_ms_stft']:
-                loss_output['sc'], loss_output['mag'] = self.stft_loss(y.squeeze(1), y_.squeeze(1))
-            self.y_ = y_.detach()
-            self.y_mel = y_mel.detach()
-            self.y_hat_mel = y_hat_mel.detach()
-        else:
-            #######################
-            #    Discriminator    #
-            #######################
-            y_ = self.y_
-            # MPD
-            y_p_hat_r, y_p_hat_g, _, _ = self.model_disc['mpd'](y, y_.detach(), mel)
-            loss_output['r_p'], loss_output['f_p'] = discriminator_loss(y_p_hat_r, y_p_hat_g)
-            # MSD
-            y_s_hat_r, y_s_hat_g, _, _ = self.model_disc['msd'](y, y_.detach(), mel)
-            loss_output['r_s'], loss_output['f_s'] = discriminator_loss(y_s_hat_r, y_s_hat_g)
-        total_loss = sum(loss_output.values())
-        return total_loss, loss_output
diff --git a/spaces/NN520/AI/src/lib/isomorphic/browser.ts b/spaces/NN520/AI/src/lib/isomorphic/browser.ts
deleted file mode 100644
index de125b1f1786d1618cb1ff47f403d76c6784f4ce..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/src/lib/isomorphic/browser.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-'use client'
-
-const debug = console.info.bind(console)
-
-class WebSocketAlias extends WebSocket {
-  constructor(address: string | URL, ...args: any) {
-    super(address)
-  }
-}
-
-export default { fetch, WebSocket: WebSocketAlias, debug }
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/huffman/huffman_coder.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/huffman/huffman_coder.py
deleted file mode 100644
index 6531f1547cbd7250aa03e0ef8c2efbac49bb1aff..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/huffman/huffman_coder.py
+++ /dev/null
@@ -1,265 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import re
-import typing as tp
-from collections import Counter, deque
-from dataclasses import dataclass
-
-from bitarray import bitarray, util
-from fairseq.data import Dictionary
-
-# basically we have to write to addressable bytes for the memory mapped
-# dataset loader. Sentences that get encoded to a length that is not a
-# multiple of BLOCKSIZE (a byte) will be padded to fit. (see _pad in the coder)
-BLOCKSIZE = 8
-
-
-class HuffmanCoder:
-    def __init__(
-        self, root: "HuffmanNode", bos="<s>", pad="<pad>", eos="</s>", unk="<unk>"
-    ):
-        self.root = root
-        self.table = root.code_table()
-        self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos
-
-    def _pad(self, a: bitarray) -> bitarray:
-        """
-        bitpadding, 1 then 0.
-
-        If the array is already a multiple of blocksize, we add a full block.
-        """
-        pad_len = BLOCKSIZE - (len(a) % BLOCKSIZE) - 1
-        padding = bitarray("1" + "0" * pad_len)
-        return a + padding
-
-    def _unpad(self, a: bitarray) -> bitarray:
-        """
-        remove the bitpadding.
-
-        There will be a set of 0s preceded by a 1 at the end of the bitarray, we remove that
-        """
-        # count the 0 padding at the end until we find the first 1
-        # we want to remove the one too
-        remove_cnt = util.rindex(a, 1)
-        return a[:remove_cnt]
-
-    def encode(self, iter: tp.List[str]) -> bytes:
-        """
-        encode a list of tokens a return bytes. We use bitpadding to make sure the encoded bits fit in bytes.
-        """
-        a = bitarray()
-        for token in iter:
-            code = self.get_code(token)
-            if code is None:
-                if self.unk_word is None:
-                    raise Exception(f"unknown token {token} cannot be encoded.")
-                else:
-                    token = self.unk_word
-            a = a + self.get_code(token)
-        return self._pad(a).tobytes()
-
-    def decode(self, bits: bytes) -> tp.Iterator["HuffmanNode"]:
-        """
-        take bitpadded bytes and decode it to a set of leaves. You can then use each node to find the symbol/id
-        """
-        a = bitarray()
-        a.frombytes(bits)
-        return self.root.decode(self._unpad(a))
-
-    def get_code(self, symbol: str) -> tp.Optional[bitarray]:
-        node = self.get_node(symbol)
-        return None if node is None else node.code
-
-    def get_node(self, symbol: str) -> "HuffmanNode":
-        return self.table.get(symbol)
-
-    @classmethod
-    def from_file(
-        cls,
-        filename: str,
-        bos="<s>",
-        pad="<pad>",
-        eos="</s>",
-        unk="<unk>",
-    ) -> "HuffmanCoder":
-        builder = HuffmanCodeBuilder.from_file(filename)
-        return builder.build_code(bos=bos, pad=pad, eos=eos, unk=unk)
-
-    def to_file(self, filename, sep="\t"):
-        nodes = list(self.table.values())
-        nodes.sort(key=lambda n: n.id)
-        with open(filename, "w", encoding="utf-8") as output:
-            for n in nodes:
-                output.write(f"{n.symbol}{sep}{n.count}\n")
-
-    def __iter__(self):
-        for n in self.table.values():
-            yield n
-
-    def merge(self, other_coder: "HuffmanCoder") -> "HuffmanCoder":
-        builder = HuffmanCodeBuilder()
-        for n in self:
-            builder.increment(n.symbol, n.count)
-        for n in other_coder:
-            builder.increment(n.symbol, n.count)
-        return builder.build_code()
-
-    def __eq__(self, other: "HuffmanCoder") -> bool:
-        return self.table == other.table
-
-    def __len__(self) -> int:
-        return len(self.table)
-
-    def __contains__(self, sym: str) -> bool:
-        return sym in self.table
-
-    def to_dictionary(self) -> Dictionary:
-        dictionary = Dictionary(bos=self.bos, unk=self.unk, pad=self.pad, eos=self.eos)
-        for n in self:
-            dictionary.add_symbol(n.symbol, n=n.count)
-        dictionary.finalize()
-        return dictionary
-
-
-@dataclass
-class HuffmanNode:
-    """
-    a node in a Huffman tree
-    """
-
-    id: int
-    count: int
-    symbol: tp.Optional[str] = None
-    left: tp.Optional["HuffmanNode"] = None
-    right: tp.Optional["HuffmanNode"] = None
-    code: tp.Optional[bitarray] = None
-
-    def is_leaf(self) -> bool:
-        return self.left is None and self.right is None
-
-    def code_table(self, prefix: tp.Optional[bitarray] = None) -> tp.Dict[str, "HuffmanNode"]:
-        defaulted_prefix = prefix if prefix is not None else bitarray()
-        if self.is_leaf():
-            self.code = (
-                defaulted_prefix if len(defaulted_prefix) > 0 else bitarray("0")
-            )  # leaf could be the root if there is only one symbol
-            return {self.symbol: self}
-
-        codes_right = self.right.code_table(defaulted_prefix + bitarray([0]))
-        codes_left = self.left.code_table(defaulted_prefix + bitarray([1]))
-        return {**codes_left, **codes_right}
-
-    def decode(self, bits: bitarray) -> tp.Iterator["HuffmanNode"]:
-        current_node = self
-        for bit in bits:
-            if bit == 0:  # go right
-                current_node = current_node.right
-            else:  # go left
-                current_node = current_node.left
-            if current_node is None:
-                # we shouldn't be on a leaf here
-                raise Exception("fell off a leaf")
-            if current_node.is_leaf():
-                yield current_node
-                current_node = self
-        if current_node != self:
-            raise Exception("couldn't decode all the bits")
-
-
-class HuffmanCodeBuilder:
-    """
-    build a dictionary with occurence count and then build the Huffman code for it.
-    """
-
-    def __init__(self):
-        self.symbols = Counter()
-
-    def add_symbols(self, *syms) -> None:
-        self.symbols.update(syms)
-
-    def increment(self, symbol: str, cnt: int) -> None:
-        self.symbols[symbol] += cnt
-
-    @classmethod
-    def from_file(cls, filename):
-        c = cls()
-        with open(filename, "r", encoding="utf-8") as input:
-            for line in input:
-                split = re.split(r"[\s]+", line)
-                c.increment(split[0], int(split[1]))
-        return c
-
-    def to_file(self, filename, sep="\t"):
-        with open(filename, "w", encoding="utf-8") as output:
-            for (tok, cnt) in self.symbols.most_common():
-                output.write(f"{tok}{sep}{cnt}\n")
-
-    def _smallest(self, q1: deque, q2: deque) -> HuffmanNode:
-        if len(q1) == 0:
-            return q2.pop()
-
-        if len(q2) == 0:
-            return q1.pop()
-
-        if q1[-1].count < q2[-1].count:
-            return q1.pop()
-
-        return q2.pop()
-
-    def __add__(self, c: "HuffmanCodeBuilder") -> "HuffmanCodeBuilder":
-        new_c = self.symbols + c.symbols
-        new_b = HuffmanCodeBuilder()
-        new_b.symbols = new_c
-        return new_b
-
-    def build_code(
-        self,
-        bos="<s>",
-        pad="<pad>",
-        eos="</s>",
-        unk="<unk>",
-    ) -> HuffmanCoder:
-        assert len(self.symbols) > 0, "cannot build code from empty list of symbols"
-
-        if self.symbols[bos] == 0:
-            self.add_symbols(bos)
-        if self.symbols[pad] == 0:
-            self.add_symbols(pad)
-        if self.symbols[eos] == 0:
-            self.add_symbols(eos)
-        if self.symbols[unk] == 0:
-            self.add_symbols(unk)
-
-        node_id = 0
-        leaves_queue = deque(
-            [
-                HuffmanNode(symbol=symbol, count=count, id=idx)
-                for idx, (symbol, count) in enumerate(self.symbols.most_common())
-            ]
-        )  # left are the most common, right are the least common
-
-        if len(leaves_queue) == 1:
-            root = leaves_queue.pop()
-            root.id = 0
-            return HuffmanCoder(root)
-
-        nodes_queue = deque()
-
-        while len(leaves_queue) > 0 or len(nodes_queue) != 1:
-            # get the lowest two nodes at the head of each queue
-            node1 = self._smallest(leaves_queue, nodes_queue)
-            node2 = self._smallest(leaves_queue, nodes_queue)
-
-            # add new node
-            nodes_queue.appendleft(
-                HuffmanNode(
-                    count=node1.count + node2.count, left=node1, right=node2, id=node_id
-                )
-            )
-            node_id += 1
-
-        # we are left with the root
-        return HuffmanCoder(nodes_queue.pop(), bos=bos, pad=pad, eos=eos, unk=unk)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/data/data_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/data/data_utils.py
deleted file mode 100644
index 7f843789138c62668f9e1c4e7fd44299fb5ef768..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/data/data_utils.py
+++ /dev/null
@@ -1,601 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-try:
-    from collections.abc import Iterable
-except ImportError:
-    from collections import Iterable
-import contextlib
-import itertools
-import logging
-import re
-import warnings
-from typing import Optional, Tuple
-
-import numpy as np
-import torch
-
-from fairseq.file_io import PathManager
-from fairseq import utils
-import os
-
-logger = logging.getLogger(__name__)
-
-
-def infer_language_pair(path):
-    """Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
-    src, dst = None, None
-    for filename in PathManager.ls(path):
-        parts = filename.split(".")
-        if len(parts) >= 3 and len(parts[1].split("-")) == 2:
-            return parts[1].split("-")
-    return src, dst
-
-
-def collate_tokens(
-    values,
-    pad_idx,
-    eos_idx=None,
-    left_pad=False,
-    move_eos_to_beginning=False,
-    pad_to_length=None,
-    pad_to_multiple=1,
-    pad_to_bsz=None,
-):
-    """Convert a list of 1d tensors into a padded 2d tensor."""
-    size = max(v.size(0) for v in values)
-    size = size if pad_to_length is None else max(size, pad_to_length)
-    if pad_to_multiple != 1 and size % pad_to_multiple != 0:
-        size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
-
-    def copy_tensor(src, dst):
-        assert dst.numel() == src.numel()
-        if move_eos_to_beginning:
-            if eos_idx is None:
-                # if no eos_idx is specified, then use the last token in src
-                dst[0] = src[-1]
-            else:
-                dst[0] = eos_idx
-            dst[1:] = src[:-1]
-        else:
-            dst.copy_(src)
-
-    if values[0].dim() == 1:
-        res = values[0].new(len(values), size).fill_(pad_idx)
-    elif values[0].dim() == 2:
-        assert move_eos_to_beginning is False
-        res = values[0].new(len(values), size, values[0].size(1)).fill_(pad_idx)
-    else:
-        raise NotImplementedError
-
-    for i, v in enumerate(values):
-        copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
-    return res
-
-
-def load_indexed_dataset(
-    path, dictionary=None, dataset_impl=None, combine=False, default="cached"
-):
-    """A helper function for loading indexed datasets.
-
-    Args:
-        path (str): path to indexed dataset (e.g., 'data-bin/train')
-        dictionary (~fairseq.data.Dictionary): data dictionary
-        dataset_impl (str, optional): which dataset implementation to use. If
-            not provided, it will be inferred automatically. For legacy indexed
-            data we use the 'cached' implementation by default.
-        combine (bool, optional): automatically load and combine multiple
-            datasets. For example, if *path* is 'data-bin/train', then we will
-            combine 'data-bin/train', 'data-bin/train1', ... and return a
-            single ConcatDataset instance.
-    """
-    import fairseq.data.indexed_dataset as indexed_dataset
-    from fairseq.data.concat_dataset import ConcatDataset
-
-    datasets = []
-    for k in itertools.count():
-        path_k = path + (str(k) if k > 0 else "")
-        try:
-            path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
-        except Exception as e:
-            if "StorageException: [404] Path not found" in str(e):
-                logger.warning(f"path_k: {e} not found")
-            else:
-                raise e
-
-        dataset_impl_k = dataset_impl
-        if dataset_impl_k is None:
-            dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
-        dataset = indexed_dataset.make_dataset(
-            path_k,
-            impl=dataset_impl_k or default,
-            fix_lua_indexing=True,
-            dictionary=dictionary,
-        )
-        if dataset is None:
-            break
-        logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
-        datasets.append(dataset)
-        if not combine:
-            break
-    if len(datasets) == 0:
-        return None
-    elif len(datasets) == 1:
-        return datasets[0]
-    else:
-        return ConcatDataset(datasets)
-
-
-@contextlib.contextmanager
-def numpy_seed(seed, *addl_seeds):
-    """Context manager which seeds the NumPy PRNG with the specified seed and
-    restores the state afterward"""
-    if seed is None:
-        yield
-        return
-    if len(addl_seeds) > 0:
-        seed = int(hash((seed, *addl_seeds)) % 1e6)
-    state = np.random.get_state()
-    np.random.seed(seed)
-    try:
-        yield
-    finally:
-        np.random.set_state(state)
-
-
-def collect_filtered(function, iterable, filtered):
-    """
-    Similar to :func:`filter` but collects filtered elements in ``filtered``.
-
-    Args:
-        function (callable): function that returns ``False`` for elements that
-            should be filtered
-        iterable (iterable): iterable to filter
-        filtered (list): list to store filtered elements
-    """
-    for el in iterable:
-        if function(el):
-            yield el
-        else:
-            filtered.append(el)
-
-
-def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
-    def compare_leq(a, b):
-        return a <= b if not isinstance(a, tuple) else max(a) <= b
-
-    def check_size(idx):
-        if isinstance(max_positions, float) or isinstance(max_positions, int):
-            return size_fn(idx) <= max_positions
-        elif isinstance(max_positions, dict):
-            idx_size = size_fn(idx)
-            assert isinstance(idx_size, dict)
-            intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
-            return all(
-                all(
-                    a is None or b is None or a <= b
-                    for a, b in zip(idx_size[key], max_positions[key])
-                )
-                for key in intersect_keys
-            )
-        else:
-            # For MultiCorpusSampledDataset, will generalize it later
-            if not isinstance(size_fn(idx), Iterable):
-                return all(size_fn(idx) <= b for b in max_positions)
-            return all(
-                a is None or b is None or a <= b
-                for a, b in zip(size_fn(idx), max_positions)
-            )
-
-    ignored = []
-    itr = collect_filtered(check_size, indices, ignored)
-    indices = np.fromiter(itr, dtype=np.int64, count=-1)
-    return indices, ignored
-
-
-def filter_by_size(indices, dataset, max_positions, raise_exception=False):
-    """
-    [deprecated] Filter indices based on their size.
-    Use `FairseqDataset::filter_indices_by_size` instead.
-
-    Args:
-        indices (List[int]): ordered list of dataset indices
-        dataset (FairseqDataset): fairseq dataset instance
-        max_positions (tuple): filter elements larger than this size.
-            Comparisons are done component-wise.
-        raise_exception (bool, optional): if ``True``, raise an exception if
-            any elements are filtered (default: False).
-    """
-    warnings.warn(
-        "data_utils.filter_by_size is deprecated. "
-        "Use `FairseqDataset::filter_indices_by_size` instead.",
-        stacklevel=2,
-    )
-    if isinstance(max_positions, float) or isinstance(max_positions, int):
-        if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
-            ignored = indices[dataset.sizes[indices] > max_positions].tolist()
-            indices = indices[dataset.sizes[indices] <= max_positions]
-        elif (
-            hasattr(dataset, "sizes")
-            and isinstance(dataset.sizes, list)
-            and len(dataset.sizes) == 1
-        ):
-            ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
-            indices = indices[dataset.sizes[0][indices] <= max_positions]
-        else:
-            indices, ignored = _filter_by_size_dynamic(
-                indices, dataset.size, max_positions
-            )
-    else:
-        indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
-
-    if len(ignored) > 0 and raise_exception:
-        raise Exception(
-            (
-                "Size of sample #{} is invalid (={}) since max_positions={}, "
-                "skip this example with --skip-invalid-size-inputs-valid-test"
-            ).format(ignored[0], dataset.size(ignored[0]), max_positions)
-        )
-    if len(ignored) > 0:
-        logger.warning(
-            (
-                "{} samples have invalid sizes and will be skipped, "
-                "max_positions={}, first few sample ids={}"
-            ).format(len(ignored), max_positions, ignored[:10])
-        )
-    return indices
-
-
-def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
-    """Filter a list of sample indices. Remove those that are longer
-        than specified in max_sizes.
-
-    Args:
-        indices (np.array): original array of sample indices
-        max_sizes (int or list[int] or tuple[int]): max sample size,
-            can be defined separately for src and tgt (then list or tuple)
-
-    Returns:
-        np.array: filtered sample array
-        list: list of removed indices
-    """
-    if max_sizes is None:
-        return indices, []
-    if type(max_sizes) in (int, float):
-        max_src_size, max_tgt_size = max_sizes, max_sizes
-    else:
-        max_src_size, max_tgt_size = max_sizes
-    if tgt_sizes is None:
-        ignored = indices[src_sizes[indices] > max_src_size]
-    else:
-        ignored = indices[
-            (src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
-        ]
-    if len(ignored) > 0:
-        if tgt_sizes is None:
-            indices = indices[src_sizes[indices] <= max_src_size]
-        else:
-            indices = indices[
-                (src_sizes[indices] <= max_src_size)
-                & (tgt_sizes[indices] <= max_tgt_size)
-            ]
-    return indices, ignored.tolist()
-
-
-def batch_by_size(
-    indices,
-    num_tokens_fn,
-    num_tokens_vec=None,
-    max_tokens=None,
-    max_sentences=None,
-    required_batch_size_multiple=1,
-    fixed_shapes=None,
-):
-    """
-    Yield mini-batches of indices bucketed by size. Batches may contain
-    sequences of different lengths.
-
-    Args:
-        indices (List[int]): ordered list of dataset indices
-        num_tokens_fn (callable): function that returns the number of tokens at
-            a given index
-        num_tokens_vec (List[int], optional): precomputed vector of the number
-            of tokens for each index in indices (to enable faster batch generation)
-        max_tokens (int, optional): max number of tokens in each batch
-            (default: None).
-        max_sentences (int, optional): max number of sentences in each
-            batch (default: None).
-        required_batch_size_multiple (int, optional): require batch size to
-            be less than N or a multiple of N (default: 1).
-        fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
-            only be created with the given shapes. *max_sentences* and
-            *required_batch_size_multiple* will be ignored (default: None).
-    """
-    try:
-        from fairseq.data.data_utils_fast import (
-            batch_by_size_fn,
-            batch_by_size_vec,
-            batch_fixed_shapes_fast,
-        )
-    except ImportError:
-        raise ImportError(
-            "Please build Cython components with: "
-            "`python setup.py build_ext --inplace`"
-        )
-    except ValueError:
-        raise ValueError(
-            "Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`."
-        )
-
-    # added int() to avoid TypeError: an integer is required
-    max_tokens = (
-        int(max_tokens) if max_tokens is not None else -1
-    )
-    max_sentences = max_sentences if max_sentences is not None else -1
-    bsz_mult = required_batch_size_multiple
-
-    if not isinstance(indices, np.ndarray):
-        indices = np.fromiter(indices, dtype=np.int64, count=-1)
-
-    if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
-        num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)
-
-    if fixed_shapes is None:
-        if num_tokens_vec is None:
-            return batch_by_size_fn(
-                indices,
-                num_tokens_fn,
-                max_tokens,
-                max_sentences,
-                bsz_mult,
-            )
-        else:
-            return batch_by_size_vec(
-                indices,
-                num_tokens_vec,
-                max_tokens,
-                max_sentences,
-                bsz_mult,
-            )
-
-    else:
-        fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
-        sort_order = np.lexsort(
-            [
-                fixed_shapes[:, 1].argsort(),  # length
-                fixed_shapes[:, 0].argsort(),  # bsz
-            ]
-        )
-        fixed_shapes_sorted = fixed_shapes[sort_order]
-        return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)
-
-
-def post_process(sentence: str, symbol: str):
-    if symbol == "sentencepiece":
-        sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
-    elif symbol == "wordpiece":
-        sentence = sentence.replace(" ", "").replace("_", " ").strip()
-    elif symbol == "letter":
-        sentence = sentence.replace(" ", "").replace("|", " ").strip()
-    elif symbol == "silence":
-        import re
-        sentence = sentence.replace("<SIL>", "")
-        sentence = re.sub(' +', ' ', sentence).strip()
-    elif symbol == "_EOW":
-        sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
-    elif symbol in {"subword_nmt", "@@ ", "@@"}:
-        if symbol == "subword_nmt":
-            symbol = "@@ "
-        sentence = (sentence + " ").replace(symbol, "").rstrip()
-    elif symbol == "none":
-        pass
-    elif symbol is not None:
-        raise NotImplementedError(f"Unknown post_process option: {symbol}")
-    return sentence
-
-
-def compute_mask_indices(
-    shape: Tuple[int, int],
-    padding_mask: Optional[torch.Tensor],
-    mask_prob: float,
-    mask_length: int,
-    mask_type: str = "static",
-    mask_other: float = 0.0,
-    min_masks: int = 0,
-    no_overlap: bool = False,
-    min_space: int = 0,
-) -> np.ndarray:
-    """
-    Computes random mask spans for a given shape
-
-    Args:
-        shape: the the shape for which to compute masks.
-            should be of size 2 where first element is batch size and 2nd is timesteps
-        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
-        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
-            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
-            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
-        mask_type: how to compute mask lengths
-            static = fixed size
-            uniform = sample from uniform distribution [mask_other, mask_length*2]
-            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
-            poisson = sample from possion distribution with lambda = mask length
-        min_masks: minimum number of masked spans
-        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
-        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
-    """
-
-    bsz, all_sz = shape
-    mask = np.full((bsz, all_sz), False)
-
-    all_num_mask = int(
-        # add a random number for probabilistic rounding
-        mask_prob * all_sz / float(mask_length)
-        + np.random.rand()
-    )
-
-    all_num_mask = max(min_masks, all_num_mask)
-
-    mask_idcs = []
-    for i in range(bsz):
-        if padding_mask is not None:
-            sz = all_sz - padding_mask[i].long().sum().item()
-            num_mask = int(
-                # add a random number for probabilistic rounding
-                mask_prob * sz / float(mask_length)
-                + np.random.rand()
-            )
-            num_mask = max(min_masks, num_mask)
-        else:
-            sz = all_sz
-            num_mask = all_num_mask
-
-        if mask_type == "static":
-            lengths = np.full(num_mask, mask_length)
-        elif mask_type == "uniform":
-            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
-        elif mask_type == "normal":
-            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
-            lengths = [max(1, int(round(x))) for x in lengths]
-        elif mask_type == "poisson":
-            lengths = np.random.poisson(mask_length, size=num_mask)
-            lengths = [int(round(x)) for x in lengths]
-        else:
-            raise Exception("unknown mask selection " + mask_type)
-
-        if sum(lengths) == 0:
-            lengths[0] = min(mask_length, sz - 1)
-
-        if no_overlap:
-            mask_idc = []
-
-            def arrange(s, e, length, keep_length):
-                span_start = np.random.randint(s, e - length)
-                mask_idc.extend(span_start + i for i in range(length))
-
-                new_parts = []
-                if span_start - s - min_space >= keep_length:
-                    new_parts.append((s, span_start - min_space + 1))
-                if e - span_start - keep_length - min_space > keep_length:
-                    new_parts.append((span_start + length + min_space, e))
-                return new_parts
-
-            parts = [(0, sz)]
-            min_length = min(lengths)
-            for length in sorted(lengths, reverse=True):
-                lens = np.fromiter(
-                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
-                    np.int,
-                )
-                l_sum = np.sum(lens)
-                if l_sum == 0:
-                    break
-                probs = lens / np.sum(lens)
-                c = np.random.choice(len(parts), p=probs)
-                s, e = parts.pop(c)
-                parts.extend(arrange(s, e, length, min_length))
-            mask_idc = np.asarray(mask_idc)
-        else:
-            min_len = min(lengths)
-            if sz - min_len <= num_mask:
-                min_len = sz - num_mask - 1
-
-            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
-
-            mask_idc = np.asarray(
-                [
-                    mask_idc[j] + offset
-                    for j in range(len(mask_idc))
-                    for offset in range(lengths[j])
-                ]
-            )
-
-        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
-
-    min_len = min([len(m) for m in mask_idcs])
-    for i, mask_idc in enumerate(mask_idcs):
-        if len(mask_idc) > min_len:
-            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
-        mask[i, mask_idc] = True
-
-    return mask
-
-
-def get_mem_usage():
-    try:
-        import psutil
-
-        mb = 1024 * 1024
-        return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
-    except ImportError:
-        return "N/A"
-
-
-# lens: torch.LongTensor
-# returns: torch.BoolTensor
-def lengths_to_padding_mask(lens):
-    bsz, max_lens = lens.size(0), torch.max(lens).item()
-    mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
-    mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
-    return mask
-
-
-# lens: torch.LongTensor
-# returns: torch.BoolTensor
-def lengths_to_mask(lens):
-    return ~lengths_to_padding_mask(lens)
-
-
-def get_buckets(sizes, num_buckets):
-    buckets = np.unique(
-        np.percentile(
-            sizes,
-            np.linspace(0, 100, num_buckets + 1),
-            interpolation='lower',
-        )[1:]
-    )
-    return buckets
-
-
-def get_bucketed_sizes(orig_sizes, buckets):
-    sizes = np.copy(orig_sizes)
-    assert np.min(sizes) >= 0
-    start_val = -1
-    for end_val in buckets:
-        mask = (sizes > start_val) & (sizes <= end_val)
-        sizes[mask] = end_val
-        start_val = end_val
-    return sizes
-
-
-
-def _find_extra_valid_paths(dataset_path: str) -> set:
-    paths = utils.split_paths(dataset_path)
-    all_valid_paths = set()
-    for sub_dir in paths:
-        contents = PathManager.ls(sub_dir)
-        valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None]
-        all_valid_paths |= {os.path.basename(p) for p in valid_paths}
-    # Remove .bin, .idx etc
-    roots = {os.path.splitext(p)[0] for p in all_valid_paths}
-    return roots
-
-
-def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
-    """Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
-    if (
-        train_cfg.dataset.ignore_unused_valid_subsets
-        or train_cfg.dataset.combine_valid_subsets
-        or train_cfg.dataset.disable_validation
-        or not hasattr(train_cfg.task, "data")
-    ):
-        return
-    other_paths = _find_extra_valid_paths(train_cfg.task.data)
-    specified_subsets = train_cfg.dataset.valid_subset.split(",")
-    ignored_paths = [p for p in other_paths if p not in specified_subsets]
-    if ignored_paths:
-        advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them."
-        msg = f"Valid paths {ignored_paths} will be ignored. {advice}"
-        raise ValueError(msg)
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/noisychannel/rerank_score_bw.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/noisychannel/rerank_score_bw.py
deleted file mode 100644
index b0bc913651bd76667e25c214acb70f2bca19e185..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/noisychannel/rerank_score_bw.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-from contextlib import redirect_stdout
-
-from fairseq import options
-from fairseq_cli import generate
-
-from examples.noisychannel import rerank_options, rerank_utils
-
-
-def score_bw(args):
-    if args.backwards1:
-        scorer1_src = args.target_lang
-        scorer1_tgt = args.source_lang
-    else:
-        scorer1_src = args.source_lang
-        scorer1_tgt = args.target_lang
-
-    if args.score_model2 is not None:
-        if args.backwards2:
-            scorer2_src = args.target_lang
-            scorer2_tgt = args.source_lang
-        else:
-            scorer2_src = args.source_lang
-            scorer2_tgt = args.target_lang
-
-    rerank1_is_gen = (
-        args.gen_model == args.score_model1 and args.source_prefix_frac is None
-    )
-    rerank2_is_gen = (
-        args.gen_model == args.score_model2 and args.source_prefix_frac is None
-    )
-
-    (
-        pre_gen,
-        left_to_right_preprocessed_dir,
-        right_to_left_preprocessed_dir,
-        backwards_preprocessed_dir,
-        lm_preprocessed_dir,
-    ) = rerank_utils.get_directories(
-        args.data_dir_name,
-        args.num_rescore,
-        args.gen_subset,
-        args.gen_model_name,
-        args.shard_id,
-        args.num_shards,
-        args.sampling,
-        args.prefix_len,
-        args.target_prefix_frac,
-        args.source_prefix_frac,
-    )
-
-    score1_file = rerank_utils.rescore_file_name(
-        pre_gen,
-        args.prefix_len,
-        args.model1_name,
-        target_prefix_frac=args.target_prefix_frac,
-        source_prefix_frac=args.source_prefix_frac,
-        backwards=args.backwards1,
-    )
-
-    if args.score_model2 is not None:
-        score2_file = rerank_utils.rescore_file_name(
-            pre_gen,
-            args.prefix_len,
-            args.model2_name,
-            target_prefix_frac=args.target_prefix_frac,
-            source_prefix_frac=args.source_prefix_frac,
-            backwards=args.backwards2,
-        )
-
-    if args.right_to_left1:
-        rerank_data1 = right_to_left_preprocessed_dir
-    elif args.backwards1:
-        rerank_data1 = backwards_preprocessed_dir
-    else:
-        rerank_data1 = left_to_right_preprocessed_dir
-
-    gen_param = ["--batch-size", str(128), "--score-reference", "--gen-subset", "train"]
-    if not rerank1_is_gen and not os.path.isfile(score1_file):
-        print("STEP 4: score the translations for model 1")
-
-        model_param1 = [
-            "--path",
-            args.score_model1,
-            "--source-lang",
-            scorer1_src,
-            "--target-lang",
-            scorer1_tgt,
-        ]
-        gen_model1_param = [rerank_data1] + gen_param + model_param1
-
-        gen_parser = options.get_generation_parser()
-        input_args = options.parse_args_and_arch(gen_parser, gen_model1_param)
-
-        with open(score1_file, "w") as f:
-            with redirect_stdout(f):
-                generate.main(input_args)
-
-    if (
-        args.score_model2 is not None
-        and not os.path.isfile(score2_file)
-        and not rerank2_is_gen
-    ):
-        print("STEP 4: score the translations for model 2")
-
-        if args.right_to_left2:
-            rerank_data2 = right_to_left_preprocessed_dir
-        elif args.backwards2:
-            rerank_data2 = backwards_preprocessed_dir
-        else:
-            rerank_data2 = left_to_right_preprocessed_dir
-
-        model_param2 = [
-            "--path",
-            args.score_model2,
-            "--source-lang",
-            scorer2_src,
-            "--target-lang",
-            scorer2_tgt,
-        ]
-        gen_model2_param = [rerank_data2] + gen_param + model_param2
-
-        gen_parser = options.get_generation_parser()
-        input_args = options.parse_args_and_arch(gen_parser, gen_model2_param)
-
-        with open(score2_file, "w") as f:
-            with redirect_stdout(f):
-                generate.main(input_args)
-
-
-def cli_main():
-    parser = rerank_options.get_reranking_parser()
-    args = options.parse_args_and_arch(parser)
-    score_bw(args)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py
deleted file mode 100644
index 7c4d8ba3802a2da9467c42b0aa18653c7bbb2ec9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import logging
-import math
-
-import torch
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-
-
-@register_criterion("cross_entropy_acc")
-class CrossEntropyWithAccCriterion(FairseqCriterion):
-    def __init__(self, task, sentence_avg):
-        super().__init__(task)
-        self.sentence_avg = sentence_avg
-
-    def compute_loss(self, model, net_output, target, reduction, log_probs):
-        # N, T -> N * T
-        target = target.view(-1)
-        lprobs = model.get_normalized_probs(net_output, log_probs=log_probs)
-        if not hasattr(lprobs, "batch_first"):
-            logging.warning(
-                "ERROR: we need to know whether "
-                "batch first for the net output; "
-                "you need to set batch_first attribute for the return value of "
-                "model.get_normalized_probs. Now, we assume this is true, but "
-                "in the future, we will raise exception instead. "
-            )
-        batch_first = getattr(lprobs, "batch_first", True)
-        if not batch_first:
-            lprobs = lprobs.transpose(0, 1)
-
-        # N, T, D -> N * T, D
-        lprobs = lprobs.view(-1, lprobs.size(-1))
-        loss = F.nll_loss(
-            lprobs, target, ignore_index=self.padding_idx, reduction=reduction
-        )
-        return lprobs, loss
-
-    def get_logging_output(self, sample, target, lprobs, loss):
-        target = target.view(-1)
-        mask = target != self.padding_idx
-        correct = torch.sum(
-            lprobs.argmax(1).masked_select(mask) == target.masked_select(mask)
-        )
-        total = torch.sum(mask)
-        sample_size = (
-            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
-        )
-
-        logging_output = {
-            "loss": utils.item(loss.data),  # * sample['ntokens'],
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-            "correct": utils.item(correct.data),
-            "total": utils.item(total.data),
-            "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(),
-        }
-
-        return sample_size, logging_output
-
-    def forward(self, model, sample, reduction="sum", log_probs=True):
-        """Computes the cross entropy with accuracy metric for the given sample.
-
-        This is similar to CrossEntropyCriterion in fairseq, but also
-        computes accuracy metrics as part of logging
-
-        Args:
-            logprobs (Torch.tensor) of shape N, T, D i.e.
-                batchsize, timesteps, dimensions
-            targets (Torch.tensor) of shape N, T  i.e batchsize, timesteps
-
-        Returns:
-        tuple: With three elements:
-            1) the loss
-            2) the sample size, which is used as the denominator for the gradient
-            3) logging outputs to display while training
-
-        TODO:
-            * Currently this Criterion will only work with LSTMEncoderModels or
-            FairseqModels which have decoder, or Models which return TorchTensor
-            as net_output.
-            We need to make a change to support all FairseqEncoder models.
-        """
-        net_output = model(**sample["net_input"])
-        target = model.get_targets(sample, net_output)
-        lprobs, loss = self.compute_loss(
-            model, net_output, target, reduction, log_probs
-        )
-        sample_size, logging_output = self.get_logging_output(
-            sample, target, lprobs, loss
-        )
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def aggregate_logging_outputs(logging_outputs):
-        """Aggregate logging outputs from data parallel training."""
-        correct_sum = sum(log.get("correct", 0) for log in logging_outputs)
-        total_sum = sum(log.get("total", 0) for log in logging_outputs)
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-        nframes = sum(log.get("nframes", 0) for log in logging_outputs)
-        agg_output = {
-            "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0,
-            # if args.sentence_avg, then sample_size is nsentences, then loss
-            # is per-sentence loss; else sample_size is ntokens, the loss
-            # becomes per-output token loss
-            "ntokens": ntokens,
-            "nsentences": nsentences,
-            "nframes": nframes,
-            "sample_size": sample_size,
-            "acc": correct_sum * 100.0 / total_sum if total_sum > 0 else 0.0,
-            "correct": correct_sum,
-            "total": total_sum,
-            # total is the number of validate tokens
-        }
-        if sample_size != ntokens:
-            agg_output["nll_loss"] = loss_sum / ntokens / math.log(2)
-        # loss: per output token loss
-        # nll_loss: per sentence loss
-        return agg_output
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/data/replabels.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/data/replabels.py
deleted file mode 100644
index 441f1bd432b95865fc981c6c695cee299b07ed62..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/data/replabels.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Replabel transforms for use with flashlight's ASG criterion.
-"""
-
-
-def replabel_symbol(i):
-    """
-    Replabel symbols used in flashlight, currently just "1", "2", ...
-    This prevents training with numeral tokens, so this might change in the future
-    """
-    return str(i)
-
-
-def pack_replabels(tokens, dictionary, max_reps):
-    """
-    Pack a token sequence so that repeated symbols are replaced by replabels
-    """
-    if len(tokens) == 0 or max_reps <= 0:
-        return tokens
-
-    replabel_value_to_idx = [0] * (max_reps + 1)
-    for i in range(1, max_reps + 1):
-        replabel_value_to_idx[i] = dictionary.index(replabel_symbol(i))
-
-    result = []
-    prev_token = -1
-    num_reps = 0
-    for token in tokens:
-        if token == prev_token and num_reps < max_reps:
-            num_reps += 1
-        else:
-            if num_reps > 0:
-                result.append(replabel_value_to_idx[num_reps])
-                num_reps = 0
-            result.append(token)
-            prev_token = token
-    if num_reps > 0:
-        result.append(replabel_value_to_idx[num_reps])
-    return result
-
-
-def unpack_replabels(tokens, dictionary, max_reps):
-    """
-    Unpack a token sequence so that replabels are replaced by repeated symbols
-    """
-    if len(tokens) == 0 or max_reps <= 0:
-        return tokens
-
-    replabel_idx_to_value = {}
-    for i in range(1, max_reps + 1):
-        replabel_idx_to_value[dictionary.index(replabel_symbol(i))] = i
-
-    result = []
-    prev_token = -1
-    for token in tokens:
-        try:
-            for _ in range(replabel_idx_to_value[token]):
-                result.append(prev_token)
-            prev_token = -1
-        except KeyError:
-            result.append(token)
-            prev_token = token
-    return result
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/truncated_bptt/transformer_xl_model.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/truncated_bptt/transformer_xl_model.py
deleted file mode 100644
index a6c8b25a07276c2ee30c0aa5f0e4b0a2837ed5ca..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/truncated_bptt/transformer_xl_model.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import torch
-from fairseq.dataclass import FairseqDataclass
-from fairseq.models import (
-    FairseqIncrementalDecoder,
-    FairseqLanguageModel,
-    register_model,
-)
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from omegaconf import II
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class TransformerXLConfig(FairseqDataclass):
-    # defaults come from the original Transformer-XL code
-    cutoffs: List[int] = field(default_factory=lambda: [20000, 40000, 200000])
-    d_model: int = 500
-    n_head: int = 10
-    d_head: int = 50
-    d_inner: int = 1000
-    div_val: int = 1
-    n_layer: int = 12
-    mem_len: int = 0
-    clamp_len: int = -1
-    same_length: bool = False
-    dropout: float = 0.0
-    dropatt: float = 0.0
-    checkpoint_activations: bool = False
-    offload_activations: bool = False
-    max_target_positions: int = II("task.max_target_positions")
-
-
-@register_model("transformer_xl", dataclass=TransformerXLConfig)
-class TransformerXLLanguageModel(FairseqLanguageModel):
-    @classmethod
-    def build_model(cls, cfg: TransformerXLConfig, task):
-        return cls(TransformerXLDecoder(cfg, task))
-
-
-class TransformerXLDecoder(FairseqIncrementalDecoder):
-    def __init__(self, cfg, task):
-        try:
-            from transformers.models.transfo_xl import (
-                TransfoXLConfig,
-                TransfoXLLMHeadModel,
-            )
-        except ImportError:
-            from transformers.configuration_transfo_xl import TransfoXLConfig
-            from transformers.modeling_transfo_xl import TransfoXLLMHeadModel
-
-        super().__init__(task.target_dictionary)
-        self.cfg = cfg
-
-        # remove any cutoffs larger than the vocab size
-        cutoffs = [
-            cutoff for cutoff in cfg.cutoffs if cutoff < len(task.target_dictionary)
-        ]
-
-        config = TransfoXLConfig(
-            vocab_size=len(task.target_dictionary),
-            cutoffs=cutoffs,
-            d_model=cfg.d_model,
-            d_embed=cfg.d_model,
-            n_head=cfg.n_head,
-            d_head=cfg.d_head,
-            d_inner=cfg.d_inner,
-            div_val=cfg.div_val,
-            n_layer=cfg.n_layer,
-            mem_len=cfg.mem_len,
-            clamp_len=cfg.clamp_len,
-            same_length=cfg.same_length,
-            dropout=cfg.dropout,
-            dropatt=cfg.dropatt,
-        )
-        logger.info(config)
-        self.model = TransfoXLLMHeadModel(config)
-
-        # Workaround a bug in huggingface's ``ProjectedAdaptiveLogSoftmax``
-        # which adds ``None`` values to an ``nn.ParameterList``, which is not
-        # supported in PyTorch. Instead we can replace this with an
-        # ``nn.ModuleList``, which does support ``None`` values.
-        try:
-            if all(p is None for p in self.model.crit.out_projs._parameters.values()):
-                self.model.crit.out_projs = torch.nn.ModuleList(
-                    [None] * len(self.model.crit.out_projs._parameters)
-                )
-        except Exception:
-            pass
-
-        if cfg.checkpoint_activations or cfg.offload_activations:
-            for i in range(len(self.model.transformer.layers)):
-                self.model.transformer.layers[i] = checkpoint_wrapper(
-                    self.model.transformer.layers[i],
-                    offload_to_cpu=cfg.offload_activations,
-                )
-                # TODO: may save mem to wrap(layer.pos_ff.CoreNet[3])
-
-        self._mems = None
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths=None,  # unused
-        incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None,
-        encoder_out=None,
-    ):
-        if incremental_state is not None:  # used during inference
-            mems = self.get_incremental_state(incremental_state, "mems")
-            src_tokens = src_tokens[:, -1:]  # only keep the most recent token
-        else:
-            mems = self._mems
-
-        output = self.model(
-            input_ids=src_tokens,
-            mems=mems,
-            return_dict=False,
-        )
-
-        if len(output) >= 2:
-            if incremental_state is not None:
-                self.set_incremental_state(incremental_state, "mems", output[1])
-            else:
-                self._mems = output[1]
-
-        return (output[0],)
-
-    def max_positions(self):
-        return self.cfg.max_target_positions
-
-    def reorder_incremental_state(
-        self,
-        incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]],
-        new_order: torch.Tensor,
-    ):
-        """Reorder incremental state.
-
-        This will be called when the order of the input has changed from the
-        previous time step. A typical use case is beam search, where the input
-        order changes between time steps based on the selection of beams.
-        """
-        mems = self.get_incremental_state(incremental_state, "mems")
-        if mems is not None:
-            new_mems = [mems_i.index_select(1, new_order) for mems_i in mems]
-            self.set_incremental_state(incremental_state, "mems", new_mems)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/file_chunker_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/file_chunker_utils.py
deleted file mode 100644
index 443100c61ab26808d820b7ea2b1307df6475007c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/file_chunker_utils.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-import typing as tp
-
-
-def _safe_readline(fd) -> str:
-    pos = fd.tell()
-    while True:
-        try:
-            return fd.readline()
-        except UnicodeDecodeError:
-            pos -= 1
-            fd.seek(pos)  # search where this character begins
-
-
-def find_offsets(filename: str, num_chunks: int) -> tp.List[int]:
-    """
-    given a file and a number of chuncks, find the offsets in the file
-    to be able to chunk around full lines.
-    """
-    with open(filename, "r", encoding="utf-8") as f:
-        size = os.fstat(f.fileno()).st_size
-        chunk_size = size // num_chunks
-        offsets = [0 for _ in range(num_chunks + 1)]
-        for i in range(1, num_chunks):
-            f.seek(chunk_size * i)
-            _safe_readline(f)
-            offsets[i] = f.tell()
-        offsets[-1] = size
-        return offsets
-
-
-class ChunkLineIterator:
-    """
-    Iterator to properly iterate over lines of a file chunck.
-    """
-
-    def __init__(self, fd, start_offset: int, end_offset: int):
-        self._fd = fd
-        self._start_offset = start_offset
-        self._end_offset = end_offset
-
-    def __iter__(self) -> tp.Iterable[str]:
-        self._fd.seek(self._start_offset)
-        # next(f) breaks f.tell(), hence readline() must be used
-        line = _safe_readline(self._fd)
-        while line:
-            pos = self._fd.tell()
-            # f.tell() does not always give the byte position in the file
-            # sometimes it skips to a very large number
-            # it is unlikely that through a normal read we go from
-            # end bytes to end + 2**32 bytes (4 GB) and this makes it unlikely
-            # that the procedure breaks by the undeterministic behavior of
-            # f.tell()
-            if (
-                self._end_offset > 0
-                and pos > self._end_offset
-                and pos < self._end_offset + 2 ** 32
-            ):
-                break
-            yield line
-            line = self._fd.readline()
-
-
-class Chunker:
-    """
-    contextmanager to read a chunck of a file line by line.
-    """
-
-    def __init__(self, path: str, start_offset: int, end_offset: int):
-        self.path = path
-        self.start_offset = start_offset
-        self.end_offset = end_offset
-
-    def __enter__(self) -> ChunkLineIterator:
-        self.fd = open(self.path, "r", encoding="utf-8")
-        return ChunkLineIterator(self.fd, self.start_offset, self.end_offset)
-
-    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
-        self.fd.close()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/criterions/ASG_loss.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/criterions/ASG_loss.py
deleted file mode 100644
index 41f50bbd70388ce723f2d316d4e9776bcd6be3c9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/criterions/ASG_loss.py
+++ /dev/null
@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from examples.speech_recognition.data.replabels import pack_replabels
-from fairseq import utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-
-
-@register_criterion("asg_loss")
-class ASGCriterion(FairseqCriterion):
-    @staticmethod
-    def add_args(parser):
-        group = parser.add_argument_group("ASG Loss")
-        group.add_argument(
-            "--asg-transitions-init",
-            help="initial diagonal value of transition matrix",
-            type=float,
-            default=0.0,
-        )
-        group.add_argument(
-            "--max-replabel", help="maximum # of replabels", type=int, default=2
-        )
-        group.add_argument(
-            "--linseg-updates",
-            help="# of training updates to use LinSeg initialization",
-            type=int,
-            default=0,
-        )
-        group.add_argument(
-            "--hide-linseg-messages",
-            help="hide messages about LinSeg initialization",
-            action="store_true",
-        )
-
-    def __init__(
-        self,
-        task,
-        silence_token,
-        asg_transitions_init,
-        max_replabel,
-        linseg_updates,
-        hide_linseg_messages,
-    ):
-        from flashlight.lib.sequence.criterion import ASGLoss, CriterionScaleMode
-
-        super().__init__(task)
-        self.tgt_dict = task.target_dictionary
-        self.eos = self.tgt_dict.eos()
-        self.silence = (
-            self.tgt_dict.index(silence_token)
-            if silence_token in self.tgt_dict
-            else None
-        )
-        self.max_replabel = max_replabel
-
-        num_labels = len(self.tgt_dict)
-        self.asg = ASGLoss(num_labels, scale_mode=CriterionScaleMode.TARGET_SZ_SQRT)
-        self.asg.trans = torch.nn.Parameter(
-            asg_transitions_init * torch.eye(num_labels), requires_grad=True
-        )
-
-        self.linseg_progress = torch.nn.Parameter(
-            torch.tensor([0], dtype=torch.int), requires_grad=False
-        )
-        self.linseg_maximum = linseg_updates
-        self.linseg_message_state = "none" if hide_linseg_messages else "start"
-
-    @classmethod
-    def build_criterion(cls, args, task):
-        return cls(
-            task,
-            args.silence_token,
-            args.asg_transitions_init,
-            args.max_replabel,
-            args.linseg_updates,
-            args.hide_linseg_messages,
-        )
-
-    def linseg_step(self):
-        if not self.training:
-            return False
-        if self.linseg_progress.item() < self.linseg_maximum:
-            if self.linseg_message_state == "start":
-                print("| using LinSeg to initialize ASG")
-                self.linseg_message_state = "finish"
-            self.linseg_progress.add_(1)
-            return True
-        elif self.linseg_message_state == "finish":
-            print("| finished LinSeg initialization")
-            self.linseg_message_state = "none"
-        return False
-
-    def replace_eos_with_silence(self, tgt):
-        if tgt[-1] != self.eos:
-            return tgt
-        elif self.silence is None or (len(tgt) > 1 and tgt[-2] == self.silence):
-            return tgt[:-1]
-        else:
-            return tgt[:-1] + [self.silence]
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-
-        net_output = model(**sample["net_input"])
-        emissions = net_output["encoder_out"].transpose(0, 1).contiguous()
-        B = emissions.size(0)
-        T = emissions.size(1)
-        device = emissions.device
-
-        target = torch.IntTensor(B, T)
-        target_size = torch.IntTensor(B)
-        using_linseg = self.linseg_step()
-
-        for b in range(B):
-            initial_target_size = sample["target_lengths"][b].item()
-            if initial_target_size == 0:
-                raise ValueError("target size cannot be zero")
-
-            tgt = sample["target"][b, :initial_target_size].tolist()
-            tgt = self.replace_eos_with_silence(tgt)
-            tgt = pack_replabels(tgt, self.tgt_dict, self.max_replabel)
-            tgt = tgt[:T]
-
-            if using_linseg:
-                tgt = [tgt[t * len(tgt) // T] for t in range(T)]
-
-            target[b][: len(tgt)] = torch.IntTensor(tgt)
-            target_size[b] = len(tgt)
-
-        loss = self.asg.forward(emissions, target.to(device), target_size.to(device))
-
-        if reduce:
-            loss = torch.sum(loss)
-
-        sample_size = (
-            sample["target"].size(0) if self.args.sentence_avg else sample["ntokens"]
-        )
-        logging_output = {
-            "loss": utils.item(loss.data) if reduce else loss.data,
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-        }
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def aggregate_logging_outputs(logging_outputs):
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-        agg_output = {
-            "loss": loss_sum / nsentences,
-            "ntokens": ntokens,
-            "nsentences": nsentences,
-            "sample_size": sample_size,
-        }
-        return agg_output
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/datasets/asr_prep_json.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/datasets/asr_prep_json.py
deleted file mode 100644
index b8db8ff16691158fae034a8ab3faad622b351caf..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/datasets/asr_prep_json.py
+++ /dev/null
@@ -1,125 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import argparse
-import concurrent.futures
-import json
-import multiprocessing
-import os
-from collections import namedtuple
-from itertools import chain
-
-import sentencepiece as spm
-from fairseq.data import Dictionary
-
-
-MILLISECONDS_TO_SECONDS = 0.001
-
-
-def process_sample(aud_path, lable, utt_id, sp, tgt_dict):
-    import torchaudio
-
-    input = {}
-    output = {}
-    si, ei = torchaudio.info(aud_path)
-    input["length_ms"] = int(
-        si.length / si.channels / si.rate / MILLISECONDS_TO_SECONDS
-    )
-    input["path"] = aud_path
-
-    token = " ".join(sp.EncodeAsPieces(lable))
-    ids = tgt_dict.encode_line(token, append_eos=False)
-    output["text"] = lable
-    output["token"] = token
-    output["tokenid"] = ", ".join(map(str, [t.tolist() for t in ids]))
-    return {utt_id: {"input": input, "output": output}}
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--audio-dirs",
-        nargs="+",
-        default=["-"],
-        required=True,
-        help="input directories with audio files",
-    )
-    parser.add_argument(
-        "--labels",
-        required=True,
-        help="aggregated input labels with format <ID LABEL> per line",
-        type=argparse.FileType("r", encoding="UTF-8"),
-    )
-    parser.add_argument(
-        "--spm-model",
-        required=True,
-        help="sentencepiece model to use for encoding",
-        type=argparse.FileType("r", encoding="UTF-8"),
-    )
-    parser.add_argument(
-        "--dictionary",
-        required=True,
-        help="file to load fairseq dictionary from",
-        type=argparse.FileType("r", encoding="UTF-8"),
-    )
-    parser.add_argument("--audio-format", choices=["flac", "wav"], default="wav")
-    parser.add_argument(
-        "--output",
-        required=True,
-        type=argparse.FileType("w"),
-        help="path to save json output",
-    )
-    args = parser.parse_args()
-
-    sp = spm.SentencePieceProcessor()
-    sp.Load(args.spm_model.name)
-
-    tgt_dict = Dictionary.load(args.dictionary)
-
-    labels = {}
-    for line in args.labels:
-        (utt_id, label) = line.split(" ", 1)
-        labels[utt_id] = label
-    if len(labels) == 0:
-        raise Exception("No labels found in ", args.labels_path)
-
-    Sample = namedtuple("Sample", "aud_path utt_id")
-    samples = []
-    for path, _, files in chain.from_iterable(
-        os.walk(path) for path in args.audio_dirs
-    ):
-        for f in files:
-            if f.endswith(args.audio_format):
-                if len(os.path.splitext(f)) != 2:
-                    raise Exception("Expect <utt_id.extension> file name. Got: ", f)
-                utt_id = os.path.splitext(f)[0]
-                if utt_id not in labels:
-                    continue
-                samples.append(Sample(os.path.join(path, f), utt_id))
-
-    utts = {}
-    num_cpu = multiprocessing.cpu_count()
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_cpu) as executor:
-        future_to_sample = {
-            executor.submit(
-                process_sample, s.aud_path, labels[s.utt_id], s.utt_id, sp, tgt_dict
-            ): s
-            for s in samples
-        }
-        for future in concurrent.futures.as_completed(future_to_sample):
-            try:
-                data = future.result()
-            except Exception as exc:
-                print("generated an exception: ", exc)
-            else:
-                utts.update(data)
-    json.dump({"utts": utts}, args.output, indent=4)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/ORI-Muchim/MarinTTS/commons.py b/spaces/ORI-Muchim/MarinTTS/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/MarinTTS/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/OmegaYuti/anything-v3.0/README.md b/spaces/OmegaYuti/anything-v3.0/README.md
deleted file mode 100644
index 15176bed26d36b4f9566c7102a5655e310f76036..0000000000000000000000000000000000000000
--- a/spaces/OmegaYuti/anything-v3.0/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anything V3.0
-emoji: 🏃
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: akhaliq/anything-v3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/losses/ssim.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/losses/ssim.py
deleted file mode 100644
index ee43a0095408eca98e253dea194db788446f9c0a..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/evaluation/losses/ssim.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-class SSIM(torch.nn.Module):
-    """SSIM. Modified from:
-    https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
-    """
-
-    def __init__(self, window_size=11, size_average=True):
-        super().__init__()
-        self.window_size = window_size
-        self.size_average = size_average
-        self.channel = 1
-        self.register_buffer('window', self._create_window(window_size, self.channel))
-
-    def forward(self, img1, img2):
-        assert len(img1.shape) == 4
-
-        channel = img1.size()[1]
-
-        if channel == self.channel and self.window.data.type() == img1.data.type():
-            window = self.window
-        else:
-            window = self._create_window(self.window_size, channel)
-
-            # window = window.to(img1.get_device())
-            window = window.type_as(img1)
-
-            self.window = window
-            self.channel = channel
-
-        return self._ssim(img1, img2, window, self.window_size, channel, self.size_average)
-
-    def _gaussian(self, window_size, sigma):
-        gauss = torch.Tensor([
-            np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)
-        ])
-        return gauss / gauss.sum()
-
-    def _create_window(self, window_size, channel):
-        _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1)
-        _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
-        return _2D_window.expand(channel, 1, window_size, window_size).contiguous()
-
-    def _ssim(self, img1, img2, window, window_size, channel, size_average=True):
-        mu1 = F.conv2d(img1, window, padding=(window_size // 2), groups=channel)
-        mu2 = F.conv2d(img2, window, padding=(window_size // 2), groups=channel)
-
-        mu1_sq = mu1.pow(2)
-        mu2_sq = mu2.pow(2)
-        mu1_mu2 = mu1 * mu2
-
-        sigma1_sq = F.conv2d(
-            img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq
-        sigma2_sq = F.conv2d(
-            img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq
-        sigma12 = F.conv2d(
-            img1 * img2, window, padding=(window_size // 2), groups=channel) - mu1_mu2
-
-        C1 = 0.01 ** 2
-        C2 = 0.03 ** 2
-
-        ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \
-                   ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-
-        if size_average:
-            return ssim_map.mean()
-
-        return ssim_map.mean(1).mean(1).mean(1)
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
-        return
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/multidilated_conv.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/multidilated_conv.py
deleted file mode 100644
index d267ee2aa5eb84b6a9291d0eaaff322c6c2802d0..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/modules/multidilated_conv.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import torch
-import torch.nn as nn
-import random
-from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
-
-class MultidilatedConv(nn.Module):
-    def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True,
-                 shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs):
-        super().__init__()
-        convs = []
-        self.equal_dim = equal_dim
-        assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode
-        if comb_mode in ('cat_out', 'cat_both'):
-            self.cat_out = True
-            if equal_dim:
-                assert out_dim % dilation_num == 0
-                out_dims = [out_dim // dilation_num] * dilation_num
-                self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], [])
-            else:
-                out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
-                out_dims.append(out_dim - sum(out_dims))
-                index = []
-                starts = [0] + out_dims[:-1]
-                lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)]
-                for i in range(out_dims[-1]):
-                    for j in range(dilation_num):
-                        index += list(range(starts[j], starts[j] + lengths[j]))
-                        starts[j] += lengths[j]
-                self.index = index
-                assert(len(index) == out_dim)
-            self.out_dims = out_dims
-        else:
-            self.cat_out = False
-            self.out_dims = [out_dim] * dilation_num
-
-        if comb_mode in ('cat_in', 'cat_both'):
-            if equal_dim:
-                assert in_dim % dilation_num == 0
-                in_dims = [in_dim // dilation_num] * dilation_num
-            else:
-                in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
-                in_dims.append(in_dim - sum(in_dims))
-            self.in_dims = in_dims
-            self.cat_in = True
-        else:
-            self.cat_in = False
-            self.in_dims = [in_dim] * dilation_num
-
-        conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d
-        dilation = min_dilation
-        for i in range(dilation_num):
-            if isinstance(padding, int):
-                cur_padding = padding * dilation
-            else:
-                cur_padding = padding[i]
-            convs.append(conv_type(
-                self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs
-            ))
-            if i > 0 and shared_weights:
-                convs[-1].weight = convs[0].weight
-                convs[-1].bias = convs[0].bias
-            dilation *= 2
-        self.convs = nn.ModuleList(convs)
-
-        self.shuffle_in_channels = shuffle_in_channels
-        if self.shuffle_in_channels:
-            # shuffle list as shuffling of tensors is nondeterministic
-            in_channels_permute = list(range(in_dim))
-            random.shuffle(in_channels_permute)
-            # save as buffer so it is saved and loaded with checkpoint
-            self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute))
-
-    def forward(self, x):
-        if self.shuffle_in_channels:
-            x = x[:, self.in_channels_permute]
-
-        outs = []
-        if self.cat_in:
-            if self.equal_dim:
-                x = x.chunk(len(self.convs), dim=1)
-            else:
-                new_x = []
-                start = 0
-                for dim in self.in_dims:
-                    new_x.append(x[:, start:start+dim])
-                    start += dim
-                x = new_x
-        for i, conv in enumerate(self.convs):
-            if self.cat_in:
-                input = x[i]
-            else:
-                input = x
-            outs.append(conv(input))
-        if self.cat_out:
-            out = torch.cat(outs, dim=1)[:, self.index]
-        else:
-            out = sum(outs)
-        return out
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/__init__.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/__init__.py
deleted file mode 100644
index 7246c897430f0cc7ce12719ad8608824fc734446..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .alexnet import AlexNet
-# yapf: disable
-from .bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS,
-                     PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS,
-                     ContextBlock, Conv2d, Conv3d, ConvAWS2d, ConvModule,
-                     ConvTranspose2d, ConvTranspose3d, ConvWS2d,
-                     DepthwiseSeparableConvModule, GeneralizedAttention,
-                     HSigmoid, HSwish, Linear, MaxPool2d, MaxPool3d,
-                     NonLocal1d, NonLocal2d, NonLocal3d, Scale, Swish,
-                     build_activation_layer, build_conv_layer,
-                     build_norm_layer, build_padding_layer, build_plugin_layer,
-                     build_upsample_layer, conv_ws_2d, is_norm)
-from .builder import MODELS, build_model_from_cfg
-# yapf: enable
-from .resnet import ResNet, make_res_layer
-from .utils import (INITIALIZERS, Caffe2XavierInit, ConstantInit, KaimingInit,
-                    NormalInit, PretrainedInit, TruncNormalInit, UniformInit,
-                    XavierInit, bias_init_with_prob, caffe2_xavier_init,
-                    constant_init, fuse_conv_bn, get_model_complexity_info,
-                    initialize, kaiming_init, normal_init, trunc_normal_init,
-                    uniform_init, xavier_init)
-from .vgg import VGG, make_vgg_layer
-
-__all__ = [
-    'AlexNet', 'VGG', 'make_vgg_layer', 'ResNet', 'make_res_layer',
-    'constant_init', 'xavier_init', 'normal_init', 'trunc_normal_init',
-    'uniform_init', 'kaiming_init', 'caffe2_xavier_init',
-    'bias_init_with_prob', 'ConvModule', 'build_activation_layer',
-    'build_conv_layer', 'build_norm_layer', 'build_padding_layer',
-    'build_upsample_layer', 'build_plugin_layer', 'is_norm', 'NonLocal1d',
-    'NonLocal2d', 'NonLocal3d', 'ContextBlock', 'HSigmoid', 'Swish', 'HSwish',
-    'GeneralizedAttention', 'ACTIVATION_LAYERS', 'CONV_LAYERS', 'NORM_LAYERS',
-    'PADDING_LAYERS', 'UPSAMPLE_LAYERS', 'PLUGIN_LAYERS', 'Scale',
-    'get_model_complexity_info', 'conv_ws_2d', 'ConvAWS2d', 'ConvWS2d',
-    'fuse_conv_bn', 'DepthwiseSeparableConvModule', 'Linear', 'Conv2d',
-    'ConvTranspose2d', 'MaxPool2d', 'ConvTranspose3d', 'MaxPool3d', 'Conv3d',
-    'initialize', 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit',
-    'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
-    'Caffe2XavierInit', 'MODELS', 'build_model_from_cfg'
-]
diff --git a/spaces/Plachta/VALL-E-X/modules/scaling.py b/spaces/Plachta/VALL-E-X/modules/scaling.py
deleted file mode 100644
index 824a2077cedb787dd05bbad5ba6fe65099e11fcf..0000000000000000000000000000000000000000
--- a/spaces/Plachta/VALL-E-X/modules/scaling.py
+++ /dev/null
@@ -1,1401 +0,0 @@
-# Copyright    2022  Xiaomi Corp.        (authors: Daniel Povey)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import collections
-import logging
-import random
-import math
-from functools import reduce
-from itertools import repeat
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-from torch.nn import Embedding as ScaledEmbedding
-
-from utils import Transpose
-
-
-class ActivationBalancerFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        scale_factor: Tensor,
-        sign_factor: Optional[Tensor],
-        channel_dim: int,
-    ) -> Tensor:
-        if channel_dim < 0:
-            channel_dim += x.ndim
-        ctx.channel_dim = channel_dim
-        xgt0 = x > 0
-        if sign_factor is None:
-            ctx.save_for_backward(xgt0, scale_factor)
-        else:
-            ctx.save_for_backward(xgt0, scale_factor, sign_factor)
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
-        if len(ctx.saved_tensors) == 3:
-            xgt0, scale_factor, sign_factor = ctx.saved_tensors
-            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
-                scale_factor = scale_factor.unsqueeze(-1)
-                sign_factor = sign_factor.unsqueeze(-1)
-            factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
-        else:
-            xgt0, scale_factor = ctx.saved_tensors
-            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
-                scale_factor = scale_factor.unsqueeze(-1)
-            factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
-        neg_delta_grad = x_grad.abs() * factor
-        return (
-            x_grad - neg_delta_grad,
-            None,
-            None,
-            None,
-        )
-
-
-def _compute_scale_factor(
-    x: Tensor,
-    channel_dim: int,
-    min_abs: float,
-    max_abs: float,
-    gain_factor: float,
-    max_factor: float,
-) -> Tensor:
-    if channel_dim < 0:
-        channel_dim += x.ndim
-    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
-    x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32)
-
-    if min_abs == 0.0:
-        below_threshold = 0.0
-    else:
-        # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if
-        # x_abs)_mean , min_abs.
-        below_threshold = (
-            (min_abs - x_abs_mean) * (gain_factor / min_abs)
-        ).clamp(min=0, max=max_factor)
-
-    above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp(
-        min=0, max=max_factor
-    )
-
-    return below_threshold - above_threshold
-
-
-def _compute_sign_factor(
-    x: Tensor,
-    channel_dim: int,
-    min_positive: float,
-    max_positive: float,
-    gain_factor: float,
-    max_factor: float,
-) -> Tensor:
-    if channel_dim < 0:
-        channel_dim += x.ndim
-    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
-    proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims)
-    if min_positive == 0.0:
-        factor1 = 0.0
-    else:
-        # 0 if proportion_positive >= min_positive, else can be
-        # as large as max_factor.
-        factor1 = (
-            (min_positive - proportion_positive) * (gain_factor / min_positive)
-        ).clamp_(min=0, max=max_factor)
-
-    if max_positive == 1.0:
-        factor2 = 0.0
-    else:
-        # 0 if self.proportion_positive <= max_positive, else can be
-        # as large as -max_factor.
-        factor2 = (
-            (proportion_positive - max_positive)
-            * (gain_factor / (1.0 - max_positive))
-        ).clamp_(min=0, max=max_factor)
-    sign_factor = factor1 - factor2
-    # require min_positive != 0 or max_positive != 1:
-    assert not isinstance(sign_factor, float)
-    return sign_factor
-
-
-class ActivationScaleBalancerFunction(torch.autograd.Function):
-    """
-    This object is used in class ActivationBalancer when the user specified
-    min_positive=0, max_positive=1, so there are no constraints on the signs
-    of the activations and only the absolute value has a constraint.
-    """
-
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        sign_factor: Tensor,
-        scale_factor: Tensor,
-        channel_dim: int,
-    ) -> Tensor:
-        if channel_dim < 0:
-            channel_dim += x.ndim
-        ctx.channel_dim = channel_dim
-        xgt0 = x > 0
-        ctx.save_for_backward(xgt0, sign_factor, scale_factor)
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
-        xgt0, sign_factor, scale_factor = ctx.saved_tensors
-        for _ in range(ctx.channel_dim, x_grad.ndim - 1):
-            sign_factor = sign_factor.unsqueeze(-1)
-            scale_factor = scale_factor.unsqueeze(-1)
-
-        factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
-        neg_delta_grad = x_grad.abs() * factor
-        return (
-            x_grad - neg_delta_grad,
-            None,
-            None,
-            None,
-        )
-
-
-class RandomClampFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        min: Optional[float],
-        max: Optional[float],
-        prob: float,
-        reflect: float,
-    ) -> Tensor:
-        x_clamped = torch.clamp(x, min=min, max=max)
-        mask = torch.rand_like(x) < prob
-        ans = torch.where(mask, x_clamped, x)
-        if x.requires_grad:
-            ctx.save_for_backward(ans == x)
-            ctx.reflect = reflect
-        if reflect != 0.0:
-            ans = ans * (1.0 + reflect) - (x * reflect)
-        return ans
-
-    @staticmethod
-    def backward(
-        ctx, ans_grad: Tensor
-    ) -> Tuple[Tensor, None, None, None, None]:
-        (is_same,) = ctx.saved_tensors
-        x_grad = ans_grad * is_same.to(ans_grad.dtype)
-        reflect = ctx.reflect
-        if reflect != 0.0:
-            x_grad = x_grad * (1.0 + reflect) - (ans_grad * reflect)
-        return x_grad, None, None, None, None
-
-
-def random_clamp(
-    x: Tensor,
-    min: Optional[float] = None,
-    max: Optional[float] = None,
-    prob: float = 0.5,
-    reflect: float = 0.0,
-):
-    return RandomClampFunction.apply(x, min, max, prob, reflect)
-
-
-def random_cast_to_half(x: Tensor, min_abs: float = 5.0e-06) -> Tensor:
-    """
-    A randomized way of casting a floating point value to half precision.
-    """
-    if x.dtype == torch.float16:
-        return x
-    x_abs = x.abs()
-    is_too_small = x_abs < min_abs
-    # for elements where is_too_small is true, random_val will contain +-min_abs with
-    # probability (x.abs() / min_abs), and 0.0 otherwise.  [so this preserves expectations,
-    # for those elements].
-    random_val = min_abs * x.sign() * (torch.rand_like(x) * min_abs < x_abs)
-    return torch.where(is_too_small, random_val, x).to(torch.float16)
-
-
-class RandomGradFunction(torch.autograd.Function):
-    """
-    Does nothing in forward pass; in backward pass, gets rid of very small grads using
-    randomized approach that preserves expectations (intended to reduce roundoff).
-    """
-
-    @staticmethod
-    def forward(ctx, x: Tensor, min_abs: float) -> Tensor:
-        ctx.min_abs = min_abs
-        return x
-
-    @staticmethod
-    def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None]:
-        if ans_grad.dtype == torch.float16:
-            return (
-                random_cast_to_half(
-                    ans_grad.to(torch.float32), min_abs=ctx.min_abs
-                ),
-                None,
-            )
-        else:
-            return ans_grad, None
-
-
-class RandomGrad(torch.nn.Module):
-    """
-    Gets rid of very small gradients using an expectation-preserving method, intended to increase
-    accuracy of training when using amp (automatic mixed precision)
-    """
-
-    def __init__(self, min_abs: float = 5.0e-06):
-        super(RandomGrad, self).__init__()
-        self.min_abs = min_abs
-
-    def forward(self, x: Tensor):
-        if (
-            torch.jit.is_scripting()
-            or not self.training
-            or torch.jit.is_tracing()
-        ):
-            return x
-        else:
-            return RandomGradFunction.apply(x, self.min_abs)
-
-
-class SoftmaxFunction(torch.autograd.Function):
-    """
-    Tries to handle half-precision derivatives in a randomized way that should
-    be more accurate for training than the default behavior.
-    """
-
-    @staticmethod
-    def forward(ctx, x: Tensor, dim: int):
-        ans = x.softmax(dim=dim)
-        # if x dtype is float16, x.softmax() returns a float32 because
-        # (presumably) that op does not support float16, and autocast
-        # is enabled.
-        if torch.is_autocast_enabled():
-            ans = ans.to(torch.float16)
-        ctx.save_for_backward(ans)
-        ctx.x_dtype = x.dtype
-        ctx.dim = dim
-        return ans
-
-    @staticmethod
-    def backward(ctx, ans_grad: Tensor):
-        (ans,) = ctx.saved_tensors
-        with torch.cuda.amp.autocast(enabled=False):
-            ans_grad = ans_grad.to(torch.float32)
-            ans = ans.to(torch.float32)
-            x_grad = ans_grad * ans
-            x_grad = x_grad - ans * x_grad.sum(dim=ctx.dim, keepdim=True)
-            return x_grad, None
-
-
-def softmax(x: Tensor, dim: int):
-    if torch.jit.is_scripting() or torch.jit.is_tracing():
-        return x.softmax(dim)
-
-    return SoftmaxFunction.apply(x, dim)
-
-
-class MaxEigLimiterFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        coeffs: Tensor,
-        direction: Tensor,
-        channel_dim: int,
-        grad_scale: float,
-    ) -> Tensor:
-        ctx.channel_dim = channel_dim
-        ctx.grad_scale = grad_scale
-        ctx.save_for_backward(x.detach(), coeffs.detach(), direction.detach())
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad, *args):
-        with torch.enable_grad():
-            (x_orig, coeffs, new_direction) = ctx.saved_tensors
-            x_orig.requires_grad = True
-            num_channels = x_orig.shape[ctx.channel_dim]
-            x = x_orig.transpose(ctx.channel_dim, -1).reshape(-1, num_channels)
-            new_direction.requires_grad = False
-            x = x - x.mean(dim=0)
-            x_var = (x ** 2).mean()
-            x_residual = x - coeffs * new_direction
-            x_residual_var = (x_residual ** 2).mean()
-            # `variance_proportion` is the proportion of the variance accounted for
-            # by the top eigen-direction.  This is to be minimized.
-            variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20)
-            variance_proportion.backward()
-        x_orig_grad = x_orig.grad
-        x_extra_grad = (
-            x_orig.grad
-            * ctx.grad_scale
-            * x_grad.norm()
-            / (x_orig_grad.norm() + 1.0e-20)
-        )
-        return x_grad + x_extra_grad.detach(), None, None, None, None
-
-
-class BasicNorm(torch.nn.Module):
-    """
-    This is intended to be a simpler, and hopefully cheaper, replacement for
-    LayerNorm.  The observation this is based on, is that Transformer-type
-    networks, especially with pre-norm, sometimes seem to set one of the
-    feature dimensions to a large constant value (e.g. 50), which "defeats"
-    the LayerNorm because the output magnitude is then not strongly dependent
-    on the other (useful) features.  Presumably the weight and bias of the
-    LayerNorm are required to allow it to do this.
-
-    So the idea is to introduce this large constant value as an explicit
-    parameter, that takes the role of the "eps" in LayerNorm, so the network
-    doesn't have to do this trick.  We make the "eps" learnable.
-
-    Args:
-       num_channels: the number of channels, e.g. 512.
-      channel_dim: the axis/dimension corresponding to the channel,
-        interprted as an offset from the input's ndim if negative.
-        shis is NOT the num_channels; it should typically be one of
-        {-2, -1, 0, 1, 2, 3}.
-       eps: the initial "epsilon" that we add as ballast in:
-             scale = ((input_vec**2).mean() + epsilon)**-0.5
-          Note: our epsilon is actually large, but we keep the name
-          to indicate the connection with conventional LayerNorm.
-       learn_eps: if true, we learn epsilon; if false, we keep it
-         at the initial value.
-    eps_min: float
-    eps_max: float
-    """
-
-    def __init__(
-        self,
-        num_channels: int,
-        channel_dim: int = -1,  # CAUTION: see documentation.
-        eps: float = 0.25,
-        learn_eps: bool = True,
-        eps_min: float = -3.0,
-        eps_max: float = 3.0,
-    ) -> None:
-        super(BasicNorm, self).__init__()
-        self.num_channels = num_channels
-        self.channel_dim = channel_dim
-        if learn_eps:
-            self.eps = nn.Parameter(torch.tensor(eps).log().detach())
-        else:
-            self.register_buffer("eps", torch.tensor(eps).log().detach())
-        self.eps_min = eps_min
-        self.eps_max = eps_max
-
-    def forward(self, x: Tensor) -> Tensor:
-        assert x.shape[self.channel_dim] == self.num_channels
-        eps = self.eps
-        if self.training and random.random() < 0.25:
-            # with probability 0.25, in training mode, clamp eps between the min
-            # and max; this will encourage it to learn parameters within the
-            # allowed range by making parameters that are outside the allowed
-            # range noisy.
-
-            # gradients to allow the parameter to get back into the allowed
-            # region if it happens to exit it.
-            eps = eps.clamp(min=self.eps_min, max=self.eps_max)
-        scales = (
-            torch.mean(x ** 2, dim=self.channel_dim, keepdim=True) + eps.exp()
-        ) ** -0.5
-        return x * scales
-
-
-def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
-    """
-    Behaves like a constructor of a modified version of nn.Linear
-    that gives an easy way to set the default initial parameter scale.
-
-    Args:
-        Accepts the standard args and kwargs that nn.Linear accepts
-        e.g. in_features, out_features, bias=False.
-
-        initial_scale: you can override this if you want to increase
-           or decrease the initial magnitude of the module's output
-           (affects the initialization of weight_scale and bias_scale).
-           Another option, if you want to do something like this, is
-           to re-initialize the parameters.
-    """
-    ans = nn.Linear(*args, **kwargs)
-    with torch.no_grad():
-        ans.weight[:] *= initial_scale
-        if ans.bias is not None:
-            torch.nn.init.uniform_(
-                ans.bias, -0.1 * initial_scale, 0.1 * initial_scale
-            )
-    return ans
-
-
-def ScaledConv1d(
-    *args,
-    initial_scale: float = 1.0,
-    kernel_size: int = 3,
-    padding: str = "same",
-    **kwargs,
-) -> nn.Conv1d:
-    """
-    Behaves like a constructor of a modified version of nn.Conv1d
-    that gives an easy way to set the default initial parameter scale.
-
-    Args:
-        Accepts the standard args and kwargs that nn.Linear accepts
-        e.g. in_features, out_features, bias=False.
-
-        initial_scale: you can override this if you want to increase
-           or decrease the initial magnitude of the module's output
-           (affects the initialization of weight_scale and bias_scale).
-           Another option, if you want to do something like this, is
-           to re-initialize the parameters.
-    """
-    ans = nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs)
-    with torch.no_grad():
-        ans.weight[:] *= initial_scale
-        if ans.bias is not None:
-            torch.nn.init.uniform_(
-                ans.bias, -0.1 * initial_scale, 0.1 * initial_scale
-            )
-    return ans
-
-
-def TransposeScaledConv1d(
-    *args,
-    initial_scale: float = 1.0,
-    kernel_size: int = 3,
-    padding: str = "same",
-    **kwargs,
-) -> nn.Sequential:
-    """
-    Transpose -> ScaledConv1d
-    """
-    return nn.Sequential(
-        Transpose(),
-        ScaledConv1d(
-            *args,
-            initial_scale=initial_scale,
-            kernel_size=kernel_size,
-            padding=padding,
-            **kwargs,
-        ),
-    )
-
-
-def ScaledConv1dTranspose(
-    *args,
-    initial_scale: float = 1.0,
-    kernel_size: int = 3,
-    padding: str = "same",
-    **kwargs,
-) -> nn.Sequential:
-    """
-    Transpose -> ScaledConv1d
-    """
-    return nn.Sequential(
-        ScaledConv1d(
-            *args,
-            initial_scale=initial_scale,
-            kernel_size=kernel_size,
-            padding=padding,
-            **kwargs,
-        ),
-        Transpose(),
-    )
-
-
-def TransposeConv1d(
-    *args, kernel_size: int = 3, padding: str = "same", **kwargs
-) -> nn.Sequential:
-    """
-    Transpose -> Conv1d
-    """
-    return nn.Sequential(
-        Transpose(),
-        nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
-    )
-
-
-def Conv1dTranspose(
-    *args, kernel_size: int = 3, padding: str = "same", **kwargs
-) -> nn.Sequential:
-    """
-    ScaledConv1d -> Transpose
-    """
-    return nn.Sequential(
-        nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
-        Transpose(),
-    )
-
-
-class SRLinear(nn.Linear):
-    """https://arxiv.org/abs/2303.06296
-    Stabilizing Transformer Training by Preventing Attention Entropy Collapse
-    """
-
-    def __init__(self, in_features, out_features, bias=True, **kwargs):
-        super().__init__(in_features, out_features, bias=bias, **kwargs)
-        self.register_buffer(
-            "u", nn.functional.normalize(torch.randn(in_features), dim=0)
-        )
-        with torch.no_grad():
-            sigma = self.get_sigma()
-        self.register_buffer("spectral_norm", sigma)
-        self.sigma = nn.Parameter(torch.ones(1))
-
-    def get_sigma(self):
-        with torch.no_grad():
-            u = self.u
-            v = self.weight.mv(u)
-            v = nn.functional.normalize(v, dim=0)
-            u = self.weight.T.mv(v)
-            u = nn.functional.normalize(u, dim=0)
-            self.u.data.copy_(u)
-        return torch.einsum("c,cd,d->", v, self.weight, u)
-
-    def get_weight(self):
-        sigma = self.get_sigma()
-        if self.training:
-            self.spectral_norm.data.copy_(sigma)
-        weight = (self.sigma / sigma) * self.weight
-        return weight
-
-    def forward(self, x):
-        return nn.functional.linear(x, self.get_weight(), self.bias)
-
-
-class SRConv1d(SRLinear):
-    def __init__(
-        self,
-        in_features,
-        out_features,
-        kernel_size,
-        stride: int = 1,
-        padding: str = "same",
-        bias: bool = True,
-        **kwargs,
-    ):
-        in_features = in_features * kernel_size
-        super().__init__(in_features, out_features, bias=bias, **kwargs)
-        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-
-    def forward(self, x):
-        in_features = self.in_features // self.kernel_size
-        weight = self.get_weight().view(
-            self.out_features, in_features, self.kernel_size
-        )
-        return nn.functional.conv1d(
-            x, weight, bias=self.bias, stride=self.stride, padding=self.padding
-        )
-
-
-def TransposeSRConv1d(
-    *args, kernel_size: int = 3, padding: str = "same", **kwargs
-) -> nn.Sequential:
-    """
-    Transpose -> SRConv1d
-    """
-    return nn.Sequential(
-        Transpose(),
-        SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
-    )
-
-
-def SRConv1dTranspose(
-    *args, kernel_size: int = 3, padding: str = "same", **kwargs
-) -> nn.Sequential:
-    """
-    SRConv1d -> Transpose
-    """
-    return nn.Sequential(
-        SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
-        Transpose(),
-    )
-
-
-class ActivationBalancer(torch.nn.Module):
-    """
-    Modifies the backpropped derivatives of a function to try to encourage, for
-    each channel, that it is positive at least a proportion `threshold` of the
-    time.  It does this by multiplying negative derivative values by up to
-    (1+max_factor), and positive derivative values by up to (1-max_factor),
-    interpolated from 1 at the threshold to those extremal values when none
-    of the inputs are positive.
-
-    Args:
-           num_channels: the number of channels
-           channel_dim: the dimension/axis corresponding to the channel, e.g.
-               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
-           min_positive: the minimum, per channel, of the proportion of the time
-               that (x > 0), below which we start to modify the derivatives.
-           max_positive: the maximum, per channel, of the proportion of the time
-               that (x > 0), above which we start to modify the derivatives.
-           max_factor: the maximum factor by which we modify the derivatives for
-              either the sign constraint or the magnitude constraint;
-              e.g. with max_factor=0.02, the the derivatives would be multiplied by
-              values in the range [0.98..1.02].
-           sign_gain_factor: determines the 'gain' with which we increase the
-              change in gradient once the constraints on min_positive and max_positive
-              are violated.
-           scale_gain_factor: determines the 'gain' with which we increase the
-              change in gradient once the constraints on min_abs and max_abs
-              are violated.
-           min_abs:  the minimum average-absolute-value difference from the mean
-               value per channel, which we allow, before we start to modify
-               the derivatives to prevent this.
-           max_abs:  the maximum average-absolute-value difference from the mean
-               value per channel, which we allow, before we start to modify
-               the derivatives to prevent this.
-          min_prob: determines the minimum probability with which we modify the
-             gradients for the {min,max}_positive and {min,max}_abs constraints,
-             on each forward().  This is done randomly to prevent all layers
-             from doing it at the same time.  Early in training we may use
-             higher probabilities than this; it will decay to this value.
-    """
-
-    def __init__(
-        self,
-        num_channels: int,
-        channel_dim: int,
-        min_positive: float = 0.05,
-        max_positive: float = 0.95,
-        max_factor: float = 0.04,
-        sign_gain_factor: float = 0.01,
-        scale_gain_factor: float = 0.02,
-        min_abs: float = 0.2,
-        max_abs: float = 100.0,
-        min_prob: float = 0.1,
-    ):
-        super(ActivationBalancer, self).__init__()
-        self.num_channels = num_channels
-        self.channel_dim = channel_dim
-        self.min_positive = min_positive
-        self.max_positive = max_positive
-        self.max_factor = max_factor
-        self.min_abs = min_abs
-        self.max_abs = max_abs
-        self.min_prob = min_prob
-        self.sign_gain_factor = sign_gain_factor
-        self.scale_gain_factor = scale_gain_factor
-
-        # count measures how many times the forward() function has been called.
-        # We occasionally sync this to a tensor called `count`, that exists to
-        # make sure it is synced to disk when we load and save the model.
-        self.cpu_count = 0
-        self.register_buffer("count", torch.tensor(0, dtype=torch.int64))
-
-    def forward(self, x: Tensor) -> Tensor:
-        if (
-            torch.jit.is_scripting()
-            or not x.requires_grad
-            or torch.jit.is_tracing()
-        ):
-            return _no_op(x)
-
-        count = self.cpu_count
-        self.cpu_count += 1
-
-        if random.random() < 0.01:
-            # Occasionally sync self.cpu_count with self.count.
-            # count affects the decay of 'prob'.  don't do this on every iter,
-            # because syncing with the GPU is slow.
-            self.cpu_count = max(self.cpu_count, self.count.item())
-            self.count.fill_(self.cpu_count)
-
-        # the prob of doing some work exponentially decreases from 0.5 till it hits
-        # a floor at min_prob (==0.1, by default)
-        prob = max(self.min_prob, 0.5 ** (1 + (count / 4000.0)))
-
-        if random.random() < prob:
-            sign_gain_factor = 0.5
-            if self.min_positive != 0.0 or self.max_positive != 1.0:
-                sign_factor = _compute_sign_factor(
-                    x,
-                    self.channel_dim,
-                    self.min_positive,
-                    self.max_positive,
-                    gain_factor=self.sign_gain_factor / prob,
-                    max_factor=self.max_factor,
-                )
-            else:
-                sign_factor = None
-
-            scale_factor = _compute_scale_factor(
-                x.detach(),
-                self.channel_dim,
-                min_abs=self.min_abs,
-                max_abs=self.max_abs,
-                gain_factor=self.scale_gain_factor / prob,
-                max_factor=self.max_factor,
-            )
-            return ActivationBalancerFunction.apply(
-                x,
-                scale_factor,
-                sign_factor,
-                self.channel_dim,
-            )
-        else:
-            return _no_op(x)
-
-
-def penalize_abs_values_gt(x: Tensor, limit: float, penalty: float) -> Tensor:
-    """
-    Returns x unmodified, but in backprop will put a penalty for the excess of
-    the absolute values of elements of x over the limit "limit".  E.g. if
-    limit == 10.0, then if x has any values over 10 it will get a penalty.
-
-    Caution: the value of this penalty will be affected by grad scaling used
-    in automatic mixed precision training.  For this reasons we use this,
-    it shouldn't really matter, or may even be helpful; we just use this
-    to disallow really implausible values of scores to be given to softmax.
-    """
-    x_sign = x.sign()
-    over_limit = (x.abs() - limit) > 0
-    # The following is a memory efficient way to penalize the absolute values of
-    # x that's over the limit.  (The memory efficiency comes when you think
-    # about which items torch needs to cache for the autograd, and which ones it
-    # can throw away).  The numerical value of aux_loss as computed here will
-    # actually be larger than it should be, by limit * over_limit.sum(), but it
-    # has the same derivative as the real aux_loss which is penalty * (x.abs() -
-    # limit).relu().
-    aux_loss = penalty * ((x_sign * over_limit).to(torch.int8) * x)
-    # note: we don't do sum() here on aux)_loss, but it's as if we had done
-    # sum() due to how with_loss() works.
-    x = with_loss(x, aux_loss)
-    # you must use x for something, or this will be ineffective.
-    return x
-
-
-def _diag(x: Tensor):  # like .diag(), but works for tensors with 3 dims.
-    if x.ndim == 2:
-        return x.diag()
-    else:
-        (batch, dim, dim) = x.shape
-        x = x.reshape(batch, dim * dim)
-        x = x[:, :: dim + 1]
-        assert x.shape == (batch, dim)
-        return x
-
-
-def _whitening_metric(x: Tensor, num_groups: int):
-    """
-    Computes the "whitening metric", a value which will be 1.0 if all the eigenvalues of
-    of the centered feature covariance are the same within each group's covariance matrix
-    and also between groups.
-    Args:
-        x: a Tensor of shape (*, num_channels)
-     num_groups:  the number of groups of channels, a number >=1 that divides num_channels
-    Returns:
-        Returns a scalar Tensor that will be 1.0 if the data is "perfectly white" and
-    greater than 1.0 otherwise.
-    """
-    assert x.dtype != torch.float16
-    x = x.reshape(-1, x.shape[-1])
-    (num_frames, num_channels) = x.shape
-    assert num_channels % num_groups == 0
-    channels_per_group = num_channels // num_groups
-    x = x.reshape(num_frames, num_groups, channels_per_group).transpose(0, 1)
-    # x now has shape (num_groups, num_frames, channels_per_group)
-    # subtract the mean so we use the centered, not uncentered, covariance.
-    # My experience has been that when we "mess with the gradients" like this,
-    # it's better not do anything that tries to move the mean around, because
-    # that can easily cause instability.
-    x = x - x.mean(dim=1, keepdim=True)
-    # x_covar: (num_groups, channels_per_group, channels_per_group)
-    x_covar = torch.matmul(x.transpose(1, 2), x)
-    x_covar_mean_diag = _diag(x_covar).mean()
-    # the following expression is what we'd get if we took the matrix product
-    # of each covariance and measured the mean of its trace, i.e.
-    # the same as _diag(torch.matmul(x_covar, x_covar)).mean().
-    x_covarsq_mean_diag = (x_covar ** 2).sum() / (
-        num_groups * channels_per_group
-    )
-    # this metric will be >= 1.0; the larger it is, the less 'white' the data was.
-    metric = x_covarsq_mean_diag / (x_covar_mean_diag ** 2 + 1.0e-20)
-    return metric
-
-
-class WhiteningPenaltyFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        num_groups: int,
-        whitening_limit: float,
-        grad_scale: float,
-    ) -> Tensor:
-        ctx.save_for_backward(x)
-        ctx.num_groups = num_groups
-        ctx.whitening_limit = whitening_limit
-        ctx.grad_scale = grad_scale
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad: Tensor):
-        (x_orig,) = ctx.saved_tensors
-        with torch.enable_grad():
-            with torch.cuda.amp.autocast(enabled=False):
-                x_detached = x_orig.to(torch.float32).detach()
-                x_detached.requires_grad = True
-
-                metric = _whitening_metric(x_detached, ctx.num_groups)
-
-                if random.random() < 0.005 or __name__ == "__main__":
-                    logging.info(
-                        f"Whitening: num_groups={ctx.num_groups}, num_channels={x_orig.shape[-1]}, "
-                        f"metric={metric.item():.2f} vs. limit={ctx.whitening_limit}"
-                    )
-
-                (metric - ctx.whitening_limit).relu().backward()
-                penalty_grad = x_detached.grad
-                scale = ctx.grad_scale * (
-                    x_grad.to(torch.float32).norm()
-                    / (penalty_grad.norm() + 1.0e-20)
-                )
-                penalty_grad = penalty_grad * scale
-        return x_grad + penalty_grad.to(x_grad.dtype), None, None, None
-
-
-class Whiten(nn.Module):
-    def __init__(
-        self,
-        num_groups: int,
-        whitening_limit: float,
-        prob: Union[float, Tuple[float, float]],
-        grad_scale: float,
-    ):
-        """
-        Args:
-          num_groups: the number of groups to divide the channel dim into before
-            whitening.  We will attempt to make the feature covariance
-            within each group, after mean subtraction, as "white" as possible,
-            while having the same trace across all groups.
-         whitening_limit: a value greater than 1.0, that dictates how much
-           freedom we have to violate the constraints.  1.0 would mean perfectly
-           white, with exactly the same trace across groups; larger values
-           give more freedom.  E.g. 2.0.
-         prob: the probability with which we apply the gradient modification
-           (also affects the grad scale).  May be supplied as a float,
-           or as a pair (min_prob, max_prob)
-
-          grad_scale: determines the scale on the gradient term from this object,
-            relative to the rest of the gradient on the attention weights.
-            E.g. 0.02 (you may want to use smaller values than this if prob is large)
-        """
-        super(Whiten, self).__init__()
-        assert num_groups >= 1
-        assert whitening_limit >= 1
-        assert grad_scale >= 0
-        self.num_groups = num_groups
-        self.whitening_limit = whitening_limit
-        if isinstance(prob, float):
-            assert 0 < prob <= 1
-            self.prob = prob
-        else:
-            (self.min_prob, self.max_prob) = prob
-            assert 0 < self.min_prob < self.max_prob <= 1
-            self.prob = self.max_prob
-
-        self.grad_scale = grad_scale
-
-    def forward(self, x: Tensor) -> Tensor:
-        """
-        In the forward pass, this function just returns the input unmodified.
-        In the backward pass, it will modify the gradients to ensure that the
-        distribution in each group has close to (lambda times I) as the covariance
-        after mean subtraction, with the same lambda across groups.
-        For whitening_limit > 1, there will be more freedom to violate this
-        constraint.
-
-        Args:
-           x: the input of shape (*, num_channels)
-
-        Returns:
-            x, unmodified.   You should make sure
-        you use the returned value, or the graph will be freed
-        and nothing will happen in backprop.
-        """
-        if (
-            not x.requires_grad
-            or random.random() > self.prob
-            or self.grad_scale == 0
-        ):
-            return _no_op(x)
-        else:
-            if hasattr(self, "min_prob") and random.random() < 0.25:
-                # occasionally switch between min_prob and max_prob, based on whether
-                # we are above or below the threshold.
-                if (
-                    _whitening_metric(x.to(torch.float32), self.num_groups)
-                    > self.whitening_limit
-                ):
-                    # there would be a change to the grad.
-                    self.prob = self.max_prob
-                else:
-                    self.prob = self.min_prob
-
-            return WhiteningPenaltyFunction.apply(
-                x, self.num_groups, self.whitening_limit, self.grad_scale
-            )
-
-
-class WithLoss(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x: Tensor, y: Tensor):
-        ctx.y_shape = y.shape
-        return x
-
-    @staticmethod
-    def backward(ctx, ans_grad: Tensor):
-        return ans_grad, torch.ones(
-            ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device
-        )
-
-
-def with_loss(x, y):
-    if torch.jit.is_scripting() or torch.jit.is_tracing():
-        return x
-    # returns x but adds y.sum() to the loss function.
-    return WithLoss.apply(x, y)
-
-
-def _no_op(x: Tensor) -> Tensor:
-    if torch.jit.is_scripting() or torch.jit.is_tracing():
-        return x
-    else:
-        # a no-op function that will have a node in the autograd graph,
-        # to avoid certain bugs relating to backward hooks
-        return x.chunk(1, dim=-1)[0]
-
-
-class Identity(torch.nn.Module):
-    def __init__(self):
-        super(Identity, self).__init__()
-
-    def forward(self, x):
-        return _no_op(x)
-
-
-class MaxEig(torch.nn.Module):
-    """
-    Modifies the backpropped derivatives of a function to try to discourage
-    that any given direction in activation space accounts for more than
-    a specified proportion of the covariance (e.g. 0.2).
-
-
-    Args:
-           num_channels: the number of channels
-           channel_dim: the dimension/axis corresponding to the channel, e.g.
-               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
-           max_var_per_eig:  the maximum proportion of the variance of the
-               features/channels, after mean subtraction, that can come from
-               any given eigenvalue.
-           min_prob: the minimum probability with which we apply this during any invocation
-               of forward(), assuming last time we applied the constraint it was
-               not active; supplied for speed.
-           scale: determines the scale with which we modify the gradients, relative
-               to the existing / unmodified gradients
-    """
-
-    def __init__(
-        self,
-        num_channels: int,
-        channel_dim: int,
-        max_var_per_eig: float = 0.2,
-        min_prob: float = 0.01,
-        scale: float = 0.01,
-    ):
-        super(MaxEig, self).__init__()
-        self.num_channels = num_channels
-        self.channel_dim = channel_dim
-        self.scale = scale
-        assert max_var_per_eig == 0.0 or max_var_per_eig > 1.0 / num_channels
-        self.max_var_per_eig = max_var_per_eig
-
-        # we figure out the dominant direction using the power method: starting with
-        # a random vector, keep multiplying by the covariance and renormalizing.
-        with torch.no_grad():
-            # arbitrary.. would use randn() but want to leave the rest of the model's
-            # random parameters unchanged for comparison
-            direction = torch.arange(num_channels).to(torch.float)
-            direction = direction / direction.norm()
-            self.register_buffer("max_eig_direction", direction)
-
-        self.min_prob = min_prob
-        # cur_prob is the current probability we'll use to apply the ActivationBalancer.
-        # We'll regress this towards prob, each tiem we try to apply it and it is not
-        # active.
-        self.cur_prob = 1.0
-
-    def forward(self, x: Tensor) -> Tensor:
-        if (
-            torch.jit.is_scripting()
-            or self.max_var_per_eig <= 0
-            or random.random() > self.cur_prob
-            or torch.jit.is_tracing()
-        ):
-            return _no_op(x)
-
-        with torch.cuda.amp.autocast(enabled=False):
-            eps = 1.0e-20
-            orig_x = x
-            x = x.to(torch.float32)
-            with torch.no_grad():
-                x = x.transpose(self.channel_dim, -1).reshape(
-                    -1, self.num_channels
-                )
-                x = x - x.mean(dim=0)
-                new_direction, coeffs = self._find_direction_coeffs(
-                    x, self.max_eig_direction
-                )
-                x_var = (x ** 2).mean()
-                x_residual = x - coeffs * new_direction
-                x_residual_var = (x_residual ** 2).mean()
-
-                # `variance_proportion` is the proportion of the variance accounted for
-                # by the top eigen-direction.
-                variance_proportion = (x_var - x_residual_var) / (
-                    x_var + 1.0e-20
-                )
-
-                # ensure new direction is nonzero even if x == 0, by including `direction`.
-                self._set_direction(
-                    0.1 * self.max_eig_direction + new_direction
-                )
-
-            if random.random() < 0.01 or __name__ == "__main__":
-                logging.info(
-                    f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}"
-                )
-
-            if variance_proportion >= self.max_var_per_eig:
-                # The constraint is active.  Note, we should quite rarely
-                # reach here, only near the beginning of training if we are
-                # starting to diverge, should this constraint be active.
-                cur_prob = self.cur_prob
-                self.cur_prob = (
-                    1.0  # next time, do the update with probability 1.0.
-                )
-                return MaxEigLimiterFunction.apply(
-                    orig_x, coeffs, new_direction, self.channel_dim, self.scale
-                )
-            else:
-                # let self.cur_prob exponentially approach self.min_prob, as
-                # long as the constraint is inactive.
-                self.cur_prob = 0.75 * self.cur_prob + 0.25 * self.min_prob
-                return orig_x
-
-    def _set_direction(self, direction: Tensor):
-        """
-        Sets self.max_eig_direction to a normalized version of `direction`
-        """
-        direction = direction.detach()
-        direction = direction / direction.norm()
-        direction_sum = direction.sum().item()
-        if direction_sum - direction_sum == 0:  # no inf/nan
-            self.max_eig_direction[:] = direction
-        else:
-            logging.info(
-                f"Warning: sum of direction in MaxEig is {direction_sum}, "
-                "num_channels={self.num_channels}, channel_dim={self.channel_dim}"
-            )
-
-    def _find_direction_coeffs(
-        self, x: Tensor, prev_direction: Tensor
-    ) -> Tuple[Tensor, Tensor, Tensor]:
-        """
-            Figure out (an approximation to) the proportion of the variance of a set of
-            feature vectors that can be attributed to the top eigen-direction.
-            Args:
-             x: a Tensor of shape (num_frames, num_channels), with num_frames > 1.
-          prev_direction:  a Tensor of shape (num_channels,), that is our previous estimate
-                   of the top eigen-direction, or a random direction if this is the first
-                   iteration.  Does not have to be normalized, but should be nonzero.
-
-        Returns: (cur_direction, coeffs), where:
-             cur_direction: a Tensor of shape (num_channels,) that is the current
-                estimate of the top eigen-direction.
-             coeffs: a Tensor of shape (num_frames, 1) that minimizes, or
-                approximately minimizes, (x - coeffs * cur_direction).norm()
-        """
-        (num_frames, num_channels) = x.shape
-        assert num_channels > 1 and num_frames > 1
-        assert prev_direction.shape == (num_channels,)
-        # `coeffs` are the coefficients of `prev_direction` in x.
-        # actually represent the coeffs up to a constant positive factor.
-        coeffs = (x * prev_direction).sum(dim=1, keepdim=True) + 1.0e-10
-        cur_direction = (x * coeffs).sum(dim=0) / (
-            (coeffs ** 2).sum() + 1.0e-20
-        )
-        return cur_direction, coeffs
-
-
-class DoubleSwishFunction(torch.autograd.Function):
-    """
-      double_swish(x) = x * torch.sigmoid(x-1)
-    This is a definition, originally motivated by its close numerical
-    similarity to swish(swish(x)), where swish(x) =  x * sigmoid(x).
-
-    Memory-efficient derivative computation:
-     double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
-     double_swish'(x) = d/dx double_swish(x) =  x * s'(x) + x' * s(x) = x * s'(x) + s(x).
-     Now, s'(x) = s(x) * (1-s(x)).
-     double_swish'(x) =  x * s'(x) + s(x).
-                      =  x * s(x) * (1-s(x)) + s(x).
-                     = double_swish(x) * (1-s(x)) + s(x)
-     ... so we just need to remember s(x) but not x itself.
-    """
-
-    @staticmethod
-    def forward(ctx, x: Tensor) -> Tensor:
-        requires_grad = x.requires_grad
-        x_dtype = x.dtype
-        if x.dtype == torch.float16:
-            x = x.to(torch.float32)
-
-        s = torch.sigmoid(x - 1.0)
-        y = x * s
-
-        if requires_grad:
-            deriv = y * (1 - s) + s
-            # notes on derivative of x * sigmoid(x - 1):
-            # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29
-            # min \simeq -0.043638.  Take floor as -0.043637 so it's a lower bund
-            # max \simeq 1.1990.   Take ceil to be 1.2 so it's an upper bound.
-            # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which
-            # floors), should be expectation-preserving.
-            floor = -0.043637
-            ceil = 1.2
-            d_scaled = (deriv - floor) * (
-                255.0 / (ceil - floor)
-            ) + torch.rand_like(deriv)
-            if __name__ == "__main__":
-                # for self-testing only.
-                assert d_scaled.min() >= 0.0
-                assert d_scaled.max() < 256.0
-            d_int = d_scaled.to(torch.uint8)
-            ctx.save_for_backward(d_int)
-        if x.dtype == torch.float16 or torch.is_autocast_enabled():
-            y = y.to(torch.float16)
-        return y
-
-    @staticmethod
-    def backward(ctx, y_grad: Tensor) -> Tensor:
-        (d,) = ctx.saved_tensors
-        # the same constants as used in forward pass.
-        floor = -0.043637
-        ceil = 1.2
-        d = d * ((ceil - floor) / 255.0) + floor
-        return y_grad * d
-
-
-class DoubleSwish(torch.nn.Module):
-    def forward(self, x: Tensor) -> Tensor:
-        """Return double-swish activation function which is an approximation to Swish(Swish(x)),
-        that we approximate closely with x * sigmoid(x-1).
-        """
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            return x * torch.sigmoid(x - 1.0)
-        return DoubleSwishFunction.apply(x)
-
-
-def BalancedDoubleSwish(
-    d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25
-) -> nn.Sequential:
-    """
-    ActivationBalancer -> DoubleSwish
-    """
-    balancer = ActivationBalancer(
-        d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob
-    )
-    return nn.Sequential(
-        balancer,
-        DoubleSwish(),
-    )
-
-
-def _test_max_eig():
-    for proportion in [0.1, 0.5, 10.0]:
-        logging.info(f"proportion = {proportion}")
-        x = torch.randn(100, 128)
-        direction = torch.randn(128)
-        coeffs = torch.randn(100, 1)
-        x += proportion * direction * coeffs
-
-        x.requires_grad = True
-
-        num_channels = 128
-        m = MaxEig(
-            num_channels, 1, 0.5, scale=0.1  # channel_dim  # max_var_per_eig
-        )  # grad_scale
-
-        for _ in range(4):
-            y = m(x)
-
-        y_grad = torch.randn_like(x)
-        y.backward(gradient=y_grad)
-
-        if proportion < 0.2:
-            assert torch.allclose(x.grad, y_grad, atol=1.0e-02)
-        elif proportion > 1.0:
-            assert not torch.allclose(x.grad, y_grad)
-
-
-def _test_whiten():
-    for proportion in [0.1, 0.5, 10.0]:
-        logging.info(f"_test_whiten(): proportion = {proportion}")
-        x = torch.randn(100, 128)
-        direction = torch.randn(128)
-        coeffs = torch.randn(100, 1)
-        x += proportion * direction * coeffs
-
-        x.requires_grad = True
-
-        num_channels = 128
-        m = Whiten(
-            1, 5.0, prob=1.0, grad_scale=0.1  # num_groups  # whitening_limit,
-        )  # grad_scale
-
-        for _ in range(4):
-            y = m(x)
-
-        y_grad = torch.randn_like(x)
-        y.backward(gradient=y_grad)
-
-        if proportion < 0.2:
-            assert torch.allclose(x.grad, y_grad)
-        elif proportion > 1.0:
-            assert not torch.allclose(x.grad, y_grad)
-
-
-def _test_activation_balancer_sign():
-    probs = torch.arange(0, 1, 0.01)
-    N = 1000
-    x = 1.0 * (
-        (2.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))) - 1.0
-    )
-    x = x.detach()
-    x.requires_grad = True
-    m = ActivationBalancer(
-        probs.numel(),
-        channel_dim=0,
-        min_positive=0.05,
-        max_positive=0.95,
-        max_factor=0.2,
-        min_abs=0.0,
-    )
-
-    y_grad = torch.sign(torch.randn(probs.numel(), N))
-
-    y = m(x)
-    y.backward(gradient=y_grad)
-    print("_test_activation_balancer_sign: x = ", x)
-    print("_test_activation_balancer_sign: y grad = ", y_grad)
-    print("_test_activation_balancer_sign: x grad = ", x.grad)
-
-
-def _test_activation_balancer_magnitude():
-    magnitudes = torch.arange(0, 1, 0.01)
-    N = 1000
-    x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze(
-        -1
-    )
-    x = x.detach()
-    x.requires_grad = True
-    m = ActivationBalancer(
-        magnitudes.numel(),
-        channel_dim=0,
-        min_positive=0.0,
-        max_positive=1.0,
-        max_factor=0.2,
-        min_abs=0.2,
-        max_abs=0.8,
-        min_prob=1.0,
-    )
-
-    y_grad = torch.sign(torch.randn(magnitudes.numel(), N))
-
-    y = m(x)
-    y.backward(gradient=y_grad)
-    print("_test_activation_balancer_magnitude: x = ", x)
-    print("_test_activation_balancer_magnitude: y grad = ", y_grad)
-    print("_test_activation_balancer_magnitude: x grad = ", x.grad)
-
-
-def _test_basic_norm():
-    num_channels = 128
-    m = BasicNorm(num_channels=num_channels, channel_dim=1)
-
-    x = torch.randn(500, num_channels)
-
-    y = m(x)
-
-    assert y.shape == x.shape
-    x_rms = (x ** 2).mean().sqrt()
-    y_rms = (y ** 2).mean().sqrt()
-    print("x rms = ", x_rms)
-    print("y rms = ", y_rms)
-    assert y_rms < x_rms
-    assert y_rms > 0.5 * x_rms
-
-
-def _test_double_swish_deriv():
-    x = torch.randn(10, 12, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    m = DoubleSwish()
-
-    tol = (1.2 - (-0.043637)) / 255.0
-    torch.autograd.gradcheck(m, x, atol=tol)
-
-    # for self-test.
-    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    y = m(x)
-
-
-def _test_softmax():
-    a = torch.randn(2, 10, dtype=torch.float64)
-    b = a.clone()
-    a.requires_grad = True
-    b.requires_grad = True
-    a.softmax(dim=1)[:, 0].sum().backward()
-    print("a grad = ", a.grad)
-    softmax(b, dim=1)[:, 0].sum().backward()
-    print("b grad = ", b.grad)
-    assert torch.allclose(a.grad, b.grad)
-
-
-if __name__ == "__main__":
-    logging.getLogger().setLevel(logging.INFO)
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-    _test_softmax()
-    _test_whiten()
-    _test_max_eig()
-    _test_activation_balancer_sign()
-    _test_activation_balancer_magnitude()
-    _test_basic_norm()
-    _test_double_swish_deriv()
diff --git a/spaces/Plurigrid/LifeSim/Dockerfile b/spaces/Plurigrid/LifeSim/Dockerfile
deleted file mode 100644
index 91319be9b3dd35d916d18fba5260f51125c46b50..0000000000000000000000000000000000000000
--- a/spaces/Plurigrid/LifeSim/Dockerfile
+++ /dev/null
@@ -1,65 +0,0 @@
-FROM node:18-alpine AS base
-
-# Install dependencies only when needed
-FROM base AS deps
-# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
-RUN apk add --no-cache libc6-compat
-WORKDIR /app
-
-# Install dependencies based on the preferred package manager
-COPY package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
-RUN \
-  if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
-  elif [ -f package-lock.json ]; then npm ci; \
-  elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
-  else echo "Lockfile not found." && exit 1; \
-  fi
-
-# Uncomment the following lines if you want to use a secret at buildtime, 
-# for example to access your private npm packages
-# RUN --mount=type=secret,id=HF_EXAMPLE_SECRET,mode=0444,required=true \
-#     $(cat /run/secrets/HF_EXAMPLE_SECRET)
-
-# Rebuild the source code only when needed
-FROM base AS builder
-WORKDIR /app
-COPY --from=deps /app/node_modules ./node_modules
-COPY . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-# RUN yarn build
-
-# If you use yarn, comment out this line and use the line above
-RUN npm run build
-
-# Production image, copy all the files and run next
-FROM base AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN addgroup --system --gid 1001 nodejs
-RUN adduser --system --uid 1001 nextjs
-
-COPY --from=builder /app/public ./public
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
-COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
-COPY --from=builder --chown=nextjs:nodejs /app/.next/cache ./.next/cache
-# COPY --from=builder --chown=nextjs:nodejs /app/.next/cache/fetch-cache ./.next/cache/fetch-cache
-
-USER nextjs
-
-EXPOSE 3000
-
-ENV PORT 3000
-
-CMD ["node", "server.js"]
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/audiogen/__init__.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/audiogen/__init__.py
deleted file mode 100644
index 8a0a2688450ce120088b79c3314a2f267394dc11..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/audiogen/__init__.py
+++ /dev/null
@@ -1,6 +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.
-"""AudioGen grids."""
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/compression/encodec_musicgen_32khz.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/compression/encodec_musicgen_32khz.py
deleted file mode 100644
index 9da31daa5f009f46e753601a51a06391594b8f9b..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/compression/encodec_musicgen_32khz.py
+++ /dev/null
@@ -1,34 +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.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid shows how to train a MusicGen EnCodec model at 32 kHz.
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=8, partition=partitions)
-    # use configuration for MusicGen's EnCodec model trained on monophonic audio sampled at 32 kHz
-    # MusicGen's EnCodec is trained with a total stride of 640 leading to a frame rate of 50 hz
-    launcher.bind_(solver='compression/encodec_musicgen_32khz')
-    # replace this by the desired music dataset
-    launcher.bind_(dset='internal/music_400k_32khz')
-    # launch xp
-    launcher()
-    launcher({
-        'metrics.visqol.bin': '/data/home/jadecopet/local/usr/opt/visqol',
-        'label': 'visqol',
-        'evaluate.metrics.visqol': True
-    })
diff --git a/spaces/Qiukai/gpt/crazy_functions/test_project/cpp/cppipc/buffer.cpp b/spaces/Qiukai/gpt/crazy_functions/test_project/cpp/cppipc/buffer.cpp
deleted file mode 100644
index 0ac0fa7bc3ced0447ba4caa359355dd4252670b3..0000000000000000000000000000000000000000
--- a/spaces/Qiukai/gpt/crazy_functions/test_project/cpp/cppipc/buffer.cpp
+++ /dev/null
@@ -1,87 +0,0 @@
-#include "libipc/buffer.h"
-#include "libipc/utility/pimpl.h"
-
-#include <cstring>
-
-namespace ipc {
-
-bool operator==(buffer const & b1, buffer const & b2) {
-    return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);
-}
-
-bool operator!=(buffer const & b1, buffer const & b2) {
-    return !(b1 == b2);
-}
-
-class buffer::buffer_ : public pimpl<buffer_> {
-public:
-    void*       p_;
-    std::size_t s_;
-    void*       a_;
-    buffer::destructor_t d_;
-
-    buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)
-        : p_(p), s_(s), a_(a), d_(d) {
-    }
-
-    ~buffer_() {
-        if (d_ == nullptr) return;
-        d_((a_ == nullptr) ? p_ : a_, s_);
-    }
-};
-
-buffer::buffer()
-    : buffer(nullptr, 0, nullptr, nullptr) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d)
-    : p_(p_->make(p, s, d, nullptr)) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)
-    : p_(p_->make(p, s, d, additional)) {
-}
-
-buffer::buffer(void* p, std::size_t s)
-    : buffer(p, s, nullptr) {
-}
-
-buffer::buffer(char const & c)
-    : buffer(const_cast<char*>(&c), 1) {
-}
-
-buffer::buffer(buffer&& rhs)
-    : buffer() {
-    swap(rhs);
-}
-
-buffer::~buffer() {
-    p_->clear();
-}
-
-void buffer::swap(buffer& rhs) {
-    std::swap(p_, rhs.p_);
-}
-
-buffer& buffer::operator=(buffer rhs) {
-    swap(rhs);
-    return *this;
-}
-
-bool buffer::empty() const noexcept {
-    return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);
-}
-
-void* buffer::data() noexcept {
-    return impl(p_)->p_;
-}
-
-void const * buffer::data() const noexcept {
-    return impl(p_)->p_;
-}
-
-std::size_t buffer::size() const noexcept {
-    return impl(p_)->s_;
-}
-
-} // namespace ipc
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/euctwfreq.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/euctwfreq.py
deleted file mode 100644
index 4900ccc160a1dbf4de3a01c234735c21dd4417d6..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/chardet/euctwfreq.py
+++ /dev/null
@@ -1,388 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Communicator client code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-# EUCTW frequency table
-# Converted from big5 work
-# by Taiwan's Mandarin Promotion Council
-# <http:#www.edu.tw:81/mandr/>
-
-# 128  --> 0.42261
-# 256  --> 0.57851
-# 512  --> 0.74851
-# 1024 --> 0.89384
-# 2048 --> 0.97583
-#
-# Idea Distribution Ratio = 0.74851/(1-0.74851) =2.98
-# Random Distribution Ration = 512/(5401-512)=0.105
-#
-# Typical Distribution Ratio about 25% of Ideal one, still much higher than RDR
-
-EUCTW_TYPICAL_DISTRIBUTION_RATIO = 0.75
-
-# Char to FreqOrder table
-EUCTW_TABLE_SIZE = 5376
-
-# fmt: off
-EUCTW_CHAR_TO_FREQ_ORDER = (
-    1, 1800, 1506, 255, 1431, 198, 9, 82, 6, 7310, 177, 202, 3615, 1256, 2808, 110,  # 2742
-    3735, 33, 3241, 261, 76, 44, 2113, 16, 2931, 2184, 1176, 659, 3868, 26, 3404, 2643,  # 2758
-    1198, 3869, 3313, 4060, 410, 2211, 302, 590, 361, 1963, 8, 204, 58, 4296, 7311, 1931,  # 2774
-    63, 7312, 7313, 317, 1614, 75, 222, 159, 4061, 2412, 1480, 7314, 3500, 3068, 224, 2809,  # 2790
-    3616, 3, 10, 3870, 1471, 29, 2774, 1135, 2852, 1939, 873, 130, 3242, 1123, 312, 7315,  # 2806
-    4297, 2051, 507, 252, 682, 7316, 142, 1914, 124, 206, 2932, 34, 3501, 3173, 64, 604,  # 2822
-    7317, 2494, 1976, 1977, 155, 1990, 645, 641, 1606, 7318, 3405, 337, 72, 406, 7319, 80,  # 2838
-    630, 238, 3174, 1509, 263, 939, 1092, 2644, 756, 1440, 1094, 3406, 449, 69, 2969, 591,  # 2854
-    179, 2095, 471, 115, 2034, 1843, 60, 50, 2970, 134, 806, 1868, 734, 2035, 3407, 180,  # 2870
-    995, 1607, 156, 537, 2893, 688, 7320, 319, 1305, 779, 2144, 514, 2374, 298, 4298, 359,  # 2886
-    2495, 90, 2707, 1338, 663, 11, 906, 1099, 2545, 20, 2436, 182, 532, 1716, 7321, 732,  # 2902
-    1376, 4062, 1311, 1420, 3175, 25, 2312, 1056, 113, 399, 382, 1949, 242, 3408, 2467, 529,  # 2918
-    3243, 475, 1447, 3617, 7322, 117, 21, 656, 810, 1297, 2295, 2329, 3502, 7323, 126, 4063,  # 2934
-    706, 456, 150, 613, 4299, 71, 1118, 2036, 4064, 145, 3069, 85, 835, 486, 2114, 1246,  # 2950
-    1426, 428, 727, 1285, 1015, 800, 106, 623, 303, 1281, 7324, 2127, 2354, 347, 3736, 221,  # 2966
-    3503, 3110, 7325, 1955, 1153, 4065, 83, 296, 1199, 3070, 192, 624, 93, 7326, 822, 1897,  # 2982
-    2810, 3111, 795, 2064, 991, 1554, 1542, 1592, 27, 43, 2853, 859, 139, 1456, 860, 4300,  # 2998
-    437, 712, 3871, 164, 2392, 3112, 695, 211, 3017, 2096, 195, 3872, 1608, 3504, 3505, 3618,  # 3014
-    3873, 234, 811, 2971, 2097, 3874, 2229, 1441, 3506, 1615, 2375, 668, 2076, 1638, 305, 228,  # 3030
-    1664, 4301, 467, 415, 7327, 262, 2098, 1593, 239, 108, 300, 200, 1033, 512, 1247, 2077,  # 3046
-    7328, 7329, 2173, 3176, 3619, 2673, 593, 845, 1062, 3244, 88, 1723, 2037, 3875, 1950, 212,  # 3062
-    266, 152, 149, 468, 1898, 4066, 4302, 77, 187, 7330, 3018, 37, 5, 2972, 7331, 3876,  # 3078
-    7332, 7333, 39, 2517, 4303, 2894, 3177, 2078, 55, 148, 74, 4304, 545, 483, 1474, 1029,  # 3094
-    1665, 217, 1869, 1531, 3113, 1104, 2645, 4067, 24, 172, 3507, 900, 3877, 3508, 3509, 4305,  # 3110
-    32, 1408, 2811, 1312, 329, 487, 2355, 2247, 2708, 784, 2674, 4, 3019, 3314, 1427, 1788,  # 3126
-    188, 109, 499, 7334, 3620, 1717, 1789, 888, 1217, 3020, 4306, 7335, 3510, 7336, 3315, 1520,  # 3142
-    3621, 3878, 196, 1034, 775, 7337, 7338, 929, 1815, 249, 439, 38, 7339, 1063, 7340, 794,  # 3158
-    3879, 1435, 2296, 46, 178, 3245, 2065, 7341, 2376, 7342, 214, 1709, 4307, 804, 35, 707,  # 3174
-    324, 3622, 1601, 2546, 140, 459, 4068, 7343, 7344, 1365, 839, 272, 978, 2257, 2572, 3409,  # 3190
-    2128, 1363, 3623, 1423, 697, 100, 3071, 48, 70, 1231, 495, 3114, 2193, 7345, 1294, 7346,  # 3206
-    2079, 462, 586, 1042, 3246, 853, 256, 988, 185, 2377, 3410, 1698, 434, 1084, 7347, 3411,  # 3222
-    314, 2615, 2775, 4308, 2330, 2331, 569, 2280, 637, 1816, 2518, 757, 1162, 1878, 1616, 3412,  # 3238
-    287, 1577, 2115, 768, 4309, 1671, 2854, 3511, 2519, 1321, 3737, 909, 2413, 7348, 4069, 933,  # 3254
-    3738, 7349, 2052, 2356, 1222, 4310, 765, 2414, 1322, 786, 4311, 7350, 1919, 1462, 1677, 2895,  # 3270
-    1699, 7351, 4312, 1424, 2437, 3115, 3624, 2590, 3316, 1774, 1940, 3413, 3880, 4070, 309, 1369,  # 3286
-    1130, 2812, 364, 2230, 1653, 1299, 3881, 3512, 3882, 3883, 2646, 525, 1085, 3021, 902, 2000,  # 3302
-    1475, 964, 4313, 421, 1844, 1415, 1057, 2281, 940, 1364, 3116, 376, 4314, 4315, 1381, 7,  # 3318
-    2520, 983, 2378, 336, 1710, 2675, 1845, 321, 3414, 559, 1131, 3022, 2742, 1808, 1132, 1313,  # 3334
-    265, 1481, 1857, 7352, 352, 1203, 2813, 3247, 167, 1089, 420, 2814, 776, 792, 1724, 3513,  # 3350
-    4071, 2438, 3248, 7353, 4072, 7354, 446, 229, 333, 2743, 901, 3739, 1200, 1557, 4316, 2647,  # 3366
-    1920, 395, 2744, 2676, 3740, 4073, 1835, 125, 916, 3178, 2616, 4317, 7355, 7356, 3741, 7357,  # 3382
-    7358, 7359, 4318, 3117, 3625, 1133, 2547, 1757, 3415, 1510, 2313, 1409, 3514, 7360, 2145, 438,  # 3398
-    2591, 2896, 2379, 3317, 1068, 958, 3023, 461, 311, 2855, 2677, 4074, 1915, 3179, 4075, 1978,  # 3414
-    383, 750, 2745, 2617, 4076, 274, 539, 385, 1278, 1442, 7361, 1154, 1964, 384, 561, 210,  # 3430
-    98, 1295, 2548, 3515, 7362, 1711, 2415, 1482, 3416, 3884, 2897, 1257, 129, 7363, 3742, 642,  # 3446
-    523, 2776, 2777, 2648, 7364, 141, 2231, 1333, 68, 176, 441, 876, 907, 4077, 603, 2592,  # 3462
-    710, 171, 3417, 404, 549, 18, 3118, 2393, 1410, 3626, 1666, 7365, 3516, 4319, 2898, 4320,  # 3478
-    7366, 2973, 368, 7367, 146, 366, 99, 871, 3627, 1543, 748, 807, 1586, 1185, 22, 2258,  # 3494
-    379, 3743, 3180, 7368, 3181, 505, 1941, 2618, 1991, 1382, 2314, 7369, 380, 2357, 218, 702,  # 3510
-    1817, 1248, 3418, 3024, 3517, 3318, 3249, 7370, 2974, 3628, 930, 3250, 3744, 7371, 59, 7372,  # 3526
-    585, 601, 4078, 497, 3419, 1112, 1314, 4321, 1801, 7373, 1223, 1472, 2174, 7374, 749, 1836,  # 3542
-    690, 1899, 3745, 1772, 3885, 1476, 429, 1043, 1790, 2232, 2116, 917, 4079, 447, 1086, 1629,  # 3558
-    7375, 556, 7376, 7377, 2020, 1654, 844, 1090, 105, 550, 966, 1758, 2815, 1008, 1782, 686,  # 3574
-    1095, 7378, 2282, 793, 1602, 7379, 3518, 2593, 4322, 4080, 2933, 2297, 4323, 3746, 980, 2496,  # 3590
-    544, 353, 527, 4324, 908, 2678, 2899, 7380, 381, 2619, 1942, 1348, 7381, 1341, 1252, 560,  # 3606
-    3072, 7382, 3420, 2856, 7383, 2053, 973, 886, 2080, 143, 4325, 7384, 7385, 157, 3886, 496,  # 3622
-    4081, 57, 840, 540, 2038, 4326, 4327, 3421, 2117, 1445, 970, 2259, 1748, 1965, 2081, 4082,  # 3638
-    3119, 1234, 1775, 3251, 2816, 3629, 773, 1206, 2129, 1066, 2039, 1326, 3887, 1738, 1725, 4083,  # 3654
-    279, 3120, 51, 1544, 2594, 423, 1578, 2130, 2066, 173, 4328, 1879, 7386, 7387, 1583, 264,  # 3670
-    610, 3630, 4329, 2439, 280, 154, 7388, 7389, 7390, 1739, 338, 1282, 3073, 693, 2857, 1411,  # 3686
-    1074, 3747, 2440, 7391, 4330, 7392, 7393, 1240, 952, 2394, 7394, 2900, 1538, 2679, 685, 1483,  # 3702
-    4084, 2468, 1436, 953, 4085, 2054, 4331, 671, 2395, 79, 4086, 2441, 3252, 608, 567, 2680,  # 3718
-    3422, 4087, 4088, 1691, 393, 1261, 1791, 2396, 7395, 4332, 7396, 7397, 7398, 7399, 1383, 1672,  # 3734
-    3748, 3182, 1464, 522, 1119, 661, 1150, 216, 675, 4333, 3888, 1432, 3519, 609, 4334, 2681,  # 3750
-    2397, 7400, 7401, 7402, 4089, 3025, 0, 7403, 2469, 315, 231, 2442, 301, 3319, 4335, 2380,  # 3766
-    7404, 233, 4090, 3631, 1818, 4336, 4337, 7405, 96, 1776, 1315, 2082, 7406, 257, 7407, 1809,  # 3782
-    3632, 2709, 1139, 1819, 4091, 2021, 1124, 2163, 2778, 1777, 2649, 7408, 3074, 363, 1655, 3183,  # 3798
-    7409, 2975, 7410, 7411, 7412, 3889, 1567, 3890, 718, 103, 3184, 849, 1443, 341, 3320, 2934,  # 3814
-    1484, 7413, 1712, 127, 67, 339, 4092, 2398, 679, 1412, 821, 7414, 7415, 834, 738, 351,  # 3830
-    2976, 2146, 846, 235, 1497, 1880, 418, 1992, 3749, 2710, 186, 1100, 2147, 2746, 3520, 1545,  # 3846
-    1355, 2935, 2858, 1377, 583, 3891, 4093, 2573, 2977, 7416, 1298, 3633, 1078, 2549, 3634, 2358,  # 3862
-    78, 3750, 3751, 267, 1289, 2099, 2001, 1594, 4094, 348, 369, 1274, 2194, 2175, 1837, 4338,  # 3878
-    1820, 2817, 3635, 2747, 2283, 2002, 4339, 2936, 2748, 144, 3321, 882, 4340, 3892, 2749, 3423,  # 3894
-    4341, 2901, 7417, 4095, 1726, 320, 7418, 3893, 3026, 788, 2978, 7419, 2818, 1773, 1327, 2859,  # 3910
-    3894, 2819, 7420, 1306, 4342, 2003, 1700, 3752, 3521, 2359, 2650, 787, 2022, 506, 824, 3636,  # 3926
-    534, 323, 4343, 1044, 3322, 2023, 1900, 946, 3424, 7421, 1778, 1500, 1678, 7422, 1881, 4344,  # 3942
-    165, 243, 4345, 3637, 2521, 123, 683, 4096, 764, 4346, 36, 3895, 1792, 589, 2902, 816,  # 3958
-    626, 1667, 3027, 2233, 1639, 1555, 1622, 3753, 3896, 7423, 3897, 2860, 1370, 1228, 1932, 891,  # 3974
-    2083, 2903, 304, 4097, 7424, 292, 2979, 2711, 3522, 691, 2100, 4098, 1115, 4347, 118, 662,  # 3990
-    7425, 611, 1156, 854, 2381, 1316, 2861, 2, 386, 515, 2904, 7426, 7427, 3253, 868, 2234,  # 4006
-    1486, 855, 2651, 785, 2212, 3028, 7428, 1040, 3185, 3523, 7429, 3121, 448, 7430, 1525, 7431,  # 4022
-    2164, 4348, 7432, 3754, 7433, 4099, 2820, 3524, 3122, 503, 818, 3898, 3123, 1568, 814, 676,  # 4038
-    1444, 306, 1749, 7434, 3755, 1416, 1030, 197, 1428, 805, 2821, 1501, 4349, 7435, 7436, 7437,  # 4054
-    1993, 7438, 4350, 7439, 7440, 2195, 13, 2779, 3638, 2980, 3124, 1229, 1916, 7441, 3756, 2131,  # 4070
-    7442, 4100, 4351, 2399, 3525, 7443, 2213, 1511, 1727, 1120, 7444, 7445, 646, 3757, 2443, 307,  # 4086
-    7446, 7447, 1595, 3186, 7448, 7449, 7450, 3639, 1113, 1356, 3899, 1465, 2522, 2523, 7451, 519,  # 4102
-    7452, 128, 2132, 92, 2284, 1979, 7453, 3900, 1512, 342, 3125, 2196, 7454, 2780, 2214, 1980,  # 4118
-    3323, 7455, 290, 1656, 1317, 789, 827, 2360, 7456, 3758, 4352, 562, 581, 3901, 7457, 401,  # 4134
-    4353, 2248, 94, 4354, 1399, 2781, 7458, 1463, 2024, 4355, 3187, 1943, 7459, 828, 1105, 4101,  # 4150
-    1262, 1394, 7460, 4102, 605, 4356, 7461, 1783, 2862, 7462, 2822, 819, 2101, 578, 2197, 2937,  # 4166
-    7463, 1502, 436, 3254, 4103, 3255, 2823, 3902, 2905, 3425, 3426, 7464, 2712, 2315, 7465, 7466,  # 4182
-    2332, 2067, 23, 4357, 193, 826, 3759, 2102, 699, 1630, 4104, 3075, 390, 1793, 1064, 3526,  # 4198
-    7467, 1579, 3076, 3077, 1400, 7468, 4105, 1838, 1640, 2863, 7469, 4358, 4359, 137, 4106, 598,  # 4214
-    3078, 1966, 780, 104, 974, 2938, 7470, 278, 899, 253, 402, 572, 504, 493, 1339, 7471,  # 4230
-    3903, 1275, 4360, 2574, 2550, 7472, 3640, 3029, 3079, 2249, 565, 1334, 2713, 863, 41, 7473,  # 4246
-    7474, 4361, 7475, 1657, 2333, 19, 463, 2750, 4107, 606, 7476, 2981, 3256, 1087, 2084, 1323,  # 4262
-    2652, 2982, 7477, 1631, 1623, 1750, 4108, 2682, 7478, 2864, 791, 2714, 2653, 2334, 232, 2416,  # 4278
-    7479, 2983, 1498, 7480, 2654, 2620, 755, 1366, 3641, 3257, 3126, 2025, 1609, 119, 1917, 3427,  # 4294
-    862, 1026, 4109, 7481, 3904, 3760, 4362, 3905, 4363, 2260, 1951, 2470, 7482, 1125, 817, 4110,  # 4310
-    4111, 3906, 1513, 1766, 2040, 1487, 4112, 3030, 3258, 2824, 3761, 3127, 7483, 7484, 1507, 7485,  # 4326
-    2683, 733, 40, 1632, 1106, 2865, 345, 4113, 841, 2524, 230, 4364, 2984, 1846, 3259, 3428,  # 4342
-    7486, 1263, 986, 3429, 7487, 735, 879, 254, 1137, 857, 622, 1300, 1180, 1388, 1562, 3907,  # 4358
-    3908, 2939, 967, 2751, 2655, 1349, 592, 2133, 1692, 3324, 2985, 1994, 4114, 1679, 3909, 1901,  # 4374
-    2185, 7488, 739, 3642, 2715, 1296, 1290, 7489, 4115, 2198, 2199, 1921, 1563, 2595, 2551, 1870,  # 4390
-    2752, 2986, 7490, 435, 7491, 343, 1108, 596, 17, 1751, 4365, 2235, 3430, 3643, 7492, 4366,  # 4406
-    294, 3527, 2940, 1693, 477, 979, 281, 2041, 3528, 643, 2042, 3644, 2621, 2782, 2261, 1031,  # 4422
-    2335, 2134, 2298, 3529, 4367, 367, 1249, 2552, 7493, 3530, 7494, 4368, 1283, 3325, 2004, 240,  # 4438
-    1762, 3326, 4369, 4370, 836, 1069, 3128, 474, 7495, 2148, 2525, 268, 3531, 7496, 3188, 1521,  # 4454
-    1284, 7497, 1658, 1546, 4116, 7498, 3532, 3533, 7499, 4117, 3327, 2684, 1685, 4118, 961, 1673,  # 4470
-    2622, 190, 2005, 2200, 3762, 4371, 4372, 7500, 570, 2497, 3645, 1490, 7501, 4373, 2623, 3260,  # 4486
-    1956, 4374, 584, 1514, 396, 1045, 1944, 7502, 4375, 1967, 2444, 7503, 7504, 4376, 3910, 619,  # 4502
-    7505, 3129, 3261, 215, 2006, 2783, 2553, 3189, 4377, 3190, 4378, 763, 4119, 3763, 4379, 7506,  # 4518
-    7507, 1957, 1767, 2941, 3328, 3646, 1174, 452, 1477, 4380, 3329, 3130, 7508, 2825, 1253, 2382,  # 4534
-    2186, 1091, 2285, 4120, 492, 7509, 638, 1169, 1824, 2135, 1752, 3911, 648, 926, 1021, 1324,  # 4550
-    4381, 520, 4382, 997, 847, 1007, 892, 4383, 3764, 2262, 1871, 3647, 7510, 2400, 1784, 4384,  # 4566
-    1952, 2942, 3080, 3191, 1728, 4121, 2043, 3648, 4385, 2007, 1701, 3131, 1551, 30, 2263, 4122,  # 4582
-    7511, 2026, 4386, 3534, 7512, 501, 7513, 4123, 594, 3431, 2165, 1821, 3535, 3432, 3536, 3192,  # 4598
-    829, 2826, 4124, 7514, 1680, 3132, 1225, 4125, 7515, 3262, 4387, 4126, 3133, 2336, 7516, 4388,  # 4614
-    4127, 7517, 3912, 3913, 7518, 1847, 2383, 2596, 3330, 7519, 4389, 374, 3914, 652, 4128, 4129,  # 4630
-    375, 1140, 798, 7520, 7521, 7522, 2361, 4390, 2264, 546, 1659, 138, 3031, 2445, 4391, 7523,  # 4646
-    2250, 612, 1848, 910, 796, 3765, 1740, 1371, 825, 3766, 3767, 7524, 2906, 2554, 7525, 692,  # 4662
-    444, 3032, 2624, 801, 4392, 4130, 7526, 1491, 244, 1053, 3033, 4131, 4132, 340, 7527, 3915,  # 4678
-    1041, 2987, 293, 1168, 87, 1357, 7528, 1539, 959, 7529, 2236, 721, 694, 4133, 3768, 219,  # 4694
-    1478, 644, 1417, 3331, 2656, 1413, 1401, 1335, 1389, 3916, 7530, 7531, 2988, 2362, 3134, 1825,  # 4710
-    730, 1515, 184, 2827, 66, 4393, 7532, 1660, 2943, 246, 3332, 378, 1457, 226, 3433, 975,  # 4726
-    3917, 2944, 1264, 3537, 674, 696, 7533, 163, 7534, 1141, 2417, 2166, 713, 3538, 3333, 4394,  # 4742
-    3918, 7535, 7536, 1186, 15, 7537, 1079, 1070, 7538, 1522, 3193, 3539, 276, 1050, 2716, 758,  # 4758
-    1126, 653, 2945, 3263, 7539, 2337, 889, 3540, 3919, 3081, 2989, 903, 1250, 4395, 3920, 3434,  # 4774
-    3541, 1342, 1681, 1718, 766, 3264, 286, 89, 2946, 3649, 7540, 1713, 7541, 2597, 3334, 2990,  # 4790
-    7542, 2947, 2215, 3194, 2866, 7543, 4396, 2498, 2526, 181, 387, 1075, 3921, 731, 2187, 3335,  # 4806
-    7544, 3265, 310, 313, 3435, 2299, 770, 4134, 54, 3034, 189, 4397, 3082, 3769, 3922, 7545,  # 4822
-    1230, 1617, 1849, 355, 3542, 4135, 4398, 3336, 111, 4136, 3650, 1350, 3135, 3436, 3035, 4137,  # 4838
-    2149, 3266, 3543, 7546, 2784, 3923, 3924, 2991, 722, 2008, 7547, 1071, 247, 1207, 2338, 2471,  # 4854
-    1378, 4399, 2009, 864, 1437, 1214, 4400, 373, 3770, 1142, 2216, 667, 4401, 442, 2753, 2555,  # 4870
-    3771, 3925, 1968, 4138, 3267, 1839, 837, 170, 1107, 934, 1336, 1882, 7548, 7549, 2118, 4139,  # 4886
-    2828, 743, 1569, 7550, 4402, 4140, 582, 2384, 1418, 3437, 7551, 1802, 7552, 357, 1395, 1729,  # 4902
-    3651, 3268, 2418, 1564, 2237, 7553, 3083, 3772, 1633, 4403, 1114, 2085, 4141, 1532, 7554, 482,  # 4918
-    2446, 4404, 7555, 7556, 1492, 833, 1466, 7557, 2717, 3544, 1641, 2829, 7558, 1526, 1272, 3652,  # 4934
-    4142, 1686, 1794, 416, 2556, 1902, 1953, 1803, 7559, 3773, 2785, 3774, 1159, 2316, 7560, 2867,  # 4950
-    4405, 1610, 1584, 3036, 2419, 2754, 443, 3269, 1163, 3136, 7561, 7562, 3926, 7563, 4143, 2499,  # 4966
-    3037, 4406, 3927, 3137, 2103, 1647, 3545, 2010, 1872, 4144, 7564, 4145, 431, 3438, 7565, 250,  # 4982
-    97, 81, 4146, 7566, 1648, 1850, 1558, 160, 848, 7567, 866, 740, 1694, 7568, 2201, 2830,  # 4998
-    3195, 4147, 4407, 3653, 1687, 950, 2472, 426, 469, 3196, 3654, 3655, 3928, 7569, 7570, 1188,  # 5014
-    424, 1995, 861, 3546, 4148, 3775, 2202, 2685, 168, 1235, 3547, 4149, 7571, 2086, 1674, 4408,  # 5030
-    3337, 3270, 220, 2557, 1009, 7572, 3776, 670, 2992, 332, 1208, 717, 7573, 7574, 3548, 2447,  # 5046
-    3929, 3338, 7575, 513, 7576, 1209, 2868, 3339, 3138, 4409, 1080, 7577, 7578, 7579, 7580, 2527,  # 5062
-    3656, 3549, 815, 1587, 3930, 3931, 7581, 3550, 3439, 3777, 1254, 4410, 1328, 3038, 1390, 3932,  # 5078
-    1741, 3933, 3778, 3934, 7582, 236, 3779, 2448, 3271, 7583, 7584, 3657, 3780, 1273, 3781, 4411,  # 5094
-    7585, 308, 7586, 4412, 245, 4413, 1851, 2473, 1307, 2575, 430, 715, 2136, 2449, 7587, 270,  # 5110
-    199, 2869, 3935, 7588, 3551, 2718, 1753, 761, 1754, 725, 1661, 1840, 4414, 3440, 3658, 7589,  # 5126
-    7590, 587, 14, 3272, 227, 2598, 326, 480, 2265, 943, 2755, 3552, 291, 650, 1883, 7591,  # 5142
-    1702, 1226, 102, 1547, 62, 3441, 904, 4415, 3442, 1164, 4150, 7592, 7593, 1224, 1548, 2756,  # 5158
-    391, 498, 1493, 7594, 1386, 1419, 7595, 2055, 1177, 4416, 813, 880, 1081, 2363, 566, 1145,  # 5174
-    4417, 2286, 1001, 1035, 2558, 2599, 2238, 394, 1286, 7596, 7597, 2068, 7598, 86, 1494, 1730,  # 5190
-    3936, 491, 1588, 745, 897, 2948, 843, 3340, 3937, 2757, 2870, 3273, 1768, 998, 2217, 2069,  # 5206
-    397, 1826, 1195, 1969, 3659, 2993, 3341, 284, 7599, 3782, 2500, 2137, 2119, 1903, 7600, 3938,  # 5222
-    2150, 3939, 4151, 1036, 3443, 1904, 114, 2559, 4152, 209, 1527, 7601, 7602, 2949, 2831, 2625,  # 5238
-    2385, 2719, 3139, 812, 2560, 7603, 3274, 7604, 1559, 737, 1884, 3660, 1210, 885, 28, 2686,  # 5254
-    3553, 3783, 7605, 4153, 1004, 1779, 4418, 7606, 346, 1981, 2218, 2687, 4419, 3784, 1742, 797,  # 5270
-    1642, 3940, 1933, 1072, 1384, 2151, 896, 3941, 3275, 3661, 3197, 2871, 3554, 7607, 2561, 1958,  # 5286
-    4420, 2450, 1785, 7608, 7609, 7610, 3942, 4154, 1005, 1308, 3662, 4155, 2720, 4421, 4422, 1528,  # 5302
-    2600, 161, 1178, 4156, 1982, 987, 4423, 1101, 4157, 631, 3943, 1157, 3198, 2420, 1343, 1241,  # 5318
-    1016, 2239, 2562, 372, 877, 2339, 2501, 1160, 555, 1934, 911, 3944, 7611, 466, 1170, 169,  # 5334
-    1051, 2907, 2688, 3663, 2474, 2994, 1182, 2011, 2563, 1251, 2626, 7612, 992, 2340, 3444, 1540,  # 5350
-    2721, 1201, 2070, 2401, 1996, 2475, 7613, 4424, 528, 1922, 2188, 1503, 1873, 1570, 2364, 3342,  # 5366
-    3276, 7614, 557, 1073, 7615, 1827, 3445, 2087, 2266, 3140, 3039, 3084, 767, 3085, 2786, 4425,  # 5382
-    1006, 4158, 4426, 2341, 1267, 2176, 3664, 3199, 778, 3945, 3200, 2722, 1597, 2657, 7616, 4427,  # 5398
-    7617, 3446, 7618, 7619, 7620, 3277, 2689, 1433, 3278, 131, 95, 1504, 3946, 723, 4159, 3141,  # 5414
-    1841, 3555, 2758, 2189, 3947, 2027, 2104, 3665, 7621, 2995, 3948, 1218, 7622, 3343, 3201, 3949,  # 5430
-    4160, 2576, 248, 1634, 3785, 912, 7623, 2832, 3666, 3040, 3786, 654, 53, 7624, 2996, 7625,  # 5446
-    1688, 4428, 777, 3447, 1032, 3950, 1425, 7626, 191, 820, 2120, 2833, 971, 4429, 931, 3202,  # 5462
-    135, 664, 783, 3787, 1997, 772, 2908, 1935, 3951, 3788, 4430, 2909, 3203, 282, 2723, 640,  # 5478
-    1372, 3448, 1127, 922, 325, 3344, 7627, 7628, 711, 2044, 7629, 7630, 3952, 2219, 2787, 1936,  # 5494
-    3953, 3345, 2220, 2251, 3789, 2300, 7631, 4431, 3790, 1258, 3279, 3954, 3204, 2138, 2950, 3955,  # 5510
-    3956, 7632, 2221, 258, 3205, 4432, 101, 1227, 7633, 3280, 1755, 7634, 1391, 3281, 7635, 2910,  # 5526
-    2056, 893, 7636, 7637, 7638, 1402, 4161, 2342, 7639, 7640, 3206, 3556, 7641, 7642, 878, 1325,  # 5542
-    1780, 2788, 4433, 259, 1385, 2577, 744, 1183, 2267, 4434, 7643, 3957, 2502, 7644, 684, 1024,  # 5558
-    4162, 7645, 472, 3557, 3449, 1165, 3282, 3958, 3959, 322, 2152, 881, 455, 1695, 1152, 1340,  # 5574
-    660, 554, 2153, 4435, 1058, 4436, 4163, 830, 1065, 3346, 3960, 4437, 1923, 7646, 1703, 1918,  # 5590
-    7647, 932, 2268, 122, 7648, 4438, 947, 677, 7649, 3791, 2627, 297, 1905, 1924, 2269, 4439,  # 5606
-    2317, 3283, 7650, 7651, 4164, 7652, 4165, 84, 4166, 112, 989, 7653, 547, 1059, 3961, 701,  # 5622
-    3558, 1019, 7654, 4167, 7655, 3450, 942, 639, 457, 2301, 2451, 993, 2951, 407, 851, 494,  # 5638
-    4440, 3347, 927, 7656, 1237, 7657, 2421, 3348, 573, 4168, 680, 921, 2911, 1279, 1874, 285,  # 5654
-    790, 1448, 1983, 719, 2167, 7658, 7659, 4441, 3962, 3963, 1649, 7660, 1541, 563, 7661, 1077,  # 5670
-    7662, 3349, 3041, 3451, 511, 2997, 3964, 3965, 3667, 3966, 1268, 2564, 3350, 3207, 4442, 4443,  # 5686
-    7663, 535, 1048, 1276, 1189, 2912, 2028, 3142, 1438, 1373, 2834, 2952, 1134, 2012, 7664, 4169,  # 5702
-    1238, 2578, 3086, 1259, 7665, 700, 7666, 2953, 3143, 3668, 4170, 7667, 4171, 1146, 1875, 1906,  # 5718
-    4444, 2601, 3967, 781, 2422, 132, 1589, 203, 147, 273, 2789, 2402, 898, 1786, 2154, 3968,  # 5734
-    3969, 7668, 3792, 2790, 7669, 7670, 4445, 4446, 7671, 3208, 7672, 1635, 3793, 965, 7673, 1804,  # 5750
-    2690, 1516, 3559, 1121, 1082, 1329, 3284, 3970, 1449, 3794, 65, 1128, 2835, 2913, 2759, 1590,  # 5766
-    3795, 7674, 7675, 12, 2658, 45, 976, 2579, 3144, 4447, 517, 2528, 1013, 1037, 3209, 7676,  # 5782
-    3796, 2836, 7677, 3797, 7678, 3452, 7679, 2602, 614, 1998, 2318, 3798, 3087, 2724, 2628, 7680,  # 5798
-    2580, 4172, 599, 1269, 7681, 1810, 3669, 7682, 2691, 3088, 759, 1060, 489, 1805, 3351, 3285,  # 5814
-    1358, 7683, 7684, 2386, 1387, 1215, 2629, 2252, 490, 7685, 7686, 4173, 1759, 2387, 2343, 7687,  # 5830
-    4448, 3799, 1907, 3971, 2630, 1806, 3210, 4449, 3453, 3286, 2760, 2344, 874, 7688, 7689, 3454,  # 5846
-    3670, 1858, 91, 2914, 3671, 3042, 3800, 4450, 7690, 3145, 3972, 2659, 7691, 3455, 1202, 1403,  # 5862
-    3801, 2954, 2529, 1517, 2503, 4451, 3456, 2504, 7692, 4452, 7693, 2692, 1885, 1495, 1731, 3973,  # 5878
-    2365, 4453, 7694, 2029, 7695, 7696, 3974, 2693, 1216, 237, 2581, 4174, 2319, 3975, 3802, 4454,  # 5894
-    4455, 2694, 3560, 3457, 445, 4456, 7697, 7698, 7699, 7700, 2761, 61, 3976, 3672, 1822, 3977,  # 5910
-    7701, 687, 2045, 935, 925, 405, 2660, 703, 1096, 1859, 2725, 4457, 3978, 1876, 1367, 2695,  # 5926
-    3352, 918, 2105, 1781, 2476, 334, 3287, 1611, 1093, 4458, 564, 3146, 3458, 3673, 3353, 945,  # 5942
-    2631, 2057, 4459, 7702, 1925, 872, 4175, 7703, 3459, 2696, 3089, 349, 4176, 3674, 3979, 4460,  # 5958
-    3803, 4177, 3675, 2155, 3980, 4461, 4462, 4178, 4463, 2403, 2046, 782, 3981, 400, 251, 4179,  # 5974
-    1624, 7704, 7705, 277, 3676, 299, 1265, 476, 1191, 3804, 2121, 4180, 4181, 1109, 205, 7706,  # 5990
-    2582, 1000, 2156, 3561, 1860, 7707, 7708, 7709, 4464, 7710, 4465, 2565, 107, 2477, 2157, 3982,  # 6006
-    3460, 3147, 7711, 1533, 541, 1301, 158, 753, 4182, 2872, 3562, 7712, 1696, 370, 1088, 4183,  # 6022
-    4466, 3563, 579, 327, 440, 162, 2240, 269, 1937, 1374, 3461, 968, 3043, 56, 1396, 3090,  # 6038
-    2106, 3288, 3354, 7713, 1926, 2158, 4467, 2998, 7714, 3564, 7715, 7716, 3677, 4468, 2478, 7717,  # 6054
-    2791, 7718, 1650, 4469, 7719, 2603, 7720, 7721, 3983, 2661, 3355, 1149, 3356, 3984, 3805, 3985,  # 6070
-    7722, 1076, 49, 7723, 951, 3211, 3289, 3290, 450, 2837, 920, 7724, 1811, 2792, 2366, 4184,  # 6086
-    1908, 1138, 2367, 3806, 3462, 7725, 3212, 4470, 1909, 1147, 1518, 2423, 4471, 3807, 7726, 4472,  # 6102
-    2388, 2604, 260, 1795, 3213, 7727, 7728, 3808, 3291, 708, 7729, 3565, 1704, 7730, 3566, 1351,  # 6118
-    1618, 3357, 2999, 1886, 944, 4185, 3358, 4186, 3044, 3359, 4187, 7731, 3678, 422, 413, 1714,  # 6134
-    3292, 500, 2058, 2345, 4188, 2479, 7732, 1344, 1910, 954, 7733, 1668, 7734, 7735, 3986, 2404,  # 6150
-    4189, 3567, 3809, 4190, 7736, 2302, 1318, 2505, 3091, 133, 3092, 2873, 4473, 629, 31, 2838,  # 6166
-    2697, 3810, 4474, 850, 949, 4475, 3987, 2955, 1732, 2088, 4191, 1496, 1852, 7737, 3988, 620,  # 6182
-    3214, 981, 1242, 3679, 3360, 1619, 3680, 1643, 3293, 2139, 2452, 1970, 1719, 3463, 2168, 7738,  # 6198
-    3215, 7739, 7740, 3361, 1828, 7741, 1277, 4476, 1565, 2047, 7742, 1636, 3568, 3093, 7743, 869,  # 6214
-    2839, 655, 3811, 3812, 3094, 3989, 3000, 3813, 1310, 3569, 4477, 7744, 7745, 7746, 1733, 558,  # 6230
-    4478, 3681, 335, 1549, 3045, 1756, 4192, 3682, 1945, 3464, 1829, 1291, 1192, 470, 2726, 2107,  # 6246
-    2793, 913, 1054, 3990, 7747, 1027, 7748, 3046, 3991, 4479, 982, 2662, 3362, 3148, 3465, 3216,  # 6262
-    3217, 1946, 2794, 7749, 571, 4480, 7750, 1830, 7751, 3570, 2583, 1523, 2424, 7752, 2089, 984,  # 6278
-    4481, 3683, 1959, 7753, 3684, 852, 923, 2795, 3466, 3685, 969, 1519, 999, 2048, 2320, 1705,  # 6294
-    7754, 3095, 615, 1662, 151, 597, 3992, 2405, 2321, 1049, 275, 4482, 3686, 4193, 568, 3687,  # 6310
-    3571, 2480, 4194, 3688, 7755, 2425, 2270, 409, 3218, 7756, 1566, 2874, 3467, 1002, 769, 2840,  # 6326
-    194, 2090, 3149, 3689, 2222, 3294, 4195, 628, 1505, 7757, 7758, 1763, 2177, 3001, 3993, 521,  # 6342
-    1161, 2584, 1787, 2203, 2406, 4483, 3994, 1625, 4196, 4197, 412, 42, 3096, 464, 7759, 2632,  # 6358
-    4484, 3363, 1760, 1571, 2875, 3468, 2530, 1219, 2204, 3814, 2633, 2140, 2368, 4485, 4486, 3295,  # 6374
-    1651, 3364, 3572, 7760, 7761, 3573, 2481, 3469, 7762, 3690, 7763, 7764, 2271, 2091, 460, 7765,  # 6390
-    4487, 7766, 3002, 962, 588, 3574, 289, 3219, 2634, 1116, 52, 7767, 3047, 1796, 7768, 7769,  # 6406
-    7770, 1467, 7771, 1598, 1143, 3691, 4198, 1984, 1734, 1067, 4488, 1280, 3365, 465, 4489, 1572,  # 6422
-    510, 7772, 1927, 2241, 1812, 1644, 3575, 7773, 4490, 3692, 7774, 7775, 2663, 1573, 1534, 7776,  # 6438
-    7777, 4199, 536, 1807, 1761, 3470, 3815, 3150, 2635, 7778, 7779, 7780, 4491, 3471, 2915, 1911,  # 6454
-    2796, 7781, 3296, 1122, 377, 3220, 7782, 360, 7783, 7784, 4200, 1529, 551, 7785, 2059, 3693,  # 6470
-    1769, 2426, 7786, 2916, 4201, 3297, 3097, 2322, 2108, 2030, 4492, 1404, 136, 1468, 1479, 672,  # 6486
-    1171, 3221, 2303, 271, 3151, 7787, 2762, 7788, 2049, 678, 2727, 865, 1947, 4493, 7789, 2013,  # 6502
-    3995, 2956, 7790, 2728, 2223, 1397, 3048, 3694, 4494, 4495, 1735, 2917, 3366, 3576, 7791, 3816,  # 6518
-    509, 2841, 2453, 2876, 3817, 7792, 7793, 3152, 3153, 4496, 4202, 2531, 4497, 2304, 1166, 1010,  # 6534
-    552, 681, 1887, 7794, 7795, 2957, 2958, 3996, 1287, 1596, 1861, 3154, 358, 453, 736, 175,  # 6550
-    478, 1117, 905, 1167, 1097, 7796, 1853, 1530, 7797, 1706, 7798, 2178, 3472, 2287, 3695, 3473,  # 6566
-    3577, 4203, 2092, 4204, 7799, 3367, 1193, 2482, 4205, 1458, 2190, 2205, 1862, 1888, 1421, 3298,  # 6582
-    2918, 3049, 2179, 3474, 595, 2122, 7800, 3997, 7801, 7802, 4206, 1707, 2636, 223, 3696, 1359,  # 6598
-    751, 3098, 183, 3475, 7803, 2797, 3003, 419, 2369, 633, 704, 3818, 2389, 241, 7804, 7805,  # 6614
-    7806, 838, 3004, 3697, 2272, 2763, 2454, 3819, 1938, 2050, 3998, 1309, 3099, 2242, 1181, 7807,  # 6630
-    1136, 2206, 3820, 2370, 1446, 4207, 2305, 4498, 7808, 7809, 4208, 1055, 2605, 484, 3698, 7810,  # 6646
-    3999, 625, 4209, 2273, 3368, 1499, 4210, 4000, 7811, 4001, 4211, 3222, 2274, 2275, 3476, 7812,  # 6662
-    7813, 2764, 808, 2606, 3699, 3369, 4002, 4212, 3100, 2532, 526, 3370, 3821, 4213, 955, 7814,  # 6678
-    1620, 4214, 2637, 2427, 7815, 1429, 3700, 1669, 1831, 994, 928, 7816, 3578, 1260, 7817, 7818,  # 6694
-    7819, 1948, 2288, 741, 2919, 1626, 4215, 2729, 2455, 867, 1184, 362, 3371, 1392, 7820, 7821,  # 6710
-    4003, 4216, 1770, 1736, 3223, 2920, 4499, 4500, 1928, 2698, 1459, 1158, 7822, 3050, 3372, 2877,  # 6726
-    1292, 1929, 2506, 2842, 3701, 1985, 1187, 2071, 2014, 2607, 4217, 7823, 2566, 2507, 2169, 3702,  # 6742
-    2483, 3299, 7824, 3703, 4501, 7825, 7826, 666, 1003, 3005, 1022, 3579, 4218, 7827, 4502, 1813,  # 6758
-    2253, 574, 3822, 1603, 295, 1535, 705, 3823, 4219, 283, 858, 417, 7828, 7829, 3224, 4503,  # 6774
-    4504, 3051, 1220, 1889, 1046, 2276, 2456, 4004, 1393, 1599, 689, 2567, 388, 4220, 7830, 2484,  # 6790
-    802, 7831, 2798, 3824, 2060, 1405, 2254, 7832, 4505, 3825, 2109, 1052, 1345, 3225, 1585, 7833,  # 6806
-    809, 7834, 7835, 7836, 575, 2730, 3477, 956, 1552, 1469, 1144, 2323, 7837, 2324, 1560, 2457,  # 6822
-    3580, 3226, 4005, 616, 2207, 3155, 2180, 2289, 7838, 1832, 7839, 3478, 4506, 7840, 1319, 3704,  # 6838
-    3705, 1211, 3581, 1023, 3227, 1293, 2799, 7841, 7842, 7843, 3826, 607, 2306, 3827, 762, 2878,  # 6854
-    1439, 4221, 1360, 7844, 1485, 3052, 7845, 4507, 1038, 4222, 1450, 2061, 2638, 4223, 1379, 4508,  # 6870
-    2585, 7846, 7847, 4224, 1352, 1414, 2325, 2921, 1172, 7848, 7849, 3828, 3829, 7850, 1797, 1451,  # 6886
-    7851, 7852, 7853, 7854, 2922, 4006, 4007, 2485, 2346, 411, 4008, 4009, 3582, 3300, 3101, 4509,  # 6902
-    1561, 2664, 1452, 4010, 1375, 7855, 7856, 47, 2959, 316, 7857, 1406, 1591, 2923, 3156, 7858,  # 6918
-    1025, 2141, 3102, 3157, 354, 2731, 884, 2224, 4225, 2407, 508, 3706, 726, 3583, 996, 2428,  # 6934
-    3584, 729, 7859, 392, 2191, 1453, 4011, 4510, 3707, 7860, 7861, 2458, 3585, 2608, 1675, 2800,  # 6950
-    919, 2347, 2960, 2348, 1270, 4511, 4012, 73, 7862, 7863, 647, 7864, 3228, 2843, 2255, 1550,  # 6966
-    1346, 3006, 7865, 1332, 883, 3479, 7866, 7867, 7868, 7869, 3301, 2765, 7870, 1212, 831, 1347,  # 6982
-    4226, 4512, 2326, 3830, 1863, 3053, 720, 3831, 4513, 4514, 3832, 7871, 4227, 7872, 7873, 4515,  # 6998
-    7874, 7875, 1798, 4516, 3708, 2609, 4517, 3586, 1645, 2371, 7876, 7877, 2924, 669, 2208, 2665,  # 7014
-    2429, 7878, 2879, 7879, 7880, 1028, 3229, 7881, 4228, 2408, 7882, 2256, 1353, 7883, 7884, 4518,  # 7030
-    3158, 518, 7885, 4013, 7886, 4229, 1960, 7887, 2142, 4230, 7888, 7889, 3007, 2349, 2350, 3833,  # 7046
-    516, 1833, 1454, 4014, 2699, 4231, 4519, 2225, 2610, 1971, 1129, 3587, 7890, 2766, 7891, 2961,  # 7062
-    1422, 577, 1470, 3008, 1524, 3373, 7892, 7893, 432, 4232, 3054, 3480, 7894, 2586, 1455, 2508,  # 7078
-    2226, 1972, 1175, 7895, 1020, 2732, 4015, 3481, 4520, 7896, 2733, 7897, 1743, 1361, 3055, 3482,  # 7094
-    2639, 4016, 4233, 4521, 2290, 895, 924, 4234, 2170, 331, 2243, 3056, 166, 1627, 3057, 1098,  # 7110
-    7898, 1232, 2880, 2227, 3374, 4522, 657, 403, 1196, 2372, 542, 3709, 3375, 1600, 4235, 3483,  # 7126
-    7899, 4523, 2767, 3230, 576, 530, 1362, 7900, 4524, 2533, 2666, 3710, 4017, 7901, 842, 3834,  # 7142
-    7902, 2801, 2031, 1014, 4018, 213, 2700, 3376, 665, 621, 4236, 7903, 3711, 2925, 2430, 7904,  # 7158
-    2431, 3302, 3588, 3377, 7905, 4237, 2534, 4238, 4525, 3589, 1682, 4239, 3484, 1380, 7906, 724,  # 7174
-    2277, 600, 1670, 7907, 1337, 1233, 4526, 3103, 2244, 7908, 1621, 4527, 7909, 651, 4240, 7910,  # 7190
-    1612, 4241, 2611, 7911, 2844, 7912, 2734, 2307, 3058, 7913, 716, 2459, 3059, 174, 1255, 2701,  # 7206
-    4019, 3590, 548, 1320, 1398, 728, 4020, 1574, 7914, 1890, 1197, 3060, 4021, 7915, 3061, 3062,  # 7222
-    3712, 3591, 3713, 747, 7916, 635, 4242, 4528, 7917, 7918, 7919, 4243, 7920, 7921, 4529, 7922,  # 7238
-    3378, 4530, 2432, 451, 7923, 3714, 2535, 2072, 4244, 2735, 4245, 4022, 7924, 1764, 4531, 7925,  # 7254
-    4246, 350, 7926, 2278, 2390, 2486, 7927, 4247, 4023, 2245, 1434, 4024, 488, 4532, 458, 4248,  # 7270
-    4025, 3715, 771, 1330, 2391, 3835, 2568, 3159, 2159, 2409, 1553, 2667, 3160, 4249, 7928, 2487,  # 7286
-    2881, 2612, 1720, 2702, 4250, 3379, 4533, 7929, 2536, 4251, 7930, 3231, 4252, 2768, 7931, 2015,  # 7302
-    2736, 7932, 1155, 1017, 3716, 3836, 7933, 3303, 2308, 201, 1864, 4253, 1430, 7934, 4026, 7935,  # 7318
-    7936, 7937, 7938, 7939, 4254, 1604, 7940, 414, 1865, 371, 2587, 4534, 4535, 3485, 2016, 3104,  # 7334
-    4536, 1708, 960, 4255, 887, 389, 2171, 1536, 1663, 1721, 7941, 2228, 4027, 2351, 2926, 1580,  # 7350
-    7942, 7943, 7944, 1744, 7945, 2537, 4537, 4538, 7946, 4539, 7947, 2073, 7948, 7949, 3592, 3380,  # 7366
-    2882, 4256, 7950, 4257, 2640, 3381, 2802, 673, 2703, 2460, 709, 3486, 4028, 3593, 4258, 7951,  # 7382
-    1148, 502, 634, 7952, 7953, 1204, 4540, 3594, 1575, 4541, 2613, 3717, 7954, 3718, 3105, 948,  # 7398
-    3232, 121, 1745, 3837, 1110, 7955, 4259, 3063, 2509, 3009, 4029, 3719, 1151, 1771, 3838, 1488,  # 7414
-    4030, 1986, 7956, 2433, 3487, 7957, 7958, 2093, 7959, 4260, 3839, 1213, 1407, 2803, 531, 2737,  # 7430
-    2538, 3233, 1011, 1537, 7960, 2769, 4261, 3106, 1061, 7961, 3720, 3721, 1866, 2883, 7962, 2017,  # 7446
-    120, 4262, 4263, 2062, 3595, 3234, 2309, 3840, 2668, 3382, 1954, 4542, 7963, 7964, 3488, 1047,  # 7462
-    2704, 1266, 7965, 1368, 4543, 2845, 649, 3383, 3841, 2539, 2738, 1102, 2846, 2669, 7966, 7967,  # 7478
-    1999, 7968, 1111, 3596, 2962, 7969, 2488, 3842, 3597, 2804, 1854, 3384, 3722, 7970, 7971, 3385,  # 7494
-    2410, 2884, 3304, 3235, 3598, 7972, 2569, 7973, 3599, 2805, 4031, 1460, 856, 7974, 3600, 7975,  # 7510
-    2885, 2963, 7976, 2886, 3843, 7977, 4264, 632, 2510, 875, 3844, 1697, 3845, 2291, 7978, 7979,  # 7526
-    4544, 3010, 1239, 580, 4545, 4265, 7980, 914, 936, 2074, 1190, 4032, 1039, 2123, 7981, 7982,  # 7542
-    7983, 3386, 1473, 7984, 1354, 4266, 3846, 7985, 2172, 3064, 4033, 915, 3305, 4267, 4268, 3306,  # 7558
-    1605, 1834, 7986, 2739, 398, 3601, 4269, 3847, 4034, 328, 1912, 2847, 4035, 3848, 1331, 4270,  # 7574
-    3011, 937, 4271, 7987, 3602, 4036, 4037, 3387, 2160, 4546, 3388, 524, 742, 538, 3065, 1012,  # 7590
-    7988, 7989, 3849, 2461, 7990, 658, 1103, 225, 3850, 7991, 7992, 4547, 7993, 4548, 7994, 3236,  # 7606
-    1243, 7995, 4038, 963, 2246, 4549, 7996, 2705, 3603, 3161, 7997, 7998, 2588, 2327, 7999, 4550,  # 7622
-    8000, 8001, 8002, 3489, 3307, 957, 3389, 2540, 2032, 1930, 2927, 2462, 870, 2018, 3604, 1746,  # 7638
-    2770, 2771, 2434, 2463, 8003, 3851, 8004, 3723, 3107, 3724, 3490, 3390, 3725, 8005, 1179, 3066,  # 7654
-    8006, 3162, 2373, 4272, 3726, 2541, 3163, 3108, 2740, 4039, 8007, 3391, 1556, 2542, 2292, 977,  # 7670
-    2887, 2033, 4040, 1205, 3392, 8008, 1765, 3393, 3164, 2124, 1271, 1689, 714, 4551, 3491, 8009,  # 7686
-    2328, 3852, 533, 4273, 3605, 2181, 617, 8010, 2464, 3308, 3492, 2310, 8011, 8012, 3165, 8013,  # 7702
-    8014, 3853, 1987, 618, 427, 2641, 3493, 3394, 8015, 8016, 1244, 1690, 8017, 2806, 4274, 4552,  # 7718
-    8018, 3494, 8019, 8020, 2279, 1576, 473, 3606, 4275, 3395, 972, 8021, 3607, 8022, 3067, 8023,  # 7734
-    8024, 4553, 4554, 8025, 3727, 4041, 4042, 8026, 153, 4555, 356, 8027, 1891, 2888, 4276, 2143,  # 7750
-    408, 803, 2352, 8028, 3854, 8029, 4277, 1646, 2570, 2511, 4556, 4557, 3855, 8030, 3856, 4278,  # 7766
-    8031, 2411, 3396, 752, 8032, 8033, 1961, 2964, 8034, 746, 3012, 2465, 8035, 4279, 3728, 698,  # 7782
-    4558, 1892, 4280, 3608, 2543, 4559, 3609, 3857, 8036, 3166, 3397, 8037, 1823, 1302, 4043, 2706,  # 7798
-    3858, 1973, 4281, 8038, 4282, 3167, 823, 1303, 1288, 1236, 2848, 3495, 4044, 3398, 774, 3859,  # 7814
-    8039, 1581, 4560, 1304, 2849, 3860, 4561, 8040, 2435, 2161, 1083, 3237, 4283, 4045, 4284, 344,  # 7830
-    1173, 288, 2311, 454, 1683, 8041, 8042, 1461, 4562, 4046, 2589, 8043, 8044, 4563, 985, 894,  # 7846
-    8045, 3399, 3168, 8046, 1913, 2928, 3729, 1988, 8047, 2110, 1974, 8048, 4047, 8049, 2571, 1194,  # 7862
-    425, 8050, 4564, 3169, 1245, 3730, 4285, 8051, 8052, 2850, 8053, 636, 4565, 1855, 3861, 760,  # 7878
-    1799, 8054, 4286, 2209, 1508, 4566, 4048, 1893, 1684, 2293, 8055, 8056, 8057, 4287, 4288, 2210,  # 7894
-    479, 8058, 8059, 832, 8060, 4049, 2489, 8061, 2965, 2490, 3731, 990, 3109, 627, 1814, 2642,  # 7910
-    4289, 1582, 4290, 2125, 2111, 3496, 4567, 8062, 799, 4291, 3170, 8063, 4568, 2112, 1737, 3013,  # 7926
-    1018, 543, 754, 4292, 3309, 1676, 4569, 4570, 4050, 8064, 1489, 8065, 3497, 8066, 2614, 2889,  # 7942
-    4051, 8067, 8068, 2966, 8069, 8070, 8071, 8072, 3171, 4571, 4572, 2182, 1722, 8073, 3238, 3239,  # 7958
-    1842, 3610, 1715, 481, 365, 1975, 1856, 8074, 8075, 1962, 2491, 4573, 8076, 2126, 3611, 3240,  # 7974
-    433, 1894, 2063, 2075, 8077, 602, 2741, 8078, 8079, 8080, 8081, 8082, 3014, 1628, 3400, 8083,  # 7990
-    3172, 4574, 4052, 2890, 4575, 2512, 8084, 2544, 2772, 8085, 8086, 8087, 3310, 4576, 2891, 8088,  # 8006
-    4577, 8089, 2851, 4578, 4579, 1221, 2967, 4053, 2513, 8090, 8091, 8092, 1867, 1989, 8093, 8094,  # 8022
-    8095, 1895, 8096, 8097, 4580, 1896, 4054, 318, 8098, 2094, 4055, 4293, 8099, 8100, 485, 8101,  # 8038
-    938, 3862, 553, 2670, 116, 8102, 3863, 3612, 8103, 3498, 2671, 2773, 3401, 3311, 2807, 8104,  # 8054
-    3613, 2929, 4056, 1747, 2930, 2968, 8105, 8106, 207, 8107, 8108, 2672, 4581, 2514, 8109, 3015,  # 8070
-    890, 3614, 3864, 8110, 1877, 3732, 3402, 8111, 2183, 2353, 3403, 1652, 8112, 8113, 8114, 941,  # 8086
-    2294, 208, 3499, 4057, 2019, 330, 4294, 3865, 2892, 2492, 3733, 4295, 8115, 8116, 8117, 8118,  # 8102
-)
-# fmt: on
diff --git a/spaces/Rayzggz/illi-Bert-VITS2/attentions.py b/spaces/Rayzggz/illi-Bert-VITS2/attentions.py
deleted file mode 100644
index 3ba2407267ecd425d2095a6428015b5b4ebc0bda..0000000000000000000000000000000000000000
--- a/spaces/Rayzggz/illi-Bert-VITS2/attentions.py
+++ /dev/null
@@ -1,464 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=4,
-        isflow=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        # if isflow:
-        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
-        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
-        #  self.cond_layer = weight_norm(cond_layer, name='weight')
-        #  self.gin_channels = 256
-        self.cond_layer_idx = self.n_layers
-        if "gin_channels" in kwargs:
-            self.gin_channels = kwargs["gin_channels"]
-            if self.gin_channels != 0:
-                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
-                # vits2 says 3rd block, so idx is 2 by default
-                self.cond_layer_idx = (
-                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
-                )
-                logging.debug(self.gin_channels, self.cond_layer_idx)
-                assert (
-                    self.cond_layer_idx < self.n_layers
-                ), "cond_layer_idx should be less than n_layers"
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, g=None):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            if i == self.cond_layer_idx and g is not None:
-                g = self.spk_emb_linear(g.transpose(1, 2))
-                g = g.transpose(1, 2)
-                x = x + g
-                x = x * x_mask
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # pad along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/scannet.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/scannet.py
deleted file mode 100644
index 91bea57c9d1ae2773c11a9c8d47f31026a2c227b..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/datasets/scannet.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import os
-import random
-from PIL import Image
-import cv2
-import h5py
-import numpy as np
-import torch
-from torch.utils.data import Dataset, DataLoader, ConcatDataset
-
-import torchvision.transforms.functional as tvf
-import kornia.augmentation as K
-import os.path as osp
-import matplotlib.pyplot as plt
-import roma
-from roma.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops
-from roma.utils.transforms import GeometricSequential
-from tqdm import tqdm
-
-
-class ScanNetScene:
-    def __init__(
-        self,
-        data_root,
-        scene_info,
-        ht=384,
-        wt=512,
-        min_overlap=0.0,
-        shake_t=0,
-        rot_prob=0.0,
-        use_horizontal_flip_aug=False,
-    ) -> None:
-        self.scene_root = osp.join(data_root, "scans", "scans_train")
-        self.data_names = scene_info["name"]
-        self.overlaps = scene_info["score"]
-        # Only sample 10s
-        valid = (self.data_names[:, -2:] % 10).sum(axis=-1) == 0
-        self.overlaps = self.overlaps[valid]
-        self.data_names = self.data_names[valid]
-        if len(self.data_names) > 10000:
-            pairinds = np.random.choice(
-                np.arange(0, len(self.data_names)), 10000, replace=False
-            )
-            self.data_names = self.data_names[pairinds]
-            self.overlaps = self.overlaps[pairinds]
-        self.im_transform_ops = get_tuple_transform_ops(resize=(ht, wt), normalize=True)
-        self.depth_transform_ops = get_depth_tuple_transform_ops(
-            resize=(ht, wt), normalize=False
-        )
-        self.wt, self.ht = wt, ht
-        self.shake_t = shake_t
-        self.H_generator = GeometricSequential(K.RandomAffine(degrees=90, p=rot_prob))
-        self.use_horizontal_flip_aug = use_horizontal_flip_aug
-
-    def load_im(self, im_B, crop=None):
-        im = Image.open(im_B)
-        return im
-
-    def load_depth(self, depth_ref, crop=None):
-        depth = cv2.imread(str(depth_ref), cv2.IMREAD_UNCHANGED)
-        depth = depth / 1000
-        depth = torch.from_numpy(depth).float()  # (h, w)
-        return depth
-
-    def __len__(self):
-        return len(self.data_names)
-
-    def scale_intrinsic(self, K, wi, hi):
-        sx, sy = self.wt / wi, self.ht / hi
-        sK = torch.tensor([[sx, 0, 0], [0, sy, 0], [0, 0, 1]])
-        return sK @ K
-
-    def horizontal_flip(self, im_A, im_B, depth_A, depth_B, K_A, K_B):
-        im_A = im_A.flip(-1)
-        im_B = im_B.flip(-1)
-        depth_A, depth_B = depth_A.flip(-1), depth_B.flip(-1)
-        flip_mat = torch.tensor([[-1, 0, self.wt], [0, 1, 0], [0, 0, 1.0]]).to(
-            K_A.device
-        )
-        K_A = flip_mat @ K_A
-        K_B = flip_mat @ K_B
-
-        return im_A, im_B, depth_A, depth_B, K_A, K_B
-
-    def read_scannet_pose(self, path):
-        """Read ScanNet's Camera2World pose and transform it to World2Camera.
-
-        Returns:
-            pose_w2c (np.ndarray): (4, 4)
-        """
-        cam2world = np.loadtxt(path, delimiter=" ")
-        world2cam = np.linalg.inv(cam2world)
-        return world2cam
-
-    def read_scannet_intrinsic(self, path):
-        """Read ScanNet's intrinsic matrix and return the 3x3 matrix."""
-        intrinsic = np.loadtxt(path, delimiter=" ")
-        return torch.tensor(intrinsic[:-1, :-1], dtype=torch.float)
-
-    def __getitem__(self, pair_idx):
-        # read intrinsics of original size
-        data_name = self.data_names[pair_idx]
-        scene_name, scene_sub_name, stem_name_1, stem_name_2 = data_name
-        scene_name = f"scene{scene_name:04d}_{scene_sub_name:02d}"
-
-        # read the intrinsic of depthmap
-        K1 = K2 = self.read_scannet_intrinsic(
-            osp.join(self.scene_root, scene_name, "intrinsic", "intrinsic_color.txt")
-        )  # the depth K is not the same, but doesnt really matter
-        # read and compute relative poses
-        T1 = self.read_scannet_pose(
-            osp.join(self.scene_root, scene_name, "pose", f"{stem_name_1}.txt")
-        )
-        T2 = self.read_scannet_pose(
-            osp.join(self.scene_root, scene_name, "pose", f"{stem_name_2}.txt")
-        )
-        T_1to2 = torch.tensor(np.matmul(T2, np.linalg.inv(T1)), dtype=torch.float)[
-            :4, :4
-        ]  # (4, 4)
-
-        # Load positive pair data
-        im_A_ref = os.path.join(
-            self.scene_root, scene_name, "color", f"{stem_name_1}.jpg"
-        )
-        im_B_ref = os.path.join(
-            self.scene_root, scene_name, "color", f"{stem_name_2}.jpg"
-        )
-        depth_A_ref = os.path.join(
-            self.scene_root, scene_name, "depth", f"{stem_name_1}.png"
-        )
-        depth_B_ref = os.path.join(
-            self.scene_root, scene_name, "depth", f"{stem_name_2}.png"
-        )
-
-        im_A = self.load_im(im_A_ref)
-        im_B = self.load_im(im_B_ref)
-        depth_A = self.load_depth(depth_A_ref)
-        depth_B = self.load_depth(depth_B_ref)
-
-        # Recompute camera intrinsic matrix due to the resize
-        K1 = self.scale_intrinsic(K1, im_A.width, im_A.height)
-        K2 = self.scale_intrinsic(K2, im_B.width, im_B.height)
-        # Process images
-        im_A, im_B = self.im_transform_ops((im_A, im_B))
-        depth_A, depth_B = self.depth_transform_ops(
-            (depth_A[None, None], depth_B[None, None])
-        )
-        if self.use_horizontal_flip_aug:
-            if np.random.rand() > 0.5:
-                im_A, im_B, depth_A, depth_B, K1, K2 = self.horizontal_flip(
-                    im_A, im_B, depth_A, depth_B, K1, K2
-                )
-
-        data_dict = {
-            "im_A": im_A,
-            "im_B": im_B,
-            "im_A_depth": depth_A[0, 0],
-            "im_B_depth": depth_B[0, 0],
-            "K1": K1,
-            "K2": K2,
-            "T_1to2": T_1to2,
-        }
-        return data_dict
-
-
-class ScanNetBuilder:
-    def __init__(self, data_root="data/scannet") -> None:
-        self.data_root = data_root
-        self.scene_info_root = os.path.join(data_root, "scannet_indices")
-        self.all_scenes = os.listdir(self.scene_info_root)
-
-    def build_scenes(self, split="train", min_overlap=0.0, **kwargs):
-        # Note: split doesn't matter here as we always use same scannet_train scenes
-        scene_names = self.all_scenes
-        scenes = []
-        for scene_name in tqdm(scene_names, disable=roma.RANK > 0):
-            scene_info = np.load(
-                os.path.join(self.scene_info_root, scene_name), allow_pickle=True
-            )
-            scenes.append(
-                ScanNetScene(
-                    self.data_root, scene_info, min_overlap=min_overlap, **kwargs
-                )
-            )
-        return scenes
-
-    def weight_scenes(self, concat_dataset, alpha=0.5):
-        ns = []
-        for d in concat_dataset.datasets:
-            ns.append(len(d))
-        ws = torch.cat([torch.ones(n) / n**alpha for n in ns])
-        return ws
diff --git a/spaces/RedBaron5/PatentSolver/App/bin/PGProcessor.py b/spaces/RedBaron5/PatentSolver/App/bin/PGProcessor.py
deleted file mode 100644
index 796b53dafbc1c5ebb9c3eda15d303531447db0d9..0000000000000000000000000000000000000000
--- a/spaces/RedBaron5/PatentSolver/App/bin/PGProcessor.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import json
-import os
-import re
-import matplotlib.pyplot as plt
-import numpy as np
-from io import StringIO
-from App4api.bin import constants
-from collections import OrderedDict
-from App4api.bin.InformationExtractor import InformationExtractor
-from App4api.bin.ParameterExtractor import ParameterExtractor
-from App4api.bin.TechnologyFinder import TechnologyFinder
-
-class PGProcessor(object):
-
-    def __init__(self, patents,input_folder, file_extension):
-        self.patents = patents
-        self.input_folder = input_folder
-        self.file_extension = file_extension
-        print("Processing started")
-
-    def process_corpus(self):
-
-        count_abstract = 0
-        count_claims = 0
-        count_description = 0
-        count_patent = 0
-        total_sentences_number =0
-        count_concepts_solupart = 0
-        count_concepts_problem = 0
-        patents = self.patents
-        input_folder = self.input_folder
-        file_extension = self.file_extension
-        project_folder = os.path.basename(os.path.normpath(input_folder))
-        graph_folder = constants.GRAPH_FOLDER + project_folder+"/"
-        extracted_concepts = []
-        output_result = []
-        parameters_graph = []
-        reduced_content = []
-        patent_corpus = []
-        source_list = []
-        parameters_list =[]
-        technologies_graph =[]
-
-
-        for patent_file in patents:
-
-            read_patent = StringIO(patent_file)
-            patent = json.load(read_patent)
-            nNumber = patent['number']
-            aAbstract = patent['abstract']
-            cClaims = patent['claims']
-            dDescription = patent['description']
-            source = patent['source']
-
-            if dDescription !="":
-                count_description +=1
-                extract_concepts = InformationExtractor(dDescription,input_folder, file_extension, nNumber, source )
-                output_json, total_sentences_number = extract_concepts.get_from_description()
-                if output_json !="":
-                    extracted_concepts.append(output_json)
-                total_sentences_number += total_sentences_number
-            elif cClaims !="":
-                count_claims +=1
-                print('Processing claims')
-            else:
-                count_abstract +=1
-                print("processing abstract")
-            count_patent +=1
-
-
-            #print(source)
-            source_list.append(source)
-
-
-        header = '{'
-        graph = '"problem_graph": [%s]' % ','.join(extracted_concepts)
-        footer = '}'
-        output_result.extend((header, graph, footer))
-        output_result = "".join(output_result)
-        concepts_json = json.loads(output_result)
-        count_concepts = len(concepts_json['problem_graph'])
-        for item, value in concepts_json.items():
-            #if cle == "type" and value =="partialSolution":
-             #   print ("yes")
-            for element in value:
-                for cle, valeur in element.items():
-                    for k,v in valeur.items():
-                        if k == "type" and v =="partialSolution":
-                            count_concepts_solupart += 1
-                        elif k == "type" and v =="problem":
-                            count_concepts_problem += 1
-        json_write_to_file = json.dumps(concepts_json, sort_keys=False, indent=4, separators=(',', ': '))
-        #print(concepts_json.keys())
-        with open(graph_folder+"graph.json", 'w') as json_graph:
-            json_graph.write(json_write_to_file)
-
-        print("Le corpus contenait %s brevets dont %s abstract, %s revendications et %s descriptions" % (count_patent, count_abstract, count_claims, count_description))
-        print("%s phrases ont été analysée(s)" % (total_sentences_number))
-        print("%s concepts ont été trouvé(s) dont %s problèmes et %s solutions partielles" % (count_concepts, count_concepts_problem, count_concepts_solupart))
-
-        #Display graphics
-        first_color = (46, 204, 113)
-        second_color = (245, 176, 65)
-        #self.make_graphic([count_concepts_problem, count_concepts_solupart], "Ratio",[first_color,second_color],['Problems','Partial Solutions'])
-        return concepts_json
\ No newline at end of file
diff --git a/spaces/Reself/StableVideo/cldm/cldm.py b/spaces/Reself/StableVideo/cldm/cldm.py
deleted file mode 100644
index 1a86f4165f00e6fecdf235407cfb80fbfa36aae0..0000000000000000000000000000000000000000
--- a/spaces/Reself/StableVideo/cldm/cldm.py
+++ /dev/null
@@ -1,429 +0,0 @@
-import einops
-import torch
-import torch as th
-import torch.nn as nn
-
-from ldm.modules.diffusionmodules.util import (
-    conv_nd,
-    linear,
-    zero_module,
-    timestep_embedding,
-)
-
-from einops import rearrange, repeat
-from torchvision.utils import make_grid
-from ldm.modules.attention import SpatialTransformer
-from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
-from ldm.models.diffusion.ddpm import LatentDiffusion
-from ldm.util import log_txt_as_img, exists, instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-
-
-class ControlledUnetModel(UNetModel):
-    def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs):
-        hs = []
-        with torch.no_grad():
-            t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-            emb = self.time_embed(t_emb)
-            h = x.type(self.dtype)
-            for module in self.input_blocks:
-                h = module(h, emb, context)
-                hs.append(h)
-            h = self.middle_block(h, emb, context)
-
-        h += control.pop()
-
-        for i, module in enumerate(self.output_blocks):
-            if only_mid_control:
-                h = torch.cat([h, hs.pop()], dim=1)
-            else:
-                h = torch.cat([h, hs.pop() + control.pop()], dim=1)
-            h = module(h, emb, context)
-
-        h = h.type(x.dtype)
-        return self.out(h)
-
-
-class ControlNet(nn.Module):
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        hint_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None,
-        disable_middle_self_attn=False,
-        use_linear_in_transformer=False,
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.dims = dims
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
-            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
-                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
-                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
-                  f"attention will still not be set.")
-
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
-
-        self.input_hint_block = TimestepEmbedSequential(
-                    conv_nd(dims, hint_channels, 16, 3, padding=1),
-                    nn.SiLU(),
-                    conv_nd(dims, 16, 16, 3, padding=1),
-                    nn.SiLU(),
-                    conv_nd(dims, 16, 32, 3, padding=1, stride=2),
-                    nn.SiLU(),
-                    conv_nd(dims, 32, 32, 3, padding=1),
-                    nn.SiLU(),
-                    conv_nd(dims, 32, 96, 3, padding=1, stride=2),
-                    nn.SiLU(),
-                    conv_nd(dims, 96, 96, 3, padding=1),
-                    nn.SiLU(),
-                    conv_nd(dims, 96, 256, 3, padding=1, stride=2),
-                    nn.SiLU(),
-                    zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
-        )
-
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
-                            )
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self.zero_convs.append(self.make_zero_conv(ch))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                self.zero_convs.append(self.make_zero_conv(ch))
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self.middle_block_out = self.make_zero_conv(ch)
-        self._feature_size += ch
-
-    def make_zero_conv(self, channels):
-        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
-
-    def forward(self, x, hint, timesteps, context, **kwargs):
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        guided_hint = self.input_hint_block(hint, emb, context)
-
-        outs = []
-
-        h = x.type(self.dtype)
-        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
-            if guided_hint is not None:
-                h = module(h, emb, context)
-                h += guided_hint
-                guided_hint = None
-            else:
-                h = module(h, emb, context)
-            outs.append(zero_conv(h, emb, context))
-
-        h = self.middle_block(h, emb, context)
-        outs.append(self.middle_block_out(h, emb, context))
-
-        return outs
-
-
-class ControlLDM(LatentDiffusion):
-
-    def __init__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.control_model = instantiate_from_config(control_stage_config)
-        self.control_key = control_key
-        self.only_mid_control = only_mid_control
-
-    @torch.no_grad()
-    def get_input(self, batch, k, bs=None, *args, **kwargs):
-        x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs)
-        control = batch[self.control_key]
-        if bs is not None:
-            control = control[:bs]
-        control = control.to(self.device)
-        control = einops.rearrange(control, 'b h w c -> b c h w')
-        control = control.to(memory_format=torch.contiguous_format).float()
-        return x, dict(c_crossattn=[c], c_concat=[control])
-
-    def apply_model(self, x_noisy, t, cond, *args, **kwargs):
-        assert isinstance(cond, dict)
-        diffusion_model = self.model.diffusion_model
-        cond_txt = torch.cat(cond['c_crossattn'], 1)
-        cond_hint = torch.cat(cond['c_concat'], 1)
-
-        control = self.control_model(x=x_noisy, hint=cond_hint, timesteps=t, context=cond_txt)
-        eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control)
-
-        return eps
-
-    @torch.no_grad()
-    def get_unconditional_conditioning(self, N):
-        return self.get_learned_conditioning([""] * N)
-
-    @torch.no_grad()
-    def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c = self.get_input(batch, self.first_stage_key, bs=N)
-        c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N]
-        N = min(z.shape[0], N)
-        n_row = min(z.shape[0], n_row)
-        log["reconstruction"] = self.decode_first_stage(z)
-        log["control"] = c_cat * 2.0 - 1.0
-        log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                                     batch_size=N, ddim=use_ddim,
-                                                     ddim_steps=ddim_steps, eta=ddim_eta)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-        if unconditional_guidance_scale > 1.0:
-            uc_cross = self.get_unconditional_conditioning(N)
-            uc_cat = c_cat  # torch.zeros_like(c_cat)
-            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
-            samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                             batch_size=N, ddim=use_ddim,
-                                             ddim_steps=ddim_steps, eta=ddim_eta,
-                                             unconditional_guidance_scale=unconditional_guidance_scale,
-                                             unconditional_conditioning=uc_full,
-                                             )
-            x_samples_cfg = self.decode_first_stage(samples_cfg)
-            log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        return log
-
-    @torch.no_grad()
-    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
-        ddim_sampler = DDIMSampler(self)
-        b, c, h, w = cond["c_concat"][0].shape
-        shape = (self.channels, h // 8, w // 8)
-        samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs)
-        return samples, intermediates
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.control_model.parameters())
-        if not self.sd_locked:
-            params += list(self.model.diffusion_model.output_blocks.parameters())
-            params += list(self.model.diffusion_model.out.parameters())
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-    def low_vram_shift(self, is_diffusing):
-        if is_diffusing:
-            self.model = self.model.cuda()
-            self.control_model = self.control_model.cuda()
-            self.first_stage_model = self.first_stage_model.cpu()
-            self.cond_stage_model = self.cond_stage_model.cpu()
-        else:
-            self.model = self.model.cpu()
-            self.control_model = self.control_model.cpu()
-            self.first_stage_model = self.first_stage_model.cuda()
-            self.cond_stage_model = self.cond_stage_model.cuda()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/ga_rpn_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/ga_rpn_head.py
deleted file mode 100644
index 2ec0d4fdd3475bfbd2e541a6e8130b1df9ad861a..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/ga_rpn_head.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import nms
-
-from ..builder import HEADS
-from .guided_anchor_head import GuidedAnchorHead
-from .rpn_test_mixin import RPNTestMixin
-
-
-@HEADS.register_module()
-class GARPNHead(RPNTestMixin, GuidedAnchorHead):
-    """Guided-Anchor-based RPN head."""
-
-    def __init__(self, in_channels, **kwargs):
-        super(GARPNHead, self).__init__(1, in_channels, **kwargs)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.rpn_conv = nn.Conv2d(
-            self.in_channels, self.feat_channels, 3, padding=1)
-        super(GARPNHead, self)._init_layers()
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        normal_init(self.rpn_conv, std=0.01)
-        super(GARPNHead, self).init_weights()
-
-    def forward_single(self, x):
-        """Forward feature of a single scale level."""
-
-        x = self.rpn_conv(x)
-        x = F.relu(x, inplace=True)
-        (cls_score, bbox_pred, shape_pred,
-         loc_pred) = super(GARPNHead, self).forward_single(x)
-        return cls_score, bbox_pred, shape_pred, loc_pred
-
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             shape_preds,
-             loc_preds,
-             gt_bboxes,
-             img_metas,
-             gt_bboxes_ignore=None):
-        losses = super(GARPNHead, self).loss(
-            cls_scores,
-            bbox_preds,
-            shape_preds,
-            loc_preds,
-            gt_bboxes,
-            None,
-            img_metas,
-            gt_bboxes_ignore=gt_bboxes_ignore)
-        return dict(
-            loss_rpn_cls=losses['loss_cls'],
-            loss_rpn_bbox=losses['loss_bbox'],
-            loss_anchor_shape=losses['loss_shape'],
-            loss_anchor_loc=losses['loss_loc'])
-
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           mlvl_anchors,
-                           mlvl_masks,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        cfg = self.test_cfg if cfg is None else cfg
-
-        cfg = copy.deepcopy(cfg)
-
-        # deprecate arguments warning
-        if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
-            warnings.warn(
-                'In rpn_proposal or test_cfg, '
-                'nms_thr has been moved to a dict named nms as '
-                'iou_threshold, max_num has been renamed as max_per_img, '
-                'name of original arguments and the way to specify '
-                'iou_threshold of NMS will be deprecated.')
-        if 'nms' not in cfg:
-            cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
-        if 'max_num' in cfg:
-            if 'max_per_img' in cfg:
-                assert cfg.max_num == cfg.max_per_img, f'You ' \
-                    f'set max_num and max_per_img at the same time, ' \
-                    f'but get {cfg.max_num} ' \
-                    f'and {cfg.max_per_img} respectively' \
-                    'Please delete max_num which will be deprecated.'
-            else:
-                cfg.max_per_img = cfg.max_num
-        if 'nms_thr' in cfg:
-            assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \
-                f'iou_threshold in nms and ' \
-                f'nms_thr at the same time, but get ' \
-                f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \
-                f' respectively. Please delete the ' \
-                f'nms_thr which will be deprecated.'
-
-        assert cfg.nms.get('type', 'nms') == 'nms', 'GARPNHead only support ' \
-            'naive nms.'
-
-        mlvl_proposals = []
-        for idx in range(len(cls_scores)):
-            rpn_cls_score = cls_scores[idx]
-            rpn_bbox_pred = bbox_preds[idx]
-            anchors = mlvl_anchors[idx]
-            mask = mlvl_masks[idx]
-            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
-            # if no location is kept, end.
-            if mask.sum() == 0:
-                continue
-            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
-            if self.use_sigmoid_cls:
-                rpn_cls_score = rpn_cls_score.reshape(-1)
-                scores = rpn_cls_score.sigmoid()
-            else:
-                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
-                # remind that we set FG labels to [0, num_class-1]
-                # since mmdet v2.0
-                # BG cat_id: num_class
-                scores = rpn_cls_score.softmax(dim=1)[:, :-1]
-            # filter scores, bbox_pred w.r.t. mask.
-            # anchors are filtered in get_anchors() beforehand.
-            scores = scores[mask]
-            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1,
-                                                                   4)[mask, :]
-            if scores.dim() == 0:
-                rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0)
-                anchors = anchors.unsqueeze(0)
-                scores = scores.unsqueeze(0)
-            # filter anchors, bbox_pred, scores w.r.t. scores
-            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
-                _, topk_inds = scores.topk(cfg.nms_pre)
-                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
-                anchors = anchors[topk_inds, :]
-                scores = scores[topk_inds]
-            # get proposals w.r.t. anchors and rpn_bbox_pred
-            proposals = self.bbox_coder.decode(
-                anchors, rpn_bbox_pred, max_shape=img_shape)
-            # filter out too small bboxes
-            if cfg.min_bbox_size > 0:
-                w = proposals[:, 2] - proposals[:, 0]
-                h = proposals[:, 3] - proposals[:, 1]
-                valid_inds = torch.nonzero(
-                    (w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size),
-                    as_tuple=False).squeeze()
-                proposals = proposals[valid_inds, :]
-                scores = scores[valid_inds]
-            # NMS in current level
-            proposals, _ = nms(proposals, scores, cfg.nms.iou_threshold)
-            proposals = proposals[:cfg.nms_post, :]
-            mlvl_proposals.append(proposals)
-        proposals = torch.cat(mlvl_proposals, 0)
-        if cfg.get('nms_across_levels', False):
-            # NMS across multi levels
-            proposals, _ = nms(proposals[:, :4], proposals[:, -1],
-                               cfg.nms.iou_threshold)
-            proposals = proposals[:cfg.max_per_img, :]
-        else:
-            scores = proposals[:, 4]
-            num = min(cfg.max_per_img, proposals.shape[0])
-            _, topk_inds = scores.topk(num)
-            proposals = proposals[topk_inds, :]
-        return proposals
diff --git a/spaces/Rongjiehuang/GenerSpeech/modules/__init__.py b/spaces/Rongjiehuang/GenerSpeech/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SAAZIZI/SummarizeAV/summarization_service/summarizer.py b/spaces/SAAZIZI/SummarizeAV/summarization_service/summarizer.py
deleted file mode 100644
index 09f0d496599763474a9dcc50f1fafa36a4595659..0000000000000000000000000000000000000000
--- a/spaces/SAAZIZI/SummarizeAV/summarization_service/summarizer.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import json
-import os
-
-from llama_index import (ServiceContext, get_response_synthesizer, Document)
-from llama_index.indices.document_summary import DocumentSummaryIndex
-from llama_index.llms import OpenAI, Replicate
-
-from config import output_path_transcription
-from logger import logger
-
-
-class TranscriptSummary:
-    def __init__(self, doc_id, model="default", llm_provider=None):
-        self.doc_id = doc_id
-        self.document = self.load_json_file_and_extract_text()
-        self.model = model
-        self.llm_provider = llm_provider
-
-        # Check for the presence of the openai_key
-        if self.llm_provider == "OpenAI":
-            self.llm = OpenAI(model=self.model, temperature=0.9)
-        elif self.llm_provider == "Replicate":
-            self.llm = Replicate(model=self.model)
-        else:
-            self.llm = "default"
-
-        # logger.info(f"LLM ")
-        logger.info(f"Model used: {self.model}")
-        logger.info(f"LLM Provider: {self.llm_provider}")
-
-        self.service_context = ServiceContext.from_defaults(llm=self.llm, chunk_size=1024)
-        self.response_synthesizer = get_response_synthesizer(response_mode="tree_summarize", use_async=True)
-        self.doc_summary_index = DocumentSummaryIndex.from_documents(self.document,
-                                                                     service_context=self.service_context,
-                                                                     response_synthesizer=self.response_synthesizer,
-                                                                     show_progress=True)
-        self.query_engine = self.doc_summary_index.as_query_engine(response_mode="tree_summarize", use_async=True)
-
-    def load_json_file_and_extract_text(self):
-        documents = []
-        file_path = os.path.join(output_path_transcription, f"{self.doc_id}.json")
-        with open(file_path, "r") as json_file:
-            json_data = json.load(json_file)
-            documents.append(Document(doc_id=self.doc_id, text=json_data["text"]))
-        return documents
-
-    def get_document_summary(self):
-        return self.doc_summary_index.get_document_summary(doc_id=self.doc_id)
-
-    def query_summary(self, query):
-        response = self.query_engine.query(query)
-        return response.response
-
-
-# Example usage:
-if __name__ == "__main__":
-    ts = TranscriptSummary(doc_id="youtube_5p248yoa3oE.json")
-    print(ts.get_document_summary())
-    result = ts.query_summary("What are the author's thoughts on the risks and benefits of AI for humanity")
-    print(result)
diff --git a/spaces/SIH/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/indexes/graph.py b/spaces/SIH/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/indexes/graph.py
deleted file mode 100644
index 0eddbb1de178f1d9df0335e7cd02c0eecd493a00..0000000000000000000000000000000000000000
--- a/spaces/SIH/Augmented-Retrieval-qa-ChatGPT/streamlit_langchain_chat/customized_langchain/indexes/graph.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from typing import List
-
-from langchain.indexes.graph import *
-from langchain.indexes.graph import GraphIndexCreator as OriginalGraphIndexCreator
-
-
-class GraphIndexCreator(OriginalGraphIndexCreator):
-    def from_texts(self, texts: List[str]) -> NetworkxEntityGraph:
-        """Create graph index from text."""
-        if self.llm is None:
-            raise ValueError("llm should not be None")
-        graph = self.graph_type()
-        chain = LLMChain(llm=self.llm, prompt=KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT)
-
-        for text in texts:
-            output = chain.predict(text=text)
-            knowledge = parse_triples(output)
-            for triple in knowledge:
-                graph.add_triple(triple)
-        return graph
diff --git a/spaces/Salesforce/EDICT/my_diffusers/configuration_utils.py b/spaces/Salesforce/EDICT/my_diffusers/configuration_utils.py
deleted file mode 100644
index 2a5c40f001dec427e6158fa59d92a0d4e226c302..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/configuration_utils.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-# Copyright (c) 2022, NVIDIA CORPORATION.  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.
-""" ConfigMixinuration base class and utilities."""
-import functools
-import inspect
-import json
-import os
-import re
-from collections import OrderedDict
-from typing import Any, Dict, Tuple, Union
-
-from huggingface_hub import hf_hub_download
-from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
-from requests import HTTPError
-
-from . import __version__
-from .utils import DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, logging
-
-
-logger = logging.get_logger(__name__)
-
-_re_configuration_file = re.compile(r"config\.(.*)\.json")
-
-
-class ConfigMixin:
-    r"""
-    Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all
-    methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with
-        - [`~ConfigMixin.from_config`]
-        - [`~ConfigMixin.save_config`]
-
-    Class attributes:
-        - **config_name** (`str`) -- A filename under which the config should stored when calling
-          [`~ConfigMixin.save_config`] (should be overridden by parent class).
-        - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
-          overridden by parent class).
-    """
-    config_name = None
-    ignore_for_config = []
-
-    def register_to_config(self, **kwargs):
-        if self.config_name is None:
-            raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
-        kwargs["_class_name"] = self.__class__.__name__
-        kwargs["_diffusers_version"] = __version__
-
-        for key, value in kwargs.items():
-            try:
-                setattr(self, key, value)
-            except AttributeError as err:
-                logger.error(f"Can't set {key} with value {value} for {self}")
-                raise err
-
-        if not hasattr(self, "_internal_dict"):
-            internal_dict = kwargs
-        else:
-            previous_dict = dict(self._internal_dict)
-            internal_dict = {**self._internal_dict, **kwargs}
-            logger.debug(f"Updating config from {previous_dict} to {internal_dict}")
-
-        self._internal_dict = FrozenDict(internal_dict)
-
-    def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
-        """
-        Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
-        [`~ConfigMixin.from_config`] class method.
-
-        Args:
-            save_directory (`str` or `os.PathLike`):
-                Directory where the configuration JSON file will be saved (will be created if it does not exist).
-        """
-        if os.path.isfile(save_directory):
-            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
-
-        os.makedirs(save_directory, exist_ok=True)
-
-        # If we save using the predefined names, we can load using `from_config`
-        output_config_file = os.path.join(save_directory, self.config_name)
-
-        self.to_json_file(output_config_file)
-        logger.info(f"ConfigMixinuration saved in {output_config_file}")
-
-    @classmethod
-    def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
-        r"""
-        Instantiate a Python class from a pre-defined JSON-file.
-
-        Parameters:
-            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
-                Can be either:
-
-                    - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
-                      organization name, like `google/ddpm-celebahq-256`.
-                    - A path to a *directory* containing model weights saved using [`~ConfigMixin.save_config`], e.g.,
-                      `./my_model_directory/`.
-
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory in which a downloaded pretrained model configuration should be cached if the
-                standard cache should not be used.
-            ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
-                Whether or not to raise an error if some of the weights from the checkpoint do not have the same size
-                as the weights of the model (if for instance, you are instantiating a model with 10 labels from a
-                checkpoint with 3 labels).
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
-                cached versions if they exist.
-            resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
-                file exists.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
-            output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only(`bool`, *optional*, defaults to `False`):
-                Whether or not to only look at local files (i.e., do not try to download the model).
-            use_auth_token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-                when running `transformers-cli login` (stored in `~/.huggingface`).
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-            mirror (`str`, *optional*):
-                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
-                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
-                Please refer to the mirror site for more information.
-
-        <Tip>
-
-        Passing `use_auth_token=True`` is required when you want to use a private model.
-
-        </Tip>
-
-        <Tip>
-
-        Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to
-        use this method in a firewalled environment.
-
-        </Tip>
-
-        """
-        config_dict = cls.get_config_dict(pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs)
-
-        init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
-
-        model = cls(**init_dict)
-
-        if return_unused_kwargs:
-            return model, unused_kwargs
-        else:
-            return model
-
-    @classmethod
-    def get_config_dict(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
-    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        force_download = kwargs.pop("force_download", False)
-        resume_download = kwargs.pop("resume_download", False)
-        proxies = kwargs.pop("proxies", None)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        revision = kwargs.pop("revision", None)
-        subfolder = kwargs.pop("subfolder", None)
-
-        user_agent = {"file_type": "config"}
-
-        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-
-        if cls.config_name is None:
-            raise ValueError(
-                "`self.config_name` is not defined. Note that one should not load a config from "
-                "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
-            )
-
-        if os.path.isfile(pretrained_model_name_or_path):
-            config_file = pretrained_model_name_or_path
-        elif os.path.isdir(pretrained_model_name_or_path):
-            if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
-                # Load from a PyTorch checkpoint
-                config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
-            elif subfolder is not None and os.path.isfile(
-                os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
-            ):
-                config_file = os.path.join(pretrained_model_name_or_path, subfolder, cls.config_name)
-            else:
-                raise EnvironmentError(
-                    f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
-                )
-        else:
-            try:
-                # Load from URL or cache if already cached
-                config_file = hf_hub_download(
-                    pretrained_model_name_or_path,
-                    filename=cls.config_name,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    resume_download=resume_download,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    user_agent=user_agent,
-                    subfolder=subfolder,
-                    revision=revision,
-                )
-
-            except RepositoryNotFoundError:
-                raise EnvironmentError(
-                    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
-                    " listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a"
-                    " token having permission to this repo with `use_auth_token` or log in with `huggingface-cli"
-                    " login` and pass `use_auth_token=True`."
-                )
-            except RevisionNotFoundError:
-                raise EnvironmentError(
-                    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for"
-                    " this model name. Check the model page at"
-                    f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
-                )
-            except EntryNotFoundError:
-                raise EnvironmentError(
-                    f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
-                )
-            except HTTPError as err:
-                raise EnvironmentError(
-                    "There was a specific connection error when trying to load"
-                    f" {pretrained_model_name_or_path}:\n{err}"
-                )
-            except ValueError:
-                raise EnvironmentError(
-                    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
-                    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
-                    f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
-                    " run the library in offline mode at"
-                    " 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
-                )
-            except EnvironmentError:
-                raise EnvironmentError(
-                    f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
-                    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
-                    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
-                    f"containing a {cls.config_name} file"
-                )
-
-        try:
-            # Load config dict
-            config_dict = cls._dict_from_json_file(config_file)
-        except (json.JSONDecodeError, UnicodeDecodeError):
-            raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
-
-        return config_dict
-
-    @classmethod
-    def extract_init_dict(cls, config_dict, **kwargs):
-        expected_keys = set(dict(inspect.signature(cls.__init__).parameters).keys())
-        expected_keys.remove("self")
-        # remove general kwargs if present in dict
-        if "kwargs" in expected_keys:
-            expected_keys.remove("kwargs")
-        # remove keys to be ignored
-        if len(cls.ignore_for_config) > 0:
-            expected_keys = expected_keys - set(cls.ignore_for_config)
-        init_dict = {}
-        for key in expected_keys:
-            if key in kwargs:
-                # overwrite key
-                init_dict[key] = kwargs.pop(key)
-            elif key in config_dict:
-                # use value from config dict
-                init_dict[key] = config_dict.pop(key)
-
-        unused_kwargs = config_dict.update(kwargs)
-
-        passed_keys = set(init_dict.keys())
-        if len(expected_keys - passed_keys) > 0:
-            logger.warning(
-                f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
-            )
-
-        return init_dict, unused_kwargs
-
-    @classmethod
-    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
-        with open(json_file, "r", encoding="utf-8") as reader:
-            text = reader.read()
-        return json.loads(text)
-
-    def __repr__(self):
-        return f"{self.__class__.__name__} {self.to_json_string()}"
-
-    @property
-    def config(self) -> Dict[str, Any]:
-        return self._internal_dict
-
-    def to_json_string(self) -> str:
-        """
-        Serializes this instance to a JSON string.
-
-        Returns:
-            `str`: String containing all the attributes that make up this configuration instance in JSON format.
-        """
-        config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
-        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path: Union[str, os.PathLike]):
-        """
-        Save this instance to a JSON file.
-
-        Args:
-            json_file_path (`str` or `os.PathLike`):
-                Path to the JSON file in which this configuration instance's parameters will be saved.
-        """
-        with open(json_file_path, "w", encoding="utf-8") as writer:
-            writer.write(self.to_json_string())
-
-
-class FrozenDict(OrderedDict):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        for key, value in self.items():
-            setattr(self, key, value)
-
-        self.__frozen = True
-
-    def __delitem__(self, *args, **kwargs):
-        raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
-
-    def setdefault(self, *args, **kwargs):
-        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
-
-    def pop(self, *args, **kwargs):
-        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
-
-    def update(self, *args, **kwargs):
-        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
-
-    def __setattr__(self, name, value):
-        if hasattr(self, "__frozen") and self.__frozen:
-            raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
-        super().__setattr__(name, value)
-
-    def __setitem__(self, name, value):
-        if hasattr(self, "__frozen") and self.__frozen:
-            raise Exception(f"You cannot use ``__setattr__`` on a {self.__class__.__name__} instance.")
-        super().__setitem__(name, value)
-
-
-def register_to_config(init):
-    r"""
-    Decorator to apply on the init of classes inheriting from [`ConfigMixin`] so that all the arguments are
-    automatically sent to `self.register_for_config`. To ignore a specific argument accepted by the init but that
-    shouldn't be registered in the config, use the `ignore_for_config` class variable
-
-    Warning: Once decorated, all private arguments (beginning with an underscore) are trashed and not sent to the init!
-    """
-
-    @functools.wraps(init)
-    def inner_init(self, *args, **kwargs):
-        # Ignore private kwargs in the init.
-        init_kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")}
-        init(self, *args, **init_kwargs)
-        if not isinstance(self, ConfigMixin):
-            raise RuntimeError(
-                f"`@register_for_config` was applied to {self.__class__.__name__} init method, but this class does "
-                "not inherit from `ConfigMixin`."
-            )
-
-        ignore = getattr(self, "ignore_for_config", [])
-        # Get positional arguments aligned with kwargs
-        new_kwargs = {}
-        signature = inspect.signature(init)
-        parameters = {
-            name: p.default for i, (name, p) in enumerate(signature.parameters.items()) if i > 0 and name not in ignore
-        }
-        for arg, name in zip(args, parameters.keys()):
-            new_kwargs[name] = arg
-
-        # Then add all kwargs
-        new_kwargs.update(
-            {
-                k: init_kwargs.get(k, default)
-                for k, default in parameters.items()
-                if k not in ignore and k not in new_kwargs
-            }
-        )
-        getattr(self, "register_to_config")(**new_kwargs)
-
-    return inner_init
diff --git a/spaces/Sanathkumar1603/hackathon/app/Hackathon_setup/face_recognition_model.py b/spaces/Sanathkumar1603/hackathon/app/Hackathon_setup/face_recognition_model.py
deleted file mode 100644
index f0cadcbf282653c01a314ada3525b49dc9833ed1..0000000000000000000000000000000000000000
--- a/spaces/Sanathkumar1603/hackathon/app/Hackathon_setup/face_recognition_model.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import math
-import torch
-import torchvision
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision import transforms
-# Add more imports if required
-
-# Sample Transformation function
-# YOUR CODE HERE for changing the Transformation values.
-trnscm = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()])
-
-##Example Network
-class Siamese(torch.nn.Module):
-    def __init__(self):
-        super(Siamese, self).__init__()
-
-        #YOUR CODE HERE
-        self.cnn1 = nn.Sequential(
-            nn.ReflectionPad2d(1),       #Pads the input tensor using the reflection of the input boundary, it similar to the padding.
-            nn.Conv2d(1, 4, kernel_size=3),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(4),
-
-            nn.ReflectionPad2d(1),
-            nn.Conv2d(4, 8, kernel_size=3),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(8),
-
-
-            nn.ReflectionPad2d(1),
-            nn.Conv2d(8, 8, kernel_size=3),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(8),
-        )
-
-        self.fc1 = nn.Sequential(
-            nn.Linear(8*100*100, 500),
-            nn.ReLU(inplace=True),
-
-            nn.Linear(500, 500),
-            nn.ReLU(inplace=True),
-
-            nn.Linear(500, 5))
-
-    # forward_once is for one image. This can be used while classifying the face images
-    def forward_once(self, x):
-        output = self.cnn1(x)
-        output = output.view(output.size()[0], -1)
-        output = self.fc1(output)
-        return output
-
-    def forward(self, input1, input2):
-        output1 = self.forward_once(input1)
-        output2 = self.forward_once(input2)
-        return output1, output2
-        
-    # def forward(self, x):
-    #     pass   # remove 'pass' once you have written your code
-        #YOUR CODE HERE
-        
-##########################################################################################################
-## Sample classification network (Specify if you are using a pytorch classifier during the training)    ##
-## classifier = nn.Sequential(nn.Linear(64, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear...)           ##
-##########################################################################################################
-
-# YOUR CODE HERE for pytorch classifier
-# Definition of classes as dictionary
-classes = ["",'person1','person2','person3','person4','person5','person6','person7']
\ No newline at end of file
diff --git a/spaces/Sandiago21/text-to-speech-french/README.md b/spaces/Sandiago21/text-to-speech-french/README.md
deleted file mode 100644
index 21c114980a44ad0938fd11036ce12ef57d88b6f8..0000000000000000000000000000000000000000
--- a/spaces/Sandiago21/text-to-speech-french/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: text-to-speech-french
-app_file: app.py
-sdk: gradio
-sdk_version: 3.36.0
----
diff --git a/spaces/Sangmin/OpenAI_TTS/app.py b/spaces/Sangmin/OpenAI_TTS/app.py
deleted file mode 100644
index f1a6109b1e695573427b81cf863fbcde1c4d0c11..0000000000000000000000000000000000000000
--- a/spaces/Sangmin/OpenAI_TTS/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import gradio as gr
-import os
-import tempfile
-from openai import OpenAI
-
-# Set an environment variable for key
-os.environ['OPENAI_API_KEY'] = os.environ.get('OPENAI_API_KEY')
-
-client = OpenAI() # add api_key
-
-def tts(text, model, voice):
-    response = client.audio.speech.create(
-        model=model, #"tts-1-1106","tts-1-hd-1106"
-        voice=voice, #'alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer'
-        input=text,
-    )
-
-    # Create a temp file to save the audio
-    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
-        temp_file.write(response.content)
-
-    # Get the file path of the temp file
-    temp_file_path = temp_file.name
-
-    return temp_file_path
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# <center> OpenAI Text-To-Speech API </center>")
-    gr.Markdown("<center>⭐️Brought to you by <a href='https://note.com/sangmin/n/n9813f2064a6a'>Chiomirai School</a>⭐️</center>")
-    gr.Markdown("<center>🚨Enter fewer than 4096 characters🚨</center>")
-    with gr.Row():
-      model = gr.Dropdown(choices=['tts-1-1106','tts-1-hd-1106'], label='Model', value='tts-1-1106')
-      voice = gr.Dropdown(choices=['alloy', 'echo', 'fable', 'onyx', 'nova', 'shimmer'], label='Voice Options', value='alloy')
-
-    text = gr.Textbox(label="Input text", placeholder="Input text and press the Text-To-Speech button or press Enter.")
-    btn = gr.Button("Text-To-Speech")
-    output_audio = gr.Audio(label="Speech Output")
-    
-    text.submit(fn=tts, inputs=[text, model, voice], outputs=output_audio, api_name="tts", concurrency_limit=None)
-    btn.click(fn=tts, inputs=[text, model, voice], outputs=output_audio, api_name="tts", concurrency_limit=None)
-
-demo.launch()
diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/liver abscesses.md b/spaces/SarthakSidhant/Go-Cattle/diseases/liver abscesses.md
deleted file mode 100644
index 6cd38045d6726d2f8b1e84c118715c3aba126e8d..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/liver abscesses.md	
+++ /dev/null
@@ -1,41 +0,0 @@
-## Liver abscesses
-
-**Information:** Liver abscesses are pockets of pus that form in the liver of cattle. They are caused by a variety of bacteria, including **Trueperella pyogenes**, **Actinomyces bovis**, and **Streptococcus spp.**
-
-**Symptoms:**
-
-* Weight loss
-* Poor growth
-* Anorexia
-* Jaundice
-* Abdominal pain
-* Diarrhea
-* Coughing
-* Fever
-* Death
-
-**Remedies:**
-
-* There is no specific cure for liver abscesses.
-* Treatment is usually supportive and may include:
-    * Deworming with anthelmintics
-    * Providing fluids and electrolytes
-    * Treating other underlying conditions
-    * Draining the abscesses
-    * Administering antibiotics
-
-**Causes:**
-
-* Liver abscesses are caused by a variety of bacteria, including **Trueperella pyogenes**, **Actinomyces bovis**, and **Streptococcus spp.**
-* These bacteria can enter the liver through cuts or bruises, or through the bloodstream.
-* Liver abscesses are more common in cattle that are stressed or malnourished.
-* Liver abscesses can also be caused by liver fluke infection.
-
-**Prevention:**
-
-* The best way to prevent liver abscesses is to control the bacteria that cause them.
-* Cattle should be dewormed regularly with anthelmintics.
-* Cattle should not be stressed or malnourished.
-* Cattle should not be grazed in wet and marshy areas.
-* Cattle should not be fed hay that has been cut from wet and marshy areas.
-
diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/mastitis.md b/spaces/SarthakSidhant/Go-Cattle/diseases/mastitis.md
deleted file mode 100644
index eca782e623df3162ba56ff34678ad1517e30b76d..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/mastitis.md
+++ /dev/null
@@ -1,38 +0,0 @@
-## Mastitis
-
-**Information:** Mastitis is an inflammation of the mammary gland in mammals, including cattle. It is caused by bacteria, viruses, or fungi. Mastitis can cause a variety of symptoms in affected animals, including swelling, redness, pain, and decreased milk production. In some cases, mastitis can also be fatal.
-
-**Symptoms:**
-
-* Swelling
-* Redness
-* Pain
-* Decreased milk production
-* Fever
-* Depression
-* Weight loss
-
-**Remedies:**
-
-* Mastitis can be treated with antibiotics, such as penicillin or tetracycline.
-* Treatment may take several weeks.
-* Animals that have been diagnosed with mastitis should be isolated from other animals to prevent the spread of the disease.
-
-**Causes:**
-
-* Mastitis is caused by bacteria, viruses, or fungi.
-* The most common cause of mastitis in cattle is bacteria called Staphylococcus aureus.
-* Bacteria can enter the mammary gland through cracks in the teat or through the milk ducts.
-* Viruses and fungi can also cause mastitis, but they are less common.
-
-**Prevention:**
-
-* The best way to prevent mastitis is to keep the mammary gland clean and dry.
-* Animals should be milked regularly and thoroughly.
-* Teats should be cleaned and disinfected before and after milking.
-* Animals should be vaccinated against mastitis.
-* Other preventive measures include:
-    * Maintaining good herd health practices
-    * Practicing biosecurity measures
-    * Testing animals for mastitis
-    * Disposing of infected animals and their tissues properly
diff --git a/spaces/ServerX/PorcoDiaz/infer/lib/train/process_ckpt.py b/spaces/ServerX/PorcoDiaz/infer/lib/train/process_ckpt.py
deleted file mode 100644
index 36d359d5f853452da4e1a696a84b8457b8386c29..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/infer/lib/train/process_ckpt.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import os
-import sys
-import traceback
-from collections import OrderedDict
-
-import torch
-
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-
-
-def savee(ckpt, sr, if_f0, name, epoch, version, hps):
-    try:
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        opt["config"] = [
-            hps.data.filter_length // 2 + 1,
-            32,
-            hps.model.inter_channels,
-            hps.model.hidden_channels,
-            hps.model.filter_channels,
-            hps.model.n_heads,
-            hps.model.n_layers,
-            hps.model.kernel_size,
-            hps.model.p_dropout,
-            hps.model.resblock,
-            hps.model.resblock_kernel_sizes,
-            hps.model.resblock_dilation_sizes,
-            hps.model.upsample_rates,
-            hps.model.upsample_initial_channel,
-            hps.model.upsample_kernel_sizes,
-            hps.model.spk_embed_dim,
-            hps.model.gin_channels,
-            hps.data.sampling_rate,
-        ]
-        opt["info"] = "%sepoch" % epoch
-        opt["sr"] = sr
-        opt["f0"] = if_f0
-        opt["version"] = version
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def show_info(path):
-    try:
-        a = torch.load(path, map_location="cpu")
-        return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
-            a.get("info", "None"),
-            a.get("sr", "None"),
-            a.get("f0", "None"),
-            a.get("version", "None"),
-        )
-    except:
-        return traceback.format_exc()
-
-
-def extract_small_model(path, name, sr, if_f0, info, version):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        if "model" in ckpt:
-            ckpt = ckpt["model"]
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        if sr == "40k":
-            opt["config"] = [
-                1025,
-                32,
-                192,
-                192,
-                768,
-                2,
-                6,
-                3,
-                0,
-                "1",
-                [3, 7, 11],
-                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                [10, 10, 2, 2],
-                512,
-                [16, 16, 4, 4],
-                109,
-                256,
-                40000,
-            ]
-        elif sr == "48k":
-            if version == "v1":
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 6, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-            else:
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [12, 10, 2, 2],
-                    512,
-                    [24, 20, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-        elif sr == "32k":
-            if version == "v1":
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 4, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-            else:
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 8, 2, 2],
-                    512,
-                    [20, 16, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-        if info == "":
-            info = "Extracted model."
-        opt["info"] = info
-        opt["version"] = version
-        opt["sr"] = sr
-        opt["f0"] = int(if_f0)
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def change_info(path, info, name):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        ckpt["info"] = info
-        if name == "":
-            name = os.path.basename(path)
-        torch.save(ckpt, "weights/%s" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def merge(path1, path2, alpha1, sr, f0, info, name, version):
-    try:
-
-        def extract(ckpt):
-            a = ckpt["model"]
-            opt = OrderedDict()
-            opt["weight"] = {}
-            for key in a.keys():
-                if "enc_q" in key:
-                    continue
-                opt["weight"][key] = a[key]
-            return opt
-
-        ckpt1 = torch.load(path1, map_location="cpu")
-        ckpt2 = torch.load(path2, map_location="cpu")
-        cfg = ckpt1["config"]
-        if "model" in ckpt1:
-            ckpt1 = extract(ckpt1)
-        else:
-            ckpt1 = ckpt1["weight"]
-        if "model" in ckpt2:
-            ckpt2 = extract(ckpt2)
-        else:
-            ckpt2 = ckpt2["weight"]
-        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
-            return "Fail to merge the models. The model architectures are not the same."
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt1.keys():
-            # try:
-            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
-                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key][:min_shape0].float())
-                    + (1 - alpha1) * (ckpt2[key][:min_shape0].float())
-                ).half()
-            else:
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
-                ).half()
-        # except:
-        #     pdb.set_trace()
-        opt["config"] = cfg
-        """
-        if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
-        elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
-        elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
-        """
-        opt["sr"] = sr
-        opt["f0"] = 1 if f0 == i18n("是") else 0
-        opt["version"] = version
-        opt["info"] = info
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
diff --git a/spaces/ServerX/PorcoDiaz/lib/infer_pack/models_onnx.py b/spaces/ServerX/PorcoDiaz/lib/infer_pack/models_onnx.py
deleted file mode 100644
index 963e67b29f828e9fdd096397952054fe77cf3d10..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/lib/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,819 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMsNSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        version,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        self.speaker_map = None
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/SeyedAli/Persian-Image-Captioning-1/README.md b/spaces/SeyedAli/Persian-Image-Captioning-1/README.md
deleted file mode 100644
index 00efadb4eca547e21b6752fd7d2222953d13d6f6..0000000000000000000000000000000000000000
--- a/spaces/SeyedAli/Persian-Image-Captioning-1/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Persian Image Captioning (Version 1)
-emoji: 🖼️➡️📝
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: mit
----
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sparkles-AI/design-look-a-likes/server.py b/spaces/Sparkles-AI/design-look-a-likes/server.py
deleted file mode 100644
index 3c56bdbf0214f4f01c40a5c72ae51cb659df91ad..0000000000000000000000000000000000000000
--- a/spaces/Sparkles-AI/design-look-a-likes/server.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import asyncio
-from concurrent.futures import ThreadPoolExecutor
-
-from fastapi import FastAPI, Response, Request, BackgroundTasks
-from starlette.middleware.cors import CORSMiddleware
-# import json
-# from load_data import get_design_data, get_design_resolutions_for_shop
-from starlette.responses import RedirectResponse
-
-from createlookalike import create_feature_files
-from schemas import Shop
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Allows all origins
-    allow_credentials=True,
-    allow_methods=["*"],  # Allows all methods
-    allow_headers=["*"],  # Allows all headers
-)
-
-
-API_TOKEN = '34dsadfFTGREGEFGE'
-
-
-
-
-
-# ---------------------------------------------------------------------------
-# - Token auth method -
-# ---------------------------------------------------------------------------
-def check_auth(authorization_header):
-    if authorization_header == API_TOKEN:
-        return True
-    else:
-        return False
-
-
-
-
-
-@app.get("/")
-async def root():
-    # return {"message": "where are you looking for?"}
-    response = RedirectResponse(url='/docs')
-    return response
-
-@app.get("/active")
-async def active(request: Request, response: Response):
-    authorization_token = request.headers.get("Authorization", None)
-    check_token = check_auth(authorization_token)
-    return {"application":"active"}
-
-
-
-@app.post("/lal_for_shop")
-async def submit_async_task(request: Request, response: Response,background_tasks: BackgroundTasks, shop: Shop):
-    # loop = asyncio.new_event_loop()
-    authorization_token = request.headers.get("Authorization", None)
-    check_token = check_auth(authorization_token)
-    if check_token:
-        # task = asyncio.create_task(create_feature_files(shop))
-        background_tasks.add_task(create_feature_files, shop)
-        return {"message": f"shop calculation for shop {shop.id} submitted"}
-    else:
-        print(authorization_token)
-        response.status_code = 401
-        return response
-
-
-
diff --git a/spaces/Sreeja123/memristor-based-neural-search-optimization-GUI/README.md b/spaces/Sreeja123/memristor-based-neural-search-optimization-GUI/README.md
deleted file mode 100644
index 2637ab4675061d5e135f8e97c3fd4beceb56b230..0000000000000000000000000000000000000000
--- a/spaces/Sreeja123/memristor-based-neural-search-optimization-GUI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Memristor Based Neural Search Optimization GUI
-emoji: 👀
-colorFrom: yellow
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/refbug.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/refbug.py
deleted file mode 100644
index 92e2eead347aa6607547a0b1e516f5021b8414c8..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/refbug.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""Minimal script to reproduce our nasty reference counting bug.
-
-The problem is related to https://github.com/ipython/ipython/issues/141
-
-The original fix for that appeared to work, but John D. Hunter found a
-matplotlib example which, when run twice in a row, would break.  The problem
-were references held by open figures to internals of Tkinter.
-
-This code reproduces the problem that John saw, without matplotlib.
-
-This script is meant to be called by other parts of the test suite that call it
-via %run as if it were executed interactively by the user. As of 2011-05-29,
-test_run.py calls it.
-"""
-
-#-----------------------------------------------------------------------------
-# Module imports
-#-----------------------------------------------------------------------------
-
-from IPython import get_ipython
-
-#-----------------------------------------------------------------------------
-# Globals
-#-----------------------------------------------------------------------------
-
-# This needs to be here because nose and other test runners will import
-# this module. Importing this module has potential side effects that we
-# want to prevent.
-if __name__ == '__main__':
-
-    ip = get_ipython()
-
-    if not '_refbug_cache' in ip.user_ns:
-        ip.user_ns['_refbug_cache'] = []
-
-
-    aglobal = 'Hello'
-    def f():
-        return aglobal
-
-    cache = ip.user_ns['_refbug_cache']
-    cache.append(f)
-
-    def call_f():
-        for func in cache:
-            print('lowercased:',func().lower())
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/click/_termui_impl.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/click/_termui_impl.py
deleted file mode 100644
index 4b979bcc1edcb33feb9b385156492221d92253dc..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/click/_termui_impl.py
+++ /dev/null
@@ -1,717 +0,0 @@
-"""
-This module contains implementations for the termui module. To keep the
-import time of Click down, some infrequently used functionality is
-placed in this module and only imported as needed.
-"""
-import contextlib
-import math
-import os
-import sys
-import time
-import typing as t
-from gettext import gettext as _
-
-from ._compat import _default_text_stdout
-from ._compat import CYGWIN
-from ._compat import get_best_encoding
-from ._compat import isatty
-from ._compat import open_stream
-from ._compat import strip_ansi
-from ._compat import term_len
-from ._compat import WIN
-from .exceptions import ClickException
-from .utils import echo
-
-V = t.TypeVar("V")
-
-if os.name == "nt":
-    BEFORE_BAR = "\r"
-    AFTER_BAR = "\n"
-else:
-    BEFORE_BAR = "\r\033[?25l"
-    AFTER_BAR = "\033[?25h\n"
-
-
-class ProgressBar(t.Generic[V]):
-    def __init__(
-        self,
-        iterable: t.Optional[t.Iterable[V]],
-        length: t.Optional[int] = None,
-        fill_char: str = "#",
-        empty_char: str = " ",
-        bar_template: str = "%(bar)s",
-        info_sep: str = "  ",
-        show_eta: bool = True,
-        show_percent: t.Optional[bool] = None,
-        show_pos: bool = False,
-        item_show_func: t.Optional[t.Callable[[t.Optional[V]], t.Optional[str]]] = None,
-        label: t.Optional[str] = None,
-        file: t.Optional[t.TextIO] = None,
-        color: t.Optional[bool] = None,
-        update_min_steps: int = 1,
-        width: int = 30,
-    ) -> None:
-        self.fill_char = fill_char
-        self.empty_char = empty_char
-        self.bar_template = bar_template
-        self.info_sep = info_sep
-        self.show_eta = show_eta
-        self.show_percent = show_percent
-        self.show_pos = show_pos
-        self.item_show_func = item_show_func
-        self.label = label or ""
-        if file is None:
-            file = _default_text_stdout()
-        self.file = file
-        self.color = color
-        self.update_min_steps = update_min_steps
-        self._completed_intervals = 0
-        self.width = width
-        self.autowidth = width == 0
-
-        if length is None:
-            from operator import length_hint
-
-            length = length_hint(iterable, -1)
-
-            if length == -1:
-                length = None
-        if iterable is None:
-            if length is None:
-                raise TypeError("iterable or length is required")
-            iterable = t.cast(t.Iterable[V], range(length))
-        self.iter = iter(iterable)
-        self.length = length
-        self.pos = 0
-        self.avg: t.List[float] = []
-        self.start = self.last_eta = time.time()
-        self.eta_known = False
-        self.finished = False
-        self.max_width: t.Optional[int] = None
-        self.entered = False
-        self.current_item: t.Optional[V] = None
-        self.is_hidden = not isatty(self.file)
-        self._last_line: t.Optional[str] = None
-
-    def __enter__(self) -> "ProgressBar":
-        self.entered = True
-        self.render_progress()
-        return self
-
-    def __exit__(self, exc_type, exc_value, tb):  # type: ignore
-        self.render_finish()
-
-    def __iter__(self) -> t.Iterator[V]:
-        if not self.entered:
-            raise RuntimeError("You need to use progress bars in a with block.")
-        self.render_progress()
-        return self.generator()
-
-    def __next__(self) -> V:
-        # Iteration is defined in terms of a generator function,
-        # returned by iter(self); use that to define next(). This works
-        # because `self.iter` is an iterable consumed by that generator,
-        # so it is re-entry safe. Calling `next(self.generator())`
-        # twice works and does "what you want".
-        return next(iter(self))
-
-    def render_finish(self) -> None:
-        if self.is_hidden:
-            return
-        self.file.write(AFTER_BAR)
-        self.file.flush()
-
-    @property
-    def pct(self) -> float:
-        if self.finished:
-            return 1.0
-        return min(self.pos / (float(self.length or 1) or 1), 1.0)
-
-    @property
-    def time_per_iteration(self) -> float:
-        if not self.avg:
-            return 0.0
-        return sum(self.avg) / float(len(self.avg))
-
-    @property
-    def eta(self) -> float:
-        if self.length is not None and not self.finished:
-            return self.time_per_iteration * (self.length - self.pos)
-        return 0.0
-
-    def format_eta(self) -> str:
-        if self.eta_known:
-            t = int(self.eta)
-            seconds = t % 60
-            t //= 60
-            minutes = t % 60
-            t //= 60
-            hours = t % 24
-            t //= 24
-            if t > 0:
-                return f"{t}d {hours:02}:{minutes:02}:{seconds:02}"
-            else:
-                return f"{hours:02}:{minutes:02}:{seconds:02}"
-        return ""
-
-    def format_pos(self) -> str:
-        pos = str(self.pos)
-        if self.length is not None:
-            pos += f"/{self.length}"
-        return pos
-
-    def format_pct(self) -> str:
-        return f"{int(self.pct * 100): 4}%"[1:]
-
-    def format_bar(self) -> str:
-        if self.length is not None:
-            bar_length = int(self.pct * self.width)
-            bar = self.fill_char * bar_length
-            bar += self.empty_char * (self.width - bar_length)
-        elif self.finished:
-            bar = self.fill_char * self.width
-        else:
-            chars = list(self.empty_char * (self.width or 1))
-            if self.time_per_iteration != 0:
-                chars[
-                    int(
-                        (math.cos(self.pos * self.time_per_iteration) / 2.0 + 0.5)
-                        * self.width
-                    )
-                ] = self.fill_char
-            bar = "".join(chars)
-        return bar
-
-    def format_progress_line(self) -> str:
-        show_percent = self.show_percent
-
-        info_bits = []
-        if self.length is not None and show_percent is None:
-            show_percent = not self.show_pos
-
-        if self.show_pos:
-            info_bits.append(self.format_pos())
-        if show_percent:
-            info_bits.append(self.format_pct())
-        if self.show_eta and self.eta_known and not self.finished:
-            info_bits.append(self.format_eta())
-        if self.item_show_func is not None:
-            item_info = self.item_show_func(self.current_item)
-            if item_info is not None:
-                info_bits.append(item_info)
-
-        return (
-            self.bar_template
-            % {
-                "label": self.label,
-                "bar": self.format_bar(),
-                "info": self.info_sep.join(info_bits),
-            }
-        ).rstrip()
-
-    def render_progress(self) -> None:
-        import shutil
-
-        if self.is_hidden:
-            # Only output the label as it changes if the output is not a
-            # TTY. Use file=stderr if you expect to be piping stdout.
-            if self._last_line != self.label:
-                self._last_line = self.label
-                echo(self.label, file=self.file, color=self.color)
-
-            return
-
-        buf = []
-        # Update width in case the terminal has been resized
-        if self.autowidth:
-            old_width = self.width
-            self.width = 0
-            clutter_length = term_len(self.format_progress_line())
-            new_width = max(0, shutil.get_terminal_size().columns - clutter_length)
-            if new_width < old_width:
-                buf.append(BEFORE_BAR)
-                buf.append(" " * self.max_width)  # type: ignore
-                self.max_width = new_width
-            self.width = new_width
-
-        clear_width = self.width
-        if self.max_width is not None:
-            clear_width = self.max_width
-
-        buf.append(BEFORE_BAR)
-        line = self.format_progress_line()
-        line_len = term_len(line)
-        if self.max_width is None or self.max_width < line_len:
-            self.max_width = line_len
-
-        buf.append(line)
-        buf.append(" " * (clear_width - line_len))
-        line = "".join(buf)
-        # Render the line only if it changed.
-
-        if line != self._last_line:
-            self._last_line = line
-            echo(line, file=self.file, color=self.color, nl=False)
-            self.file.flush()
-
-    def make_step(self, n_steps: int) -> None:
-        self.pos += n_steps
-        if self.length is not None and self.pos >= self.length:
-            self.finished = True
-
-        if (time.time() - self.last_eta) < 1.0:
-            return
-
-        self.last_eta = time.time()
-
-        # self.avg is a rolling list of length <= 7 of steps where steps are
-        # defined as time elapsed divided by the total progress through
-        # self.length.
-        if self.pos:
-            step = (time.time() - self.start) / self.pos
-        else:
-            step = time.time() - self.start
-
-        self.avg = self.avg[-6:] + [step]
-
-        self.eta_known = self.length is not None
-
-    def update(self, n_steps: int, current_item: t.Optional[V] = None) -> None:
-        """Update the progress bar by advancing a specified number of
-        steps, and optionally set the ``current_item`` for this new
-        position.
-
-        :param n_steps: Number of steps to advance.
-        :param current_item: Optional item to set as ``current_item``
-            for the updated position.
-
-        .. versionchanged:: 8.0
-            Added the ``current_item`` optional parameter.
-
-        .. versionchanged:: 8.0
-            Only render when the number of steps meets the
-            ``update_min_steps`` threshold.
-        """
-        if current_item is not None:
-            self.current_item = current_item
-
-        self._completed_intervals += n_steps
-
-        if self._completed_intervals >= self.update_min_steps:
-            self.make_step(self._completed_intervals)
-            self.render_progress()
-            self._completed_intervals = 0
-
-    def finish(self) -> None:
-        self.eta_known = False
-        self.current_item = None
-        self.finished = True
-
-    def generator(self) -> t.Iterator[V]:
-        """Return a generator which yields the items added to the bar
-        during construction, and updates the progress bar *after* the
-        yielded block returns.
-        """
-        # WARNING: the iterator interface for `ProgressBar` relies on
-        # this and only works because this is a simple generator which
-        # doesn't create or manage additional state. If this function
-        # changes, the impact should be evaluated both against
-        # `iter(bar)` and `next(bar)`. `next()` in particular may call
-        # `self.generator()` repeatedly, and this must remain safe in
-        # order for that interface to work.
-        if not self.entered:
-            raise RuntimeError("You need to use progress bars in a with block.")
-
-        if self.is_hidden:
-            yield from self.iter
-        else:
-            for rv in self.iter:
-                self.current_item = rv
-
-                # This allows show_item_func to be updated before the
-                # item is processed. Only trigger at the beginning of
-                # the update interval.
-                if self._completed_intervals == 0:
-                    self.render_progress()
-
-                yield rv
-                self.update(1)
-
-            self.finish()
-            self.render_progress()
-
-
-def pager(generator: t.Iterable[str], color: t.Optional[bool] = None) -> None:
-    """Decide what method to use for paging through text."""
-    stdout = _default_text_stdout()
-    if not isatty(sys.stdin) or not isatty(stdout):
-        return _nullpager(stdout, generator, color)
-    pager_cmd = (os.environ.get("PAGER", None) or "").strip()
-    if pager_cmd:
-        if WIN:
-            return _tempfilepager(generator, pager_cmd, color)
-        return _pipepager(generator, pager_cmd, color)
-    if os.environ.get("TERM") in ("dumb", "emacs"):
-        return _nullpager(stdout, generator, color)
-    if WIN or sys.platform.startswith("os2"):
-        return _tempfilepager(generator, "more <", color)
-    if hasattr(os, "system") and os.system("(less) 2>/dev/null") == 0:
-        return _pipepager(generator, "less", color)
-
-    import tempfile
-
-    fd, filename = tempfile.mkstemp()
-    os.close(fd)
-    try:
-        if hasattr(os, "system") and os.system(f'more "{filename}"') == 0:
-            return _pipepager(generator, "more", color)
-        return _nullpager(stdout, generator, color)
-    finally:
-        os.unlink(filename)
-
-
-def _pipepager(generator: t.Iterable[str], cmd: str, color: t.Optional[bool]) -> None:
-    """Page through text by feeding it to another program.  Invoking a
-    pager through this might support colors.
-    """
-    import subprocess
-
-    env = dict(os.environ)
-
-    # If we're piping to less we might support colors under the
-    # condition that
-    cmd_detail = cmd.rsplit("/", 1)[-1].split()
-    if color is None and cmd_detail[0] == "less":
-        less_flags = f"{os.environ.get('LESS', '')}{' '.join(cmd_detail[1:])}"
-        if not less_flags:
-            env["LESS"] = "-R"
-            color = True
-        elif "r" in less_flags or "R" in less_flags:
-            color = True
-
-    c = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, env=env)
-    stdin = t.cast(t.BinaryIO, c.stdin)
-    encoding = get_best_encoding(stdin)
-    try:
-        for text in generator:
-            if not color:
-                text = strip_ansi(text)
-
-            stdin.write(text.encode(encoding, "replace"))
-    except (OSError, KeyboardInterrupt):
-        pass
-    else:
-        stdin.close()
-
-    # Less doesn't respect ^C, but catches it for its own UI purposes (aborting
-    # search or other commands inside less).
-    #
-    # That means when the user hits ^C, the parent process (click) terminates,
-    # but less is still alive, paging the output and messing up the terminal.
-    #
-    # If the user wants to make the pager exit on ^C, they should set
-    # `LESS='-K'`. It's not our decision to make.
-    while True:
-        try:
-            c.wait()
-        except KeyboardInterrupt:
-            pass
-        else:
-            break
-
-
-def _tempfilepager(
-    generator: t.Iterable[str], cmd: str, color: t.Optional[bool]
-) -> None:
-    """Page through text by invoking a program on a temporary file."""
-    import tempfile
-
-    fd, filename = tempfile.mkstemp()
-    # TODO: This never terminates if the passed generator never terminates.
-    text = "".join(generator)
-    if not color:
-        text = strip_ansi(text)
-    encoding = get_best_encoding(sys.stdout)
-    with open_stream(filename, "wb")[0] as f:
-        f.write(text.encode(encoding))
-    try:
-        os.system(f'{cmd} "{filename}"')
-    finally:
-        os.close(fd)
-        os.unlink(filename)
-
-
-def _nullpager(
-    stream: t.TextIO, generator: t.Iterable[str], color: t.Optional[bool]
-) -> None:
-    """Simply print unformatted text.  This is the ultimate fallback."""
-    for text in generator:
-        if not color:
-            text = strip_ansi(text)
-        stream.write(text)
-
-
-class Editor:
-    def __init__(
-        self,
-        editor: t.Optional[str] = None,
-        env: t.Optional[t.Mapping[str, str]] = None,
-        require_save: bool = True,
-        extension: str = ".txt",
-    ) -> None:
-        self.editor = editor
-        self.env = env
-        self.require_save = require_save
-        self.extension = extension
-
-    def get_editor(self) -> str:
-        if self.editor is not None:
-            return self.editor
-        for key in "VISUAL", "EDITOR":
-            rv = os.environ.get(key)
-            if rv:
-                return rv
-        if WIN:
-            return "notepad"
-        for editor in "sensible-editor", "vim", "nano":
-            if os.system(f"which {editor} >/dev/null 2>&1") == 0:
-                return editor
-        return "vi"
-
-    def edit_file(self, filename: str) -> None:
-        import subprocess
-
-        editor = self.get_editor()
-        environ: t.Optional[t.Dict[str, str]] = None
-
-        if self.env:
-            environ = os.environ.copy()
-            environ.update(self.env)
-
-        try:
-            c = subprocess.Popen(f'{editor} "{filename}"', env=environ, shell=True)
-            exit_code = c.wait()
-            if exit_code != 0:
-                raise ClickException(
-                    _("{editor}: Editing failed").format(editor=editor)
-                )
-        except OSError as e:
-            raise ClickException(
-                _("{editor}: Editing failed: {e}").format(editor=editor, e=e)
-            ) from e
-
-    def edit(self, text: t.Optional[t.AnyStr]) -> t.Optional[t.AnyStr]:
-        import tempfile
-
-        if not text:
-            data = b""
-        elif isinstance(text, (bytes, bytearray)):
-            data = text
-        else:
-            if text and not text.endswith("\n"):
-                text += "\n"
-
-            if WIN:
-                data = text.replace("\n", "\r\n").encode("utf-8-sig")
-            else:
-                data = text.encode("utf-8")
-
-        fd, name = tempfile.mkstemp(prefix="editor-", suffix=self.extension)
-        f: t.BinaryIO
-
-        try:
-            with os.fdopen(fd, "wb") as f:
-                f.write(data)
-
-            # If the filesystem resolution is 1 second, like Mac OS
-            # 10.12 Extended, or 2 seconds, like FAT32, and the editor
-            # closes very fast, require_save can fail. Set the modified
-            # time to be 2 seconds in the past to work around this.
-            os.utime(name, (os.path.getatime(name), os.path.getmtime(name) - 2))
-            # Depending on the resolution, the exact value might not be
-            # recorded, so get the new recorded value.
-            timestamp = os.path.getmtime(name)
-
-            self.edit_file(name)
-
-            if self.require_save and os.path.getmtime(name) == timestamp:
-                return None
-
-            with open(name, "rb") as f:
-                rv = f.read()
-
-            if isinstance(text, (bytes, bytearray)):
-                return rv
-
-            return rv.decode("utf-8-sig").replace("\r\n", "\n")  # type: ignore
-        finally:
-            os.unlink(name)
-
-
-def open_url(url: str, wait: bool = False, locate: bool = False) -> int:
-    import subprocess
-
-    def _unquote_file(url: str) -> str:
-        from urllib.parse import unquote
-
-        if url.startswith("file://"):
-            url = unquote(url[7:])
-
-        return url
-
-    if sys.platform == "darwin":
-        args = ["open"]
-        if wait:
-            args.append("-W")
-        if locate:
-            args.append("-R")
-        args.append(_unquote_file(url))
-        null = open("/dev/null", "w")
-        try:
-            return subprocess.Popen(args, stderr=null).wait()
-        finally:
-            null.close()
-    elif WIN:
-        if locate:
-            url = _unquote_file(url.replace('"', ""))
-            args = f'explorer /select,"{url}"'
-        else:
-            url = url.replace('"', "")
-            wait_str = "/WAIT" if wait else ""
-            args = f'start {wait_str} "" "{url}"'
-        return os.system(args)
-    elif CYGWIN:
-        if locate:
-            url = os.path.dirname(_unquote_file(url).replace('"', ""))
-            args = f'cygstart "{url}"'
-        else:
-            url = url.replace('"', "")
-            wait_str = "-w" if wait else ""
-            args = f'cygstart {wait_str} "{url}"'
-        return os.system(args)
-
-    try:
-        if locate:
-            url = os.path.dirname(_unquote_file(url)) or "."
-        else:
-            url = _unquote_file(url)
-        c = subprocess.Popen(["xdg-open", url])
-        if wait:
-            return c.wait()
-        return 0
-    except OSError:
-        if url.startswith(("http://", "https://")) and not locate and not wait:
-            import webbrowser
-
-            webbrowser.open(url)
-            return 0
-        return 1
-
-
-def _translate_ch_to_exc(ch: str) -> t.Optional[BaseException]:
-    if ch == "\x03":
-        raise KeyboardInterrupt()
-
-    if ch == "\x04" and not WIN:  # Unix-like, Ctrl+D
-        raise EOFError()
-
-    if ch == "\x1a" and WIN:  # Windows, Ctrl+Z
-        raise EOFError()
-
-    return None
-
-
-if WIN:
-    import msvcrt
-
-    @contextlib.contextmanager
-    def raw_terminal() -> t.Iterator[int]:
-        yield -1
-
-    def getchar(echo: bool) -> str:
-        # The function `getch` will return a bytes object corresponding to
-        # the pressed character. Since Windows 10 build 1803, it will also
-        # return \x00 when called a second time after pressing a regular key.
-        #
-        # `getwch` does not share this probably-bugged behavior. Moreover, it
-        # returns a Unicode object by default, which is what we want.
-        #
-        # Either of these functions will return \x00 or \xe0 to indicate
-        # a special key, and you need to call the same function again to get
-        # the "rest" of the code. The fun part is that \u00e0 is
-        # "latin small letter a with grave", so if you type that on a French
-        # keyboard, you _also_ get a \xe0.
-        # E.g., consider the Up arrow. This returns \xe0 and then \x48. The
-        # resulting Unicode string reads as "a with grave" + "capital H".
-        # This is indistinguishable from when the user actually types
-        # "a with grave" and then "capital H".
-        #
-        # When \xe0 is returned, we assume it's part of a special-key sequence
-        # and call `getwch` again, but that means that when the user types
-        # the \u00e0 character, `getchar` doesn't return until a second
-        # character is typed.
-        # The alternative is returning immediately, but that would mess up
-        # cross-platform handling of arrow keys and others that start with
-        # \xe0. Another option is using `getch`, but then we can't reliably
-        # read non-ASCII characters, because return values of `getch` are
-        # limited to the current 8-bit codepage.
-        #
-        # Anyway, Click doesn't claim to do this Right(tm), and using `getwch`
-        # is doing the right thing in more situations than with `getch`.
-        func: t.Callable[[], str]
-
-        if echo:
-            func = msvcrt.getwche  # type: ignore
-        else:
-            func = msvcrt.getwch  # type: ignore
-
-        rv = func()
-
-        if rv in ("\x00", "\xe0"):
-            # \x00 and \xe0 are control characters that indicate special key,
-            # see above.
-            rv += func()
-
-        _translate_ch_to_exc(rv)
-        return rv
-
-else:
-    import tty
-    import termios
-
-    @contextlib.contextmanager
-    def raw_terminal() -> t.Iterator[int]:
-        f: t.Optional[t.TextIO]
-        fd: int
-
-        if not isatty(sys.stdin):
-            f = open("/dev/tty")
-            fd = f.fileno()
-        else:
-            fd = sys.stdin.fileno()
-            f = None
-
-        try:
-            old_settings = termios.tcgetattr(fd)
-
-            try:
-                tty.setraw(fd)
-                yield fd
-            finally:
-                termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
-                sys.stdout.flush()
-
-                if f is not None:
-                    f.close()
-        except termios.error:
-            pass
-
-    def getchar(echo: bool) -> str:
-        with raw_terminal() as fd:
-            ch = os.read(fd, 32).decode(get_best_encoding(sys.stdin), "replace")
-
-            if echo and isatty(sys.stdout):
-                sys.stdout.write(ch)
-
-            _translate_ch_to_exc(ch)
-            return ch
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/audio/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/audio/__init__.py
deleted file mode 100644
index a505ab05720b0db785caafb21b506d4bf6a4e29e..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/audio/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import types
-from typing import TYPE_CHECKING
-
-from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
-from docarray.typing.tensor.audio.audio_tensor import AudioTensor
-from docarray.utils._internal.misc import (
-    _get_path_from_docarray_root_level,
-    import_library,
-)
-
-if TYPE_CHECKING:
-    from docarray.typing.tensor.audio.audio_tensorflow_tensor import (  # noqa
-        AudioTensorFlowTensor,
-    )
-    from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor  # noqa
-
-__all__ = ['AudioNdArray', 'AudioTensor']
-
-
-def __getattr__(name: str):
-    lib: types.ModuleType
-    if name == 'AudioTorchTensor':
-        import_library('torch', raise_error=True)
-        import docarray.typing.tensor.audio.audio_torch_tensor as lib
-    elif name == 'AudioTensorFlowTensor':
-        import_library('tensorflow', raise_error=True)
-        import docarray.typing.tensor.audio.audio_tensorflow_tensor as lib
-    else:
-        raise ImportError(
-            f'cannot import name \'{name}\' from \'{_get_path_from_docarray_root_level(__file__)}\''
-        )
-
-    tensor_cls = getattr(lib, name)
-
-    if name not in __all__:
-        __all__.append(name)
-
-    return tensor_cls
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/csrc/deformable/deform_conv.h b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/csrc/deformable/deform_conv.h
deleted file mode 100644
index 965c1bfd47b58f9802d1c3fd69a5962517b2da61..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/csrc/deformable/deform_conv.h
+++ /dev/null
@@ -1,377 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#pragma once
-#include <torch/types.h>
-
-namespace detectron2 {
-
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-int deform_conv_forward_cuda(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor offset,
-    at::Tensor output,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step);
-
-int deform_conv_backward_input_cuda(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradInput,
-    at::Tensor gradOffset,
-    at::Tensor weight,
-    at::Tensor columns,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step);
-
-int deform_conv_backward_parameters_cuda(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradWeight, // at::Tensor gradBias,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    float scale,
-    int im2col_step);
-
-void modulated_deform_conv_cuda_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor output,
-    at::Tensor columns,
-    int kernel_h,
-    int kernel_w,
-    const int stride_h,
-    const int stride_w,
-    const int pad_h,
-    const int pad_w,
-    const int dilation_h,
-    const int dilation_w,
-    const int group,
-    const int deformable_group,
-    const bool with_bias);
-
-void modulated_deform_conv_cuda_backward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor columns,
-    at::Tensor grad_input,
-    at::Tensor grad_weight,
-    at::Tensor grad_bias,
-    at::Tensor grad_offset,
-    at::Tensor grad_mask,
-    at::Tensor grad_output,
-    int kernel_h,
-    int kernel_w,
-    int stride_h,
-    int stride_w,
-    int pad_h,
-    int pad_w,
-    int dilation_h,
-    int dilation_w,
-    int group,
-    int deformable_group,
-    const bool with_bias);
-
-#endif
-
-inline int deform_conv_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor offset,
-    at::Tensor output,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step) {
-  if (input.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_forward_cuda(
-        input,
-        weight,
-        offset,
-        output,
-        columns,
-        ones,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        im2col_step);
-#else
-    AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
-  }
-  AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline int deform_conv_backward_input(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradInput,
-    at::Tensor gradOffset,
-    at::Tensor weight,
-    at::Tensor columns,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    int im2col_step) {
-  if (gradOutput.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_backward_input_cuda(
-        input,
-        offset,
-        gradOutput,
-        gradInput,
-        gradOffset,
-        weight,
-        columns,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        im2col_step);
-#else
-    AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
-  }
-  AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline int deform_conv_backward_filter(
-    at::Tensor input,
-    at::Tensor offset,
-    at::Tensor gradOutput,
-    at::Tensor gradWeight, // at::Tensor gradBias,
-    at::Tensor columns,
-    at::Tensor ones,
-    int kW,
-    int kH,
-    int dW,
-    int dH,
-    int padW,
-    int padH,
-    int dilationW,
-    int dilationH,
-    int group,
-    int deformable_group,
-    float scale,
-    int im2col_step) {
-  if (gradOutput.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
-    return deform_conv_backward_parameters_cuda(
-        input,
-        offset,
-        gradOutput,
-        gradWeight,
-        columns,
-        ones,
-        kW,
-        kH,
-        dW,
-        dH,
-        padW,
-        padH,
-        dilationW,
-        dilationH,
-        group,
-        deformable_group,
-        scale,
-        im2col_step);
-#else
-    AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
-  }
-  AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline void modulated_deform_conv_forward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor output,
-    at::Tensor columns,
-    int kernel_h,
-    int kernel_w,
-    const int stride_h,
-    const int stride_w,
-    const int pad_h,
-    const int pad_w,
-    const int dilation_h,
-    const int dilation_w,
-    const int group,
-    const int deformable_group,
-    const bool with_bias) {
-  if (input.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!");
-    TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
-    return modulated_deform_conv_cuda_forward(
-        input,
-        weight,
-        bias,
-        ones,
-        offset,
-        mask,
-        output,
-        columns,
-        kernel_h,
-        kernel_w,
-        stride_h,
-        stride_w,
-        pad_h,
-        pad_w,
-        dilation_h,
-        dilation_w,
-        group,
-        deformable_group,
-        with_bias);
-#else
-    AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
-  }
-  AT_ERROR("This operator is not implemented on CPU");
-}
-
-inline void modulated_deform_conv_backward(
-    at::Tensor input,
-    at::Tensor weight,
-    at::Tensor bias,
-    at::Tensor ones,
-    at::Tensor offset,
-    at::Tensor mask,
-    at::Tensor columns,
-    at::Tensor grad_input,
-    at::Tensor grad_weight,
-    at::Tensor grad_bias,
-    at::Tensor grad_offset,
-    at::Tensor grad_mask,
-    at::Tensor grad_output,
-    int kernel_h,
-    int kernel_w,
-    int stride_h,
-    int stride_w,
-    int pad_h,
-    int pad_w,
-    int dilation_h,
-    int dilation_w,
-    int group,
-    int deformable_group,
-    const bool with_bias) {
-  if (grad_output.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    TORCH_CHECK(input.is_cuda(), "input tensor is not on GPU!");
-    TORCH_CHECK(weight.is_cuda(), "weight tensor is not on GPU!");
-    TORCH_CHECK(bias.is_cuda(), "bias tensor is not on GPU!");
-    TORCH_CHECK(offset.is_cuda(), "offset tensor is not on GPU!");
-    return modulated_deform_conv_cuda_backward(
-        input,
-        weight,
-        bias,
-        ones,
-        offset,
-        mask,
-        columns,
-        grad_input,
-        grad_weight,
-        grad_bias,
-        grad_offset,
-        grad_mask,
-        grad_output,
-        kernel_h,
-        kernel_w,
-        stride_h,
-        stride_w,
-        pad_h,
-        pad_w,
-        dilation_h,
-        dilation_w,
-        group,
-        deformable_group,
-        with_bias);
-#else
-    AT_ERROR("Detectron2 is not compiled with GPU support!");
-#endif
-  }
-  AT_ERROR("This operator is not implemented on CPU");
-}
-
-} // namespace detectron2
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/video/io.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/video/io.py
deleted file mode 100644
index 9879154227f640c262853b92c219461c6f67ee8e..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/video/io.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-from collections import OrderedDict
-
-import cv2
-from cv2 import (CAP_PROP_FOURCC, CAP_PROP_FPS, CAP_PROP_FRAME_COUNT,
-                 CAP_PROP_FRAME_HEIGHT, CAP_PROP_FRAME_WIDTH,
-                 CAP_PROP_POS_FRAMES, VideoWriter_fourcc)
-
-from annotator.uniformer.mmcv.utils import (check_file_exist, mkdir_or_exist, scandir,
-                        track_progress)
-
-
-class Cache:
-
-    def __init__(self, capacity):
-        self._cache = OrderedDict()
-        self._capacity = int(capacity)
-        if capacity <= 0:
-            raise ValueError('capacity must be a positive integer')
-
-    @property
-    def capacity(self):
-        return self._capacity
-
-    @property
-    def size(self):
-        return len(self._cache)
-
-    def put(self, key, val):
-        if key in self._cache:
-            return
-        if len(self._cache) >= self.capacity:
-            self._cache.popitem(last=False)
-        self._cache[key] = val
-
-    def get(self, key, default=None):
-        val = self._cache[key] if key in self._cache else default
-        return val
-
-
-class VideoReader:
-    """Video class with similar usage to a list object.
-
-    This video warpper class provides convenient apis to access frames.
-    There exists an issue of OpenCV's VideoCapture class that jumping to a
-    certain frame may be inaccurate. It is fixed in this class by checking
-    the position after jumping each time.
-    Cache is used when decoding videos. So if the same frame is visited for
-    the second time, there is no need to decode again if it is stored in the
-    cache.
-
-    :Example:
-
-    >>> import annotator.uniformer.mmcv as mmcv
-    >>> v = mmcv.VideoReader('sample.mp4')
-    >>> len(v)  # get the total frame number with `len()`
-    120
-    >>> for img in v:  # v is iterable
-    >>>     mmcv.imshow(img)
-    >>> v[5]  # get the 6th frame
-    """
-
-    def __init__(self, filename, cache_capacity=10):
-        # Check whether the video path is a url
-        if not filename.startswith(('https://', 'http://')):
-            check_file_exist(filename, 'Video file not found: ' + filename)
-        self._vcap = cv2.VideoCapture(filename)
-        assert cache_capacity > 0
-        self._cache = Cache(cache_capacity)
-        self._position = 0
-        # get basic info
-        self._width = int(self._vcap.get(CAP_PROP_FRAME_WIDTH))
-        self._height = int(self._vcap.get(CAP_PROP_FRAME_HEIGHT))
-        self._fps = self._vcap.get(CAP_PROP_FPS)
-        self._frame_cnt = int(self._vcap.get(CAP_PROP_FRAME_COUNT))
-        self._fourcc = self._vcap.get(CAP_PROP_FOURCC)
-
-    @property
-    def vcap(self):
-        """:obj:`cv2.VideoCapture`: The raw VideoCapture object."""
-        return self._vcap
-
-    @property
-    def opened(self):
-        """bool: Indicate whether the video is opened."""
-        return self._vcap.isOpened()
-
-    @property
-    def width(self):
-        """int: Width of video frames."""
-        return self._width
-
-    @property
-    def height(self):
-        """int: Height of video frames."""
-        return self._height
-
-    @property
-    def resolution(self):
-        """tuple: Video resolution (width, height)."""
-        return (self._width, self._height)
-
-    @property
-    def fps(self):
-        """float: FPS of the video."""
-        return self._fps
-
-    @property
-    def frame_cnt(self):
-        """int: Total frames of the video."""
-        return self._frame_cnt
-
-    @property
-    def fourcc(self):
-        """str: "Four character code" of the video."""
-        return self._fourcc
-
-    @property
-    def position(self):
-        """int: Current cursor position, indicating frame decoded."""
-        return self._position
-
-    def _get_real_position(self):
-        return int(round(self._vcap.get(CAP_PROP_POS_FRAMES)))
-
-    def _set_real_position(self, frame_id):
-        self._vcap.set(CAP_PROP_POS_FRAMES, frame_id)
-        pos = self._get_real_position()
-        for _ in range(frame_id - pos):
-            self._vcap.read()
-        self._position = frame_id
-
-    def read(self):
-        """Read the next frame.
-
-        If the next frame have been decoded before and in the cache, then
-        return it directly, otherwise decode, cache and return it.
-
-        Returns:
-            ndarray or None: Return the frame if successful, otherwise None.
-        """
-        # pos = self._position
-        if self._cache:
-            img = self._cache.get(self._position)
-            if img is not None:
-                ret = True
-            else:
-                if self._position != self._get_real_position():
-                    self._set_real_position(self._position)
-                ret, img = self._vcap.read()
-                if ret:
-                    self._cache.put(self._position, img)
-        else:
-            ret, img = self._vcap.read()
-        if ret:
-            self._position += 1
-        return img
-
-    def get_frame(self, frame_id):
-        """Get frame by index.
-
-        Args:
-            frame_id (int): Index of the expected frame, 0-based.
-
-        Returns:
-            ndarray or None: Return the frame if successful, otherwise None.
-        """
-        if frame_id < 0 or frame_id >= self._frame_cnt:
-            raise IndexError(
-                f'"frame_id" must be between 0 and {self._frame_cnt - 1}')
-        if frame_id == self._position:
-            return self.read()
-        if self._cache:
-            img = self._cache.get(frame_id)
-            if img is not None:
-                self._position = frame_id + 1
-                return img
-        self._set_real_position(frame_id)
-        ret, img = self._vcap.read()
-        if ret:
-            if self._cache:
-                self._cache.put(self._position, img)
-            self._position += 1
-        return img
-
-    def current_frame(self):
-        """Get the current frame (frame that is just visited).
-
-        Returns:
-            ndarray or None: If the video is fresh, return None, otherwise
-                return the frame.
-        """
-        if self._position == 0:
-            return None
-        return self._cache.get(self._position - 1)
-
-    def cvt2frames(self,
-                   frame_dir,
-                   file_start=0,
-                   filename_tmpl='{:06d}.jpg',
-                   start=0,
-                   max_num=0,
-                   show_progress=True):
-        """Convert a video to frame images.
-
-        Args:
-            frame_dir (str): Output directory to store all the frame images.
-            file_start (int): Filenames will start from the specified number.
-            filename_tmpl (str): Filename template with the index as the
-                placeholder.
-            start (int): The starting frame index.
-            max_num (int): Maximum number of frames to be written.
-            show_progress (bool): Whether to show a progress bar.
-        """
-        mkdir_or_exist(frame_dir)
-        if max_num == 0:
-            task_num = self.frame_cnt - start
-        else:
-            task_num = min(self.frame_cnt - start, max_num)
-        if task_num <= 0:
-            raise ValueError('start must be less than total frame number')
-        if start > 0:
-            self._set_real_position(start)
-
-        def write_frame(file_idx):
-            img = self.read()
-            if img is None:
-                return
-            filename = osp.join(frame_dir, filename_tmpl.format(file_idx))
-            cv2.imwrite(filename, img)
-
-        if show_progress:
-            track_progress(write_frame, range(file_start,
-                                              file_start + task_num))
-        else:
-            for i in range(task_num):
-                write_frame(file_start + i)
-
-    def __len__(self):
-        return self.frame_cnt
-
-    def __getitem__(self, index):
-        if isinstance(index, slice):
-            return [
-                self.get_frame(i)
-                for i in range(*index.indices(self.frame_cnt))
-            ]
-        # support negative indexing
-        if index < 0:
-            index += self.frame_cnt
-            if index < 0:
-                raise IndexError('index out of range')
-        return self.get_frame(index)
-
-    def __iter__(self):
-        self._set_real_position(0)
-        return self
-
-    def __next__(self):
-        img = self.read()
-        if img is not None:
-            return img
-        else:
-            raise StopIteration
-
-    next = __next__
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        self._vcap.release()
-
-
-def frames2video(frame_dir,
-                 video_file,
-                 fps=30,
-                 fourcc='XVID',
-                 filename_tmpl='{:06d}.jpg',
-                 start=0,
-                 end=0,
-                 show_progress=True):
-    """Read the frame images from a directory and join them as a video.
-
-    Args:
-        frame_dir (str): The directory containing video frames.
-        video_file (str): Output filename.
-        fps (float): FPS of the output video.
-        fourcc (str): Fourcc of the output video, this should be compatible
-            with the output file type.
-        filename_tmpl (str): Filename template with the index as the variable.
-        start (int): Starting frame index.
-        end (int): Ending frame index.
-        show_progress (bool): Whether to show a progress bar.
-    """
-    if end == 0:
-        ext = filename_tmpl.split('.')[-1]
-        end = len([name for name in scandir(frame_dir, ext)])
-    first_file = osp.join(frame_dir, filename_tmpl.format(start))
-    check_file_exist(first_file, 'The start frame not found: ' + first_file)
-    img = cv2.imread(first_file)
-    height, width = img.shape[:2]
-    resolution = (width, height)
-    vwriter = cv2.VideoWriter(video_file, VideoWriter_fourcc(*fourcc), fps,
-                              resolution)
-
-    def write_frame(file_idx):
-        filename = osp.join(frame_dir, filename_tmpl.format(file_idx))
-        img = cv2.imread(filename)
-        vwriter.write(img)
-
-    if show_progress:
-        track_progress(write_frame, range(start, end))
-    else:
-        for i in range(start, end):
-            write_frame(i)
-    vwriter.release()
diff --git a/spaces/Swying/text_generator/app.py b/spaces/Swying/text_generator/app.py
deleted file mode 100644
index 5cddff00f744f0a7ae3ee71844ec1ec2e081087b..0000000000000000000000000000000000000000
--- a/spaces/Swying/text_generator/app.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import gradio as gr
-from gradio.mix import Parallel
-
-title="My First Text Generator"
-description="Input text and submit."
-
-model1=gr.Interface.load("huggingface/gpt2")
-model2=gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-model3=gr.Interface.load("huggingface/bigscience/bloom-560m")
-
-gr.Parallel(model1, model2, model3, title=title, description=description).launch()
\ No newline at end of file
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/waymo_dataloader.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/waymo_dataloader.py
deleted file mode 100644
index 8b57d2fa75ed63ec606e70abe08813fb7e2d495b..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/waymo_dataloader.py
+++ /dev/null
@@ -1,490 +0,0 @@
-from typing import Tuple, List
-from cv2 import repeat
-from einops import rearrange, repeat
-from torch.utils.data import Dataset
-from torch.utils.data import DataLoader
-import torch
-from torch import Tensor
-import numpy as np
-import pickle
-import os
-
-from mmcv import Config
-
-
-class WaymoDataset(Dataset):
-    """
-    Dataset loader for custom preprocessed files of Waymo data.
-    Args:
-        path: path to the dataset directory
-        args: global settings
-    """
-
-    def __init__(self, cfg: Config, split: str, input_angle: bool = True):
-        super(WaymoDataset, self).__init__()
-        self.p_exchange_two_first = 1
-        if "val" in split.lower():
-            path = cfg.val_dataset_path
-        elif "test" in split.lower():
-            path = cfg.test_dataset_path
-        elif "sample" in split.lower():
-            path = cfg.sample_dataset_path
-        else:
-            path = cfg.train_dataset_path
-            self.p_exchange_two_first = cfg.p_exchange_two_first
-
-        self.file_list = [
-            os.path.join(path, name)
-            for name in os.listdir(path)
-            if os.path.isfile(os.path.join(path, name))
-        ]
-        self.normalize = cfg.normalize_angle
-        # self.load_dataset(path, 16)
-        # self.idx_list = list(self.dataset.keys())
-        self.input_angle = input_angle
-        self.hist_len = cfg.num_steps
-        self.fut_len = cfg.num_steps_future
-        self.time_len = self.hist_len + self.fut_len
-        self.min_num_obs = cfg.min_num_observation
-        self.max_size_lane = cfg.max_size_lane
-        self.random_rotation = cfg.random_rotation
-        self.random_translation = cfg.random_translation
-        self.angle_std = cfg.angle_std
-        self.translation_distance_std = cfg.translation_distance_std
-        self.max_num_agents = cfg.max_num_agents
-        self.max_num_objects = cfg.max_num_objects
-        self.state_dim = cfg.state_dim
-        self.map_state_dim = cfg.map_state_dim
-        self.dt = cfg.dt
-
-        if "val" in os.path.basename(path).lower():
-            self.dataset_size_limit = cfg.val_dataset_size_limit
-        else:
-            self.dataset_size_limit = cfg.train_dataset_size_limit
-
-    def __len__(self):
-        if self.dataset_size_limit is not None:
-            return min(len(self.file_list), self.dataset_size_limit)
-        else:
-            return len(self.file_list)
-
-    def __getitem__(
-        self, idx: int
-    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-        """
-        Get the item at index idx in the dataset. Normalize the scene and output absolute angle and position.
-        Returns:
-            trajectories, mask, mask_loss, lanes, mask_lanes, angle, mean_position
-        """
-        selected_file = self.file_list[idx]
-        with open(selected_file, "rb") as handle:
-            dataset = pickle.load(handle)
-        rel_state_all = dataset["traj"]
-        mask_all = dataset["mask_traj"]
-        mask_loss = dataset["mask_to_predict"]
-        rel_lane_all = dataset["lanes"]
-        mask_lane_all = dataset["mask_lanes"]
-        mean_pos = dataset["mean_pos"]
-        assert (
-            (
-                rel_state_all[self.hist_len + 5 :, :, :2][mask_all[self.hist_len + 5 :]]
-                != 0
-            )
-            .any(-1)
-            .all()
-        )
-        assert (
-            (
-                rel_state_all[self.hist_len + 5 :, :, :2][
-                    mask_loss[self.hist_len + 5 :]
-                ]
-                != 0
-            )
-            .any(-1)
-            .all()
-        )
-        if "lane_states" in dataset.keys():
-            lane_states = dataset["lane_states"]
-        else:
-            lane_states = None
-        if np.random.rand() > self.p_exchange_two_first:
-            rel_state_all[:, [0, 1]] = rel_state_all[:, [1, 0]]
-            mask_all[:, [0, 1]] = mask_all[:, [1, 0]]
-            mask_loss[:, [0, 1]] = mask_loss[:, [1, 0]]
-        assert (
-            (
-                rel_state_all[self.hist_len + 5 :, :, :2][mask_all[self.hist_len + 5 :]]
-                != 0
-            )
-            .any(-1)
-            .all()
-        )
-        assert (
-            (
-                rel_state_all[self.hist_len + 5 :, :, :2][
-                    mask_loss[self.hist_len + 5 :]
-                ]
-                != 0
-            )
-            .any(-1)
-            .all()
-        )
-        if self.normalize:
-            angle = rel_state_all[self.hist_len - 1, 1, 2]
-
-            if self.random_rotation:
-                if self.normalize:
-                    angle += np.random.normal(0, self.angle_std)
-                else:
-                    angle += np.random.uniform(-np.pi, np.pi)
-            if self.random_translation:
-                distance = (
-                    np.random.normal([0, 0], self.translation_distance_std, 2)
-                    * mask_all[self.hist_len - 1 : self.hist_len, :, None]
-                    - rel_state_all[self.hist_len - 1 : self.hist_len, 1:2, :2]
-                )
-            else:
-                distance = -rel_state_all[self.hist_len - 1 : self.hist_len, 1:2, :2]
-
-            rel_state_all[:, :, :2] += distance
-            rel_lane_all[:, :, :2] += distance
-            mean_pos += distance[0, 0, :]
-            rel_state_all = self.scene_rotation(rel_state_all, -angle)
-            rel_lane_all = self.scene_rotation(rel_lane_all, -angle)
-
-        else:
-            if self.random_translation:
-                distance = np.random.normal([0, 0], self.translation_distance_std, 2)
-                rel_state_all = (
-                    rel_state_all
-                    + mask_all[self.hist_len - 1 : self.hist_len, :, None] * distance
-                )
-                rel_lane_all = (
-                    rel_lane_all
-                    + mask_all[self.hist_len - 1 : self.hist_len, :, None] * distance
-                )
-
-            if self.random_rotation:
-                angle = np.random.uniform(0, 2 * np.pi)
-                rel_state_all = self.scene_rotation(rel_state_all, angle)
-                rel_lane_all = self.scene_rotation(rel_lane_all, angle)
-            else:
-                angle = 0
-        return (
-            rel_state_all,
-            mask_all,
-            mask_loss,
-            rel_lane_all,
-            mask_lane_all,
-            lane_states,
-            angle,
-            mean_pos,
-            idx,
-        )
-
-    @staticmethod
-    def scene_rotation(coor: np.ndarray, angle: float) -> np.ndarray:
-        """
-        Rotate all the coordinates with the same angle
-        Args:
-            coor: array of x, y coordinates
-            angle: radiants to rotate the coordinates by
-        Returns:
-            coor_rotated
-        """
-        rot_matrix = np.zeros((2, 2))
-        c = np.cos(angle)
-        s = np.sin(angle)
-        rot_matrix[0, 0] = c
-        rot_matrix[0, 1] = -s
-        rot_matrix[1, 0] = s
-        rot_matrix[1, 1] = c
-        coor[..., :2] = np.matmul(
-            rot_matrix, np.expand_dims(coor[..., :2], axis=-1)
-        ).squeeze(-1)
-        if coor.shape[-1] > 2:
-            coor[..., 2] += angle
-        if coor.shape[-1] >= 5:
-            coor[..., 3:5] = np.matmul(
-                rot_matrix, np.expand_dims(coor[..., 3:5], axis=-1)
-            ).squeeze(-1)
-        return coor
-
-    def fill_past(self, past, mask_past):
-        current_velocity = past[..., 0, 3:5]
-        for t in range(1, past.shape[-2]):
-            current_velocity = torch.where(
-                mask_past[..., t, None], past[..., t, 3:5], current_velocity
-            )
-            past[..., t, 3:5] = current_velocity
-            predicted_position = past[..., t - 1, :2] + current_velocity * self.dt
-            past[..., t, :2] = torch.where(
-                mask_past[..., t, None], past[..., t, :2], predicted_position
-            )
-        return past
-
-    def collate_fn(
-        self, samples: List
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
-        """
-        Assemble trajectories into batches with 0-padding.
-        Args:
-            samples: list of sampled trajectories (list of outputs of __getitem__)
-        Returns:
-            (starred dimensions have different values from one batch to the next but the ones with the same name are consistent within the batch)
-            batch : ((batch_size, num_agents*, num_steps, state_dim),           # past trajectories of all agents in the scene
-                     (batch_size, num_agents*, num_steps),                      # mask past False where past trajectories are padding data
-                     (batch_size, num_agents*, num_steps_future, state_dim),    # future trajectory
-                     (batch_size, num_agents*, num_steps_future),               # mask future False where future trajectories are padding data
-                     (batch_size, num_agents*, num_steps_future),               # mask loss False where future trajectories are not to be predicted
-                     (batch_size, num_objects*, object_seq_len*, map_state_dim),# map object sequences in the scene
-                     (batch_size, num_objects*, object_seq_len*),               # mask map False where map objects are padding data
-                     (batch_size, num_agents*, state_dim),                      # position offset of all agents relative to ego at present time
-                     (batch_size, num_steps, state_dim),                        # ego past trajectory
-                     (batch_size, num_steps_future, state_dim))                 # ego future trajectory
-        """
-        max_n_vehicle = 50
-        max_n_lanes = 0
-        for (
-            coor,
-            mask,
-            mask_loss,
-            lanes,
-            mask_lanes,
-            lane_states,
-            mean_angle,
-            mean_pos,
-            idx,
-        ) in samples:
-            # time_len_coor = self._count_last_obs(coor, hist_len)
-            # num_vehicle = np.sum(time_len_coor > self.min_num_obs)
-            num_vehicle = coor.shape[1]
-            num_lanes = lanes.shape[1]
-            max_n_vehicle = max(num_vehicle, max_n_vehicle)
-            max_n_lanes = max(num_lanes, max_n_lanes)
-        if max_n_vehicle <= 0:
-            raise RuntimeError
-        data_batch = np.zeros(
-            [self.time_len, len(samples), max_n_vehicle, self.state_dim]
-        )
-        mask_batch = np.zeros([self.time_len, len(samples), max_n_vehicle])
-        mask_loss_batch = np.zeros([self.time_len, len(samples), max_n_vehicle])
-        lane_batch = np.zeros(
-            [self.max_size_lane, len(samples), max_n_lanes, self.map_state_dim]
-        )
-        mask_lane_batch = np.zeros([self.max_size_lane, len(samples), max_n_lanes])
-        mean_angle_batch = np.zeros([len(samples)])
-        mean_pos_batch = np.zeros([len(samples), 2])
-        tag_list = np.zeros([len(samples)])
-        idx_list = [0 for _ in range(len(samples))]
-
-        for sample_ind, (
-            coor,
-            mask,
-            mask_loss,
-            lanes,
-            mask_lanes,
-            lane_states,
-            mean_angle,
-            mean_pos,
-            idx,
-        ) in enumerate(samples):
-            data_batch[:, sample_ind, : coor.shape[1], :] = coor[: self.time_len, :, :]
-            mask_batch[:, sample_ind, : mask.shape[1]] = mask[: self.time_len, :]
-            mask_loss_batch[:, sample_ind, : mask.shape[1]] = mask_loss[
-                : self.time_len, :
-            ]
-            lane_batch[: lanes.shape[0], sample_ind, : lanes.shape[1], :2] = lanes
-            if lane_states is not None:
-                lane_states = repeat(
-                    lane_states[:, : self.hist_len],
-                    "objects time features -> one objects (time features)",
-                    one=1,
-                )
-                lane_batch[
-                    : lanes.shape[0], sample_ind, : lanes.shape[1], 2:
-                ] = lane_states
-            mask_lane_batch[
-                : mask_lanes.shape[0], sample_ind, : mask_lanes.shape[1]
-            ] = mask_lanes
-            mean_angle_batch[sample_ind] = mean_angle
-            mean_pos_batch[sample_ind, :] = mean_pos
-            # tag_list[sample_ind] = self.dataset[idx]["tag"]
-            idx_list[sample_ind] = idx
-
-        data_batch = torch.from_numpy(data_batch.astype("float32"))
-        mask_batch = torch.from_numpy(mask_batch.astype("bool"))
-        lane_batch = torch.from_numpy(lane_batch.astype("float32"))
-        mask_lane_batch = torch.from_numpy(mask_lane_batch.astype("bool"))
-        mean_pos_batch = torch.from_numpy(mean_pos_batch.astype("float32"))
-        mask_loss_batch = torch.from_numpy(mask_loss_batch.astype("bool"))
-
-        data_batch = rearrange(
-            data_batch, "time batch agents features -> batch agents time features"
-        )
-        mask_batch = rearrange(mask_batch, "time batch agents -> batch agents time")
-        mask_loss_batch = rearrange(
-            mask_loss_batch, "time batch agents -> batch agents time"
-        )
-        lane_batch = rearrange(
-            lane_batch,
-            "object_seq_len batch objects features-> batch objects object_seq_len features",
-        )
-        mask_lane_batch = rearrange(
-            mask_lane_batch,
-            "object_seq_len batch objects -> batch objects object_seq_len",
-        )
-
-        # The two first agents are the ones interacting, others are sorted by distance from the first agent
-        # Objects are also sorted by distance from the first agent
-        # Therefore, the limits in number, max_num_agents and max_num_objects can be seen as adaptative distance limits.
-
-        if not self.input_angle:
-            data_batch = torch.cat((data_batch[..., :2], data_batch[..., 3:]), dim=-1)
-        traj_past = data_batch[:, : self.max_num_agents, : self.hist_len, :]
-        mask_past = mask_batch[:, : self.max_num_agents, : self.hist_len]
-        traj_fut = data_batch[
-            :, : self.max_num_agents, self.hist_len : self.hist_len + self.fut_len, :
-        ]
-        mask_fut = mask_batch[
-            :, : self.max_num_agents, self.hist_len : self.hist_len + self.fut_len
-        ]
-        ego_past = data_batch[:, 0:1, : self.hist_len, :]
-        ego_fut = data_batch[:, 0:1, self.hist_len : self.hist_len + self.fut_len, :]
-
-        lane_batch = lane_batch[:, : self.max_num_objects]
-        mask_lane_batch = mask_lane_batch[:, : self.max_num_objects]
-
-        # Define what to predict (could be from Waymo's label of what to predict or the other agent that interacts with the ego...)
-        mask_loss_batch = torch.logical_and(
-            mask_loss_batch[
-                :, : self.max_num_agents, self.hist_len : self.hist_len + self.fut_len
-            ],
-            mask_past.any(-1, keepdim=True),
-        )
-        # Remove all other agents so the model should only predict the first one
-        mask_loss_batch[:, 0] = False
-        mask_loss_batch[:, 2:] = False
-
-        # Normalize...
-        # traj_past = self.fill_past(traj_past, mask_past)
-        dynamic_state_size = 5 if self.input_angle else 4
-        offset_batch = traj_past[..., -1, :dynamic_state_size].clone()
-        traj_past[..., :dynamic_state_size] = traj_past[
-            ..., :dynamic_state_size
-        ] - offset_batch.unsqueeze(-2)
-        traj_fut[..., :dynamic_state_size] = traj_fut[
-            ..., :dynamic_state_size
-        ] - offset_batch.unsqueeze(-2)
-
-        return (
-            traj_past,
-            mask_past,
-            traj_fut,
-            mask_fut,
-            mask_loss_batch,
-            lane_batch,
-            mask_lane_batch,
-            offset_batch,
-            ego_past,
-            ego_fut,
-        )
-
-
-class WaymoDataloaders:
-    def __init__(self, cfg: Config) -> None:
-        self.cfg = cfg
-
-    def sample_dataloader(self) -> DataLoader:
-        """Setup and return sample DataLoader
-
-        Returns:
-            DataLoader: sample DataLoader
-        """
-        dataset = WaymoDataset(self.cfg, "sample")
-        sample_loader = DataLoader(
-            dataset=dataset,
-            batch_size=self.cfg.batch_size,
-            shuffle=False,
-            num_workers=self.cfg.num_workers,
-            collate_fn=dataset.collate_fn,
-            drop_last=True,
-        )
-        return sample_loader
-
-    def val_dataloader(
-        self, drop_last=True, shuffle=False, input_angle=True
-    ) -> DataLoader:
-        """Setup and return validation DataLoader
-
-        Returns:
-            DataLoader: validation DataLoader
-        """
-        dataset = WaymoDataset(self.cfg, "val", input_angle)
-        val_loader = DataLoader(
-            dataset=dataset,
-            batch_size=self.cfg.batch_size,
-            shuffle=shuffle,
-            num_workers=self.cfg.num_workers,
-            collate_fn=dataset.collate_fn,
-            drop_last=drop_last,
-        )
-        torch.cuda.empty_cache()
-        return val_loader
-
-    def train_dataloader(
-        self, drop_last=True, shuffle=True, input_angle=True
-    ) -> DataLoader:
-        """Setup and return training DataLoader
-
-        Returns:
-            DataLoader: training DataLoader
-        """
-        dataset = WaymoDataset(self.cfg, "train", input_angle)
-        train_loader = DataLoader(
-            dataset=dataset,
-            batch_size=self.cfg.batch_size,
-            shuffle=shuffle,
-            num_workers=self.cfg.num_workers,
-            collate_fn=dataset.collate_fn,
-            drop_last=drop_last,
-        )
-        torch.cuda.empty_cache()
-        return train_loader
-
-    def test_dataloader(self) -> DataLoader:
-        """Setup and return test DataLoader
-
-        Returns:
-            DataLoader: test DataLoader
-        """
-        raise NotImplementedError("The waymo dataloader cannot load test samples yet.")
-
-    @staticmethod
-    def unnormalize_trajectory(
-        input: torch.Tensor, offset: torch.Tensor
-    ) -> torch.Tensor:
-        """Unnormalize trajectory by adding offset to input
-
-        Args:
-            input : (..., (n_sample), num_steps_future, state_dim) tensor of future
-            trajectory y
-            offset : (..., state_dim) tensor of offset to add to y
-
-        Returns:
-            Unnormalized trajectory that has the same size as input
-        """
-        assert offset.ndim == 3
-        batch_size, num_agents = offset.shape[:2]
-        offset_state_dim = offset.shape[-1]
-        assert offset_state_dim <= input.shape[-1]
-        assert input.shape[0] == batch_size
-        assert input.shape[1] == num_agents
-        input_copy = input.clone()
-
-        input_copy[..., :offset_state_dim] = input_copy[
-            ..., :offset_state_dim
-        ] + offset[..., : input.shape[-1]].reshape(
-            [batch_size, num_agents, *([1] * (input.ndim - 3)), offset_state_dim]
-        )
-        return input_copy
diff --git a/spaces/TandCAcceptMe/face-swap-docker/roop/face_helper.py b/spaces/TandCAcceptMe/face-swap-docker/roop/face_helper.py
deleted file mode 100644
index 290d85ff1fd4c6b8db500dfe33c5b79b89a5aa5c..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/roop/face_helper.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import threading
-from typing import Any
-import insightface
-
-import roop.globals
-from roop.typing import Frame, Face
-
-import cv2
-from PIL import Image
-from roop.capturer import get_video_frame
-from roop.utilities import resolve_relative_path, conditional_download
-
-FACE_ANALYSER = None
-THREAD_LOCK_ANALYSER = threading.Lock()
-THREAD_LOCK_SWAPPER = threading.Lock()
-FACE_SWAPPER = None
-
-
-def get_face_analyser() -> Any:
-    global FACE_ANALYSER
-
-    with THREAD_LOCK_ANALYSER:
-        if FACE_ANALYSER is None:
-            FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=roop.globals.execution_providers)
-            FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
-    return FACE_ANALYSER
-
-
-def get_one_face(frame: Frame) -> Any:
-    try:
-        face = get_face_analyser().get(frame)
-        return min(face, key=lambda x: x.bbox[0])
-    except ValueError:
-        return None
-
-
-def get_many_faces(frame: Frame) -> Any:
-    try:
-        faces = get_face_analyser().get(frame)
-        return sorted(faces, key = lambda x : x.bbox[0])
-    except IndexError:
-        return None
-
-def extract_face_images(source_filename, video_info):
-    face_data = []
-    source_image = None
-    
-    if video_info[0]:
-        frame = get_video_frame(source_filename, video_info[1])
-        if frame is not None:
-            source_image = frame
-        else:
-            return face_data
-    else:
-        source_image = cv2.imread(source_filename)
-
-        
-    faces = get_many_faces(source_image)
-
-    i = 0
-    for face in faces:
-        (startX, startY, endX, endY) = face['bbox'].astype("int")
-        face_temp = source_image[startY:endY, startX:endX]
-        if face_temp.size < 1:
-            continue
-        i += 1
-        face_data.append([face, face_temp])
-    return face_data
-
-
-
-
-def get_face_swapper() -> Any:
-    global FACE_SWAPPER
-
-    with THREAD_LOCK_SWAPPER:
-        if FACE_SWAPPER is None:
-            model_path = resolve_relative_path('../models/inswapper_128.onnx')
-            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.execution_providers)
-    return FACE_SWAPPER
-
-
-def pre_check() -> bool:
-    download_directory_path = resolve_relative_path('../models')
-    conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx'])
-    return True
-
-
-def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
diff --git a/spaces/TestingCompany/ChatPDF/README.md b/spaces/TestingCompany/ChatPDF/README.md
deleted file mode 100644
index a45a2597ef12c417c3d3ee59b809fe339c2dcc53..0000000000000000000000000000000000000000
--- a/spaces/TestingCompany/ChatPDF/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatPDF
-emoji: 🏆
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TheStinger/Ilaria_RVC/rmvpe.py b/spaces/TheStinger/Ilaria_RVC/rmvpe.py
deleted file mode 100644
index 3ad346141340e03bdbaa20121e1ed435bb3da57a..0000000000000000000000000000000000000000
--- a/spaces/TheStinger/Ilaria_RVC/rmvpe.py
+++ /dev/null
@@ -1,432 +0,0 @@
-import sys, torch, numpy as np, traceback, pdb
-import torch.nn as nn
-from time import time as ttime
-import torch.nn.functional as F
-
-
-class BiGRU(nn.Module):
-    def __init__(self, input_features, hidden_features, num_layers):
-        super(BiGRU, self).__init__()
-        self.gru = nn.GRU(
-            input_features,
-            hidden_features,
-            num_layers=num_layers,
-            batch_first=True,
-            bidirectional=True,
-        )
-
-    def forward(self, x):
-        return self.gru(x)[0]
-
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, momentum=0.01):
-        super(ConvBlockRes, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-            nn.Conv2d(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-    def forward(self, x):
-        if self.is_shortcut:
-            return self.conv(x) + self.shortcut(x)
-        else:
-            return self.conv(x) + x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        in_size,
-        n_encoders,
-        kernel_size,
-        n_blocks,
-        out_channels=16,
-        momentum=0.01,
-    ):
-        super(Encoder, self).__init__()
-        self.n_encoders = n_encoders
-        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
-        self.layers = nn.ModuleList()
-        self.latent_channels = []
-        for i in range(self.n_encoders):
-            self.layers.append(
-                ResEncoderBlock(
-                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
-                )
-            )
-            self.latent_channels.append([out_channels, in_size])
-            in_channels = out_channels
-            out_channels *= 2
-            in_size //= 2
-        self.out_size = in_size
-        self.out_channel = out_channels
-
-    def forward(self, x):
-        concat_tensors = []
-        x = self.bn(x)
-        for i in range(self.n_encoders):
-            _, x = self.layers[i](x)
-            concat_tensors.append(_)
-        return x, concat_tensors
-
-
-class ResEncoderBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
-    ):
-        super(ResEncoderBlock, self).__init__()
-        self.n_blocks = n_blocks
-        self.conv = nn.ModuleList()
-        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
-        self.kernel_size = kernel_size
-        if self.kernel_size is not None:
-            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
-
-    def forward(self, x):
-        for i in range(self.n_blocks):
-            x = self.conv[i](x)
-        if self.kernel_size is not None:
-            return x, self.pool(x)
-        else:
-            return x
-
-
-class Intermediate(nn.Module):  #
-    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
-        super(Intermediate, self).__init__()
-        self.n_inters = n_inters
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
-        )
-        for i in range(self.n_inters - 1):
-            self.layers.append(
-                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
-            )
-
-    def forward(self, x):
-        for i in range(self.n_inters):
-            x = self.layers[i](x)
-        return x
-
-
-class ResDecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
-        super(ResDecoderBlock, self).__init__()
-        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
-        self.n_blocks = n_blocks
-        self.conv1 = nn.Sequential(
-            nn.ConvTranspose2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=stride,
-                padding=(1, 1),
-                output_padding=out_padding,
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        self.conv2 = nn.ModuleList()
-        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
-
-    def forward(self, x, concat_tensor):
-        x = self.conv1(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        for i in range(self.n_blocks):
-            x = self.conv2[i](x)
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_decoders = n_decoders
-        for i in range(self.n_decoders):
-            out_channels = in_channels // 2
-            self.layers.append(
-                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
-            )
-            in_channels = out_channels
-
-    def forward(self, x, concat_tensors):
-        for i in range(self.n_decoders):
-            x = self.layers[i](x, concat_tensors[-1 - i])
-        return x
-
-
-class DeepUnet(nn.Module):
-    def __init__(
-        self,
-        kernel_size,
-        n_blocks,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(DeepUnet, self).__init__()
-        self.encoder = Encoder(
-            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
-        )
-        self.intermediate = Intermediate(
-            self.encoder.out_channel // 2,
-            self.encoder.out_channel,
-            inter_layers,
-            n_blocks,
-        )
-        self.decoder = Decoder(
-            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
-        )
-
-    def forward(self, x):
-        x, concat_tensors = self.encoder(x)
-        x = self.intermediate(x)
-        x = self.decoder(x, concat_tensors)
-        return x
-
-
-class E2E(nn.Module):
-    def __init__(
-        self,
-        n_blocks,
-        n_gru,
-        kernel_size,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(E2E, self).__init__()
-        self.unet = DeepUnet(
-            kernel_size,
-            n_blocks,
-            en_de_layers,
-            inter_layers,
-            in_channels,
-            en_out_channels,
-        )
-        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
-        if n_gru:
-            self.fc = nn.Sequential(
-                BiGRU(3 * 128, 256, n_gru),
-                nn.Linear(512, 360),
-                nn.Dropout(0.25),
-                nn.Sigmoid(),
-            )
-        else:
-            self.fc = nn.Sequential(
-                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        return x
-
-
-from librosa.filters import mel
-
-
-class MelSpectrogram(torch.nn.Module):
-    def __init__(
-        self,
-        is_half,
-        n_mel_channels,
-        sampling_rate,
-        win_length,
-        hop_length,
-        n_fft=None,
-        mel_fmin=0,
-        mel_fmax=None,
-        clamp=1e-5,
-    ):
-        super().__init__()
-        n_fft = win_length if n_fft is None else n_fft
-        self.hann_window = {}
-        mel_basis = mel(
-            sr=sampling_rate,
-            n_fft=n_fft,
-            n_mels=n_mel_channels,
-            fmin=mel_fmin,
-            fmax=mel_fmax,
-            htk=True,
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-        self.n_fft = win_length if n_fft is None else n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.sampling_rate = sampling_rate
-        self.n_mel_channels = n_mel_channels
-        self.clamp = clamp
-        self.is_half = is_half
-
-    def forward(self, audio, keyshift=0, speed=1, center=True):
-        factor = 2 ** (keyshift / 12)
-        n_fft_new = int(np.round(self.n_fft * factor))
-        win_length_new = int(np.round(self.win_length * factor))
-        hop_length_new = int(np.round(self.hop_length * speed))
-        keyshift_key = str(keyshift) + "_" + str(audio.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
-                audio.device
-            )
-        fft = torch.stft(
-            audio,
-            n_fft=n_fft_new,
-            hop_length=hop_length_new,
-            win_length=win_length_new,
-            window=self.hann_window[keyshift_key],
-            center=center,
-            return_complex=True,
-        )
-        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
-        if keyshift != 0:
-            size = self.n_fft // 2 + 1
-            resize = magnitude.size(1)
-            if resize < size:
-                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
-            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
-        mel_output = torch.matmul(self.mel_basis, magnitude)
-        if self.is_half == True:
-            mel_output = mel_output.half()
-        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        model = E2E(4, 1, (2, 2))
-        ckpt = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(ckpt)
-        model.eval()
-        if is_half == True:
-            model = model.half()
-        self.model = model
-        self.resample_kernel = {}
-        self.is_half = is_half
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.mel_extractor = MelSpectrogram(
-            is_half, 128, 16000, 1024, 160, None, 30, 8000
-        ).to(device)
-        self.model = self.model.to(device)
-        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
-        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
-
-    def mel2hidden(self, mel):
-        with torch.no_grad():
-            n_frames = mel.shape[-1]
-            mel = F.pad(
-                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
-            )
-            hidden = self.model(mel)
-            return hidden[:, :n_frames]
-
-    def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
-        f0 = 10 * (2 ** (cents_pred / 1200))
-        f0[f0 == 10] = 0
-        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
-        return f0
-
-    def infer_from_audio(self, audio, thred=0.03):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        # torch.cuda.synchronize()
-        # t0=ttime()
-        mel = self.mel_extractor(audio, center=True)
-        # torch.cuda.synchronize()
-        # t1=ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        # t2=ttime()
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        # torch.cuda.synchronize()
-        # t3=ttime()
-        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
-        return f0
-
-    def to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
-        center = np.argmax(salience, axis=1)  # 帧长#index
-        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
-        # t1 = ttime()
-        center += 4
-        todo_salience = []
-        todo_cents_mapping = []
-        starts = center - 4
-        ends = center + 5
-        for idx in range(salience.shape[0]):
-            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
-            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
-        # t2 = ttime()
-        todo_salience = np.array(todo_salience)  # 帧长，9
-        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
-        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
-        weight_sum = np.sum(todo_salience, 1)  # 帧长
-        devided = product_sum / weight_sum  # 帧长
-        # t3 = ttime()
-        maxx = np.max(salience, axis=1)  # 帧长
-        devided[maxx <= thred] = 0
-        # t4 = ttime()
-        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return devided
-
-
-# if __name__ == '__main__':
-#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
-#     if len(audio.shape) > 1:
-#         audio = librosa.to_mono(audio.transpose(1, 0))
-#     audio_bak = audio.copy()
-#     if sampling_rate != 16000:
-#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
-#     thred = 0.03  # 0.01
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     rmvpe = RMVPE(model_path,is_half=False, device=device)
-#     t0=ttime()
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     t1=ttime()
-#     print(f0.shape,t1-t0)
diff --git a/spaces/Theivaprakasham/yolov6/tools/infer.py b/spaces/Theivaprakasham/yolov6/tools/infer.py
deleted file mode 100644
index 89841b0af94d49523e37a840c5f8ec9d39f0acd2..0000000000000000000000000000000000000000
--- a/spaces/Theivaprakasham/yolov6/tools/infer.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import argparse
-import os
-import sys
-import os.path as osp
-
-import torch
-
-ROOT = os.getcwd()
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))
-
-from yolov6.utils.events import LOGGER
-from yolov6.core.inferer import Inferer
-
-
-def get_args_parser(add_help=True):
-    parser = argparse.ArgumentParser(description='YOLOv6 PyTorch Inference.', add_help=add_help)
-    parser.add_argument('--weights', type=str, default='weights/yolov6s.pt', help='model path(s) for inference.')
-    parser.add_argument('--source', type=str, default='data/images', help='the source path, e.g. image-file/dir.')
-    parser.add_argument('--yaml', type=str, default='data/coco.yaml', help='data yaml file.')
-    parser.add_argument('--img-size', type=int, default=640, help='the image-size(h,w) in inference size.')
-    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold for inference.')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold for inference.')
-    parser.add_argument('--max-det', type=int, default=1000, help='maximal inferences per image.')
-    parser.add_argument('--device', default='0', help='device to run our model i.e. 0 or 0,1,2,3 or cpu.')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt.')
-    parser.add_argument('--save-img', action='store_false', help='save visuallized inference results.')
-    parser.add_argument('--classes', nargs='+', type=int, help='filter by classes, e.g. --classes 0, or --classes 0 2 3.')
-    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS.')
-    parser.add_argument('--project', default='runs/inference', help='save inference results to project/name.')
-    parser.add_argument('--name', default='exp', help='save inference results to project/name.')
-    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels.')
-    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences.')
-    parser.add_argument('--half', action='store_true', help='whether to use FP16 half-precision inference.')
-
-    args = parser.parse_args()
-    LOGGER.info(args)
-    return args
-
-@torch.no_grad()
-def run(weights=osp.join(ROOT, 'yolov6s.pt'),
-        source=osp.join(ROOT, 'data/images'),
-        yaml=None,
-        img_size=640,
-        conf_thres=0.25,
-        iou_thres=0.45,
-        max_det=1000,
-        device='',
-        save_txt=False,
-        save_img=True,
-        classes=None,
-        agnostic_nms=False,
-        project=osp.join(ROOT, 'runs/inference'),
-        name='exp',
-        hide_labels=False,
-        hide_conf=False,
-        half=False,
-        ):
-    """ Inference process
-
-    This function is the main process of inference, supporting image files or dirs containing images.
-
-    Args:
-        weights: The path of model.pt, e.g. yolov6s.pt
-        source: Source path, supporting image files or dirs containing images.
-        yaml: Data yaml file, .
-        img_size: Inference image-size, e.g. 640
-        conf_thres: Confidence threshold in inference, e.g. 0.25
-        iou_thres: NMS IOU threshold in inference, e.g. 0.45
-        max_det: Maximal detections per image, e.g. 1000
-        device: Cuda device, e.e. 0, or 0,1,2,3 or cpu
-        save_txt: Save results to *.txt
-        save_img: Save visualized inference results
-        classes: Filter by class: --class 0, or --class 0 2 3
-        agnostic_nms: Class-agnostic NMS
-        project: Save results to project/name
-        name: Save results to project/name, e.g. 'exp'
-        line_thickness: Bounding box thickness (pixels), e.g. 3
-        hide_labels: Hide labels, e.g. False
-        hide_conf: Hide confidences
-        half: Use FP16 half-precision inference, e.g. False
-    """
-    # create save dir
-    save_dir = osp.join(project, name)
-    if (save_img or save_txt) and not osp.exists(save_dir):
-        os.makedirs(save_dir)
-    else:
-        LOGGER.warning('Save directory already existed')
-    if save_txt:
-        os.mkdir(osp.join(save_dir, 'labels'))
-
-    # Inference
-    inferer = Inferer(source, weights, device, yaml, img_size, half)
-    inferer.infer(conf_thres, iou_thres, classes, agnostic_nms, max_det, save_dir, save_txt, save_img, hide_labels, hide_conf)
-
-    if save_txt or save_img:
-        LOGGER.info(f"Results saved to {save_dir}")
-
-
-def main(args):
-    run(**vars(args))
-
-
-if __name__ == "__main__":
-    args = get_args_parser()
-    main(args)
diff --git a/spaces/Theivaprakasham/yolov6/yolov6/models/efficientrep.py b/spaces/Theivaprakasham/yolov6/yolov6/models/efficientrep.py
deleted file mode 100644
index 4cb9f1ca431b466af460212becec3b0e7ea15d5c..0000000000000000000000000000000000000000
--- a/spaces/Theivaprakasham/yolov6/yolov6/models/efficientrep.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from torch import nn
-from yolov6.layers.common import RepVGGBlock, RepBlock, SimSPPF
-
-
-class EfficientRep(nn.Module):
-    '''EfficientRep Backbone
-    EfficientRep is handcrafted by hardware-aware neural network design.
-    With rep-style struct, EfficientRep is friendly to high-computation hardware(e.g. GPU).
-    '''
-
-    def __init__(
-        self,
-        in_channels=3,
-        channels_list=None,
-        num_repeats=None,
-    ):
-        super().__init__()
-
-        assert channels_list is not None
-        assert num_repeats is not None
-
-        self.stem = RepVGGBlock(
-            in_channels=in_channels,
-            out_channels=channels_list[0],
-            kernel_size=3,
-            stride=2
-        )
-
-        self.ERBlock_2 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[0],
-                out_channels=channels_list[1],
-                kernel_size=3,
-                stride=2
-            ),
-            RepBlock(
-                in_channels=channels_list[1],
-                out_channels=channels_list[1],
-                n=num_repeats[1]
-            )
-        )
-
-        self.ERBlock_3 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[1],
-                out_channels=channels_list[2],
-                kernel_size=3,
-                stride=2
-            ),
-            RepBlock(
-                in_channels=channels_list[2],
-                out_channels=channels_list[2],
-                n=num_repeats[2]
-            )
-        )
-
-        self.ERBlock_4 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[2],
-                out_channels=channels_list[3],
-                kernel_size=3,
-                stride=2
-            ),
-            RepBlock(
-                in_channels=channels_list[3],
-                out_channels=channels_list[3],
-                n=num_repeats[3]
-            )
-        )
-
-        self.ERBlock_5 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[3],
-                out_channels=channels_list[4],
-                kernel_size=3,
-                stride=2,
-            ),
-            RepBlock(
-                in_channels=channels_list[4],
-                out_channels=channels_list[4],
-                n=num_repeats[4]
-            ),
-            SimSPPF(
-                in_channels=channels_list[4],
-                out_channels=channels_list[4],
-                kernel_size=5
-            )
-        )
-
-    def forward(self, x):
-
-        outputs = []
-        x = self.stem(x)
-        x = self.ERBlock_2(x)
-        x = self.ERBlock_3(x)
-        outputs.append(x)
-        x = self.ERBlock_4(x)
-        outputs.append(x)
-        x = self.ERBlock_5(x)
-        outputs.append(x)
-
-        return tuple(outputs)
diff --git a/spaces/UholoDala/Jj_Sentiment_Analysis_App/README.md b/spaces/UholoDala/Jj_Sentiment_Analysis_App/README.md
deleted file mode 100644
index 3cae256c987a835cc3c6f298b3dbd0283daf77b5..0000000000000000000000000000000000000000
--- a/spaces/UholoDala/Jj_Sentiment_Analysis_App/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Jj Sentiment Analysis App
-emoji: 📊
-colorFrom: gray
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-#Author
-Jacob O. Jaroya
\ No newline at end of file
diff --git a/spaces/Wanlau/sovits-4.0_datealive/hubert/hubert_model_onnx.py b/spaces/Wanlau/sovits-4.0_datealive/hubert/hubert_model_onnx.py
deleted file mode 100644
index d18f3c2a0fc29592a573a9780308d38f059640b9..0000000000000000000000000000000000000000
--- a/spaces/Wanlau/sovits-4.0_datealive/hubert/hubert_model_onnx.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-    def forward(self, x):
-        return self.units(x)
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/Wootang01/text_augmenter1/README.md b/spaces/Wootang01/text_augmenter1/README.md
deleted file mode 100644
index fd276a307403c298b785ef99d01735e4a1550e8b..0000000000000000000000000000000000000000
--- a/spaces/Wootang01/text_augmenter1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text Augmenter1
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Xenova/react-translator/README.md b/spaces/Xenova/react-translator/README.md
deleted file mode 100644
index bb12eb35d5c3aa6294da18415015466695dbab53..0000000000000000000000000000000000000000
--- a/spaces/Xenova/react-translator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: React Translator
-emoji: 🌍
-colorFrom: gray
-colorTo: yellow
-sdk: static
-pinned: false
----
-
-A simple React application that performs multilingual translation using Transformers.js!
-
-This is part of the Transformers.js tutorial series. For more information, check out the [GitHub repo](https://github.com/xenova/transformers.js).
\ No newline at end of file
diff --git a/spaces/Xhaheen/chatgpt_meme_world_/app.py b/spaces/Xhaheen/chatgpt_meme_world_/app.py
deleted file mode 100644
index 56dbb1a1528812c6b890b415fd5f7129c8616c11..0000000000000000000000000000000000000000
--- a/spaces/Xhaheen/chatgpt_meme_world_/app.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# # %%bash
-
-# # # git lfs install
-# # # git clone https://huggingface.co/spaces/Xhaheen/meme_world
-
-
-# # # pip install -r /content/meme_world/requirements.txt
-# # # pip install gradio
-# # cd /meme_world
-
-
-# import torch 
-# import re 
-# import gradio as gr
-# from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
-# import cohere
-# import os
-# # 
-# # os.environ['key_srkian'] = ''
-# key_srkian = os.environ["key_srkian"]
-# co = cohere.Client(key_srkian)#srkian
-# device='cpu'
-# encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-# decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-# model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
-# feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
-# tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
-# model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
-
-
-# def predict(department,image,max_length=64, num_beams=4):
-#   image = image.convert('RGB')
-#   image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
-#   clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
-#   caption_ids = model.generate(image, max_length = max_length)[0]
-#   caption_text = clean_text(tokenizer.decode(caption_ids))
-#   dept=department
-#   context= caption_text
-#   response = co.generate(
-#   model='large',
-#   prompt=f'create non offensive one line meme for given department and context\n\ndepartment- data science\ncontext-a man sitting on a bench with a laptop\nmeme- \"I\'m not a data scientist, but I play one on my laptop.\"\n\ndepartment-startup\ncontext-a young boy is smiling while using a laptop\nmeme-\"When your startup gets funded and you can finally afford a new laptop\"\n\ndepartment- {dept}\ncontext-{context}\nmeme-',
-#   max_tokens=20,
-#   temperature=0.8,
-#   k=0,
-#   p=0.75,
-#   frequency_penalty=0,
-#   presence_penalty=0,
-#   stop_sequences=["department"],
-#   return_likelihoods='NONE')
-#   reponse=response.generations[0].text
-#   reponse = reponse.replace("department", "")
-#   Feedback_SQL="DEPT"+dept+"CAPT"+caption_text+"MAMAY"+reponse
-  
-     
-#   return reponse 
-
-
-
-# # input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
- 
- 
- 
-# output = gr.outputs.Textbox(type="text",label="Meme")
-# #examples = [f"example{i}.jpg" for i in range(1,7)]
-# #examples = os.listdir() 
-# examples = [f"example{i}.png" for i in range(1,7)]
-
-# #examples=os.listdir()
-# #for fichier in examples:
-#   #  if not(fichier.endswith(".png")):
-#    #     examples.remove(fichier)
-        
-# description= " Looking for a fun and easy way to generate memes? Look no further than Meme world! Leveraging large language models like GPT-3PT-3  / Ai21 / Cohere, you can create memes that are sure to be a hit with your friends or network. Created with ♥️ by Arsalan @[Xaheen](https://www.linkedin.com/in/sallu-mandya/). kindly share your thoughts in discussion session and use the app responsibly #NO_Offense \n \n built with ❤️   @[Xhaheen](https://www.linkedin.com/in/sallu-mandya/)"
-# title = "Meme world 🖼️"
-# dropdown=["data science", "product management","marketing","startup" ,"agile","crypto" , "SEO" ]
-
-# article = "Created By :  Xaheen "
-
-# interface = gr.Interface(
-#         fn=predict,
-#         inputs = [gr.inputs.Dropdown(dropdown),gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)],
-        
-#         theme="grass",
-#         outputs=output,
-#         examples =[['data science', 'example5.png'],
-#             ['product management', 'example2.png'],
-#             ['startup', 'example3.png'],
-#             ['marketing', 'example4.png'],
-#             ['agile', 'example1.png'],
-#             ['crypto', 'example6.png']],
-#         title=title,
-#         description=description,
-#         article = article,
-#     )
-# interface.launch(debug=True)
-
-
-
-
-
-
-
-
-
-
-
-# Step 2: Set up the Gradio interface and import necessary packages
-import gradio as gr
-import openai
-from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
-import torch
-from PIL import Image
-import os
-
-# Step 3: Load the provided image captioning model
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# Step 4: Create a function to generate captions from images
-max_length = 16
-num_beams = 4
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-
-def generate_caption(image):
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-    if image.mode != "RGB":
-        image = image.convert(mode="RGB")
-    pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values
-    pixel_values = pixel_values.to(device)
-    output_ids = model.generate(pixel_values, **gen_kwargs)
-    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
-    return caption
-
-
-# Step 5: Create a function to generate memes using the GPT-3 API
-def generate_meme(caption, department):
-    openai.api_key = os.environ["key"]
-    prompt = f"Create a non-offensive meme caption for the following image description in the context of {department} department: {caption}"
-    response = openai.Completion.create(engine="text-davinci-002", prompt=prompt, max_tokens=50, n=1, stop=None, temperature=0.7)
-    meme_caption = response.choices[0].text.strip()
-    return meme_caption
-
-# Step 6: Define the main meme generation function
-def meme_generator(image, department):
-    caption = generate_caption(image)
-    meme_caption = generate_meme(caption, department)
-    return meme_caption
-
-examples = [f"example{i}.png" for i in range(1,7)]
-
-# Step 7: Launch the Gradio application
-image_input = gr.inputs.Image()
-department_input = gr.inputs.Dropdown(choices=["data science", "product management","marketing","startup" ,"agile","crypto" , "SEO" ])
-output_text = gr.outputs.Textbox()
-
-gr.Interface(fn=meme_generator, inputs=[image_input, department_input], outputs=output_text, title="Meme world!",description= " Looking for a fun and easy way to generate memes? Look no further than Meme world! Leveraging large language models like GPT-3PT-3  / Ai21 / Cohere, you can create memes that are sure to be a hit with your friends or network. Created with ♥️ by Arsalan @[Xaheen](https://www.linkedin.com/in/sallu-mandya/). kindly share your thoughts in discussion session and use the app responsibly #NO_Offense \n \n built with ❤️   @[Xhaheen](https://www.linkedin.com/in/sallu-mandya/)",        theme="freddyaboulton/dracula_revamped",
-    
-        examples =[['example5.png','data science' ],
-            ['example2.png','product management'],
-            ['example3.png','startup'],
-            ['example4.png','marketing'],
-            ['example1.png','agile'],
-            ['example6.png','crypto']]).launch(debug=True)
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/XzJosh/Aatrox-Bert-VITS2/monotonic_align/core.py b/spaces/XzJosh/Aatrox-Bert-VITS2/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Aatrox-Bert-VITS2/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-  b = paths.shape[0]
-  max_neg_val=-1e9
-  for i in range(int(b)):
-    path = paths[i]
-    value = values[i]
-    t_y = t_ys[i]
-    t_x = t_xs[i]
-
-    v_prev = v_cur = 0.0
-    index = t_x - 1
-
-    for y in range(t_y):
-      for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-        if x == y:
-          v_cur = max_neg_val
-        else:
-          v_cur = value[y-1, x]
-        if x == 0:
-          if y == 0:
-            v_prev = 0.
-          else:
-            v_prev = max_neg_val
-        else:
-          v_prev = value[y-1, x-1]
-        value[y, x] += max(v_prev, v_cur)
-
-    for y in range(t_y - 1, -1, -1):
-      path[y, index] = 1
-      if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
-        index = index - 1
diff --git a/spaces/XzJosh/XingTong-Bert-VITS2/mel_processing.py b/spaces/XzJosh/XingTong-Bert-VITS2/mel_processing.py
deleted file mode 100644
index 50435ecf88ef4fb6c1d47f3e6edd04c3ea7d3e80..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/XingTong-Bert-VITS2/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/YlcldKlns/bing/src/components/learn-more.tsx b/spaces/YlcldKlns/bing/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
-  sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
-  if (!sourceAttributions?.length) {
-    return null
-  }
-
-  return (
-    <div className="learn-more-root" role="list" aria-label="了解详细信息:">
-      <div className="learn-more">了解详细信息:</div>
-      <div className="attribution-container">
-        <div className="attribution-items">
-          {sourceAttributions.map((attribution, index) => {
-            const { providerDisplayName, seeMoreUrl } = attribution
-            const { host } = new URL(seeMoreUrl)
-            return (
-              <a
-                key={index}
-                className="attribution-item"
-                target="_blank"
-                role="listitem"
-                href={seeMoreUrl}
-                title={providerDisplayName}
-                tabIndex={index}
-              >
-                {index + 1}. {host}
-              </a>
-            )
-          })}
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/YlcldKlns/bing/src/components/ui/badge.tsx b/spaces/YlcldKlns/bing/src/components/ui/badge.tsx
deleted file mode 100644
index d9a84b394090e5b4b3bd34f6135b9a2f2ead0aa2..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/ui/badge.tsx
+++ /dev/null
@@ -1,36 +0,0 @@
-import * as React from 'react'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const badgeVariants = cva(
-  'inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2',
-  {
-    variants: {
-      variant: {
-        default:
-          'border-transparent bg-primary text-primary-foreground hover:bg-primary/80',
-        secondary:
-          'border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        destructive:
-          'border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80',
-        outline: 'text-foreground'
-      }
-    },
-    defaultVariants: {
-      variant: 'default'
-    }
-  }
-)
-
-export interface BadgeProps
-  extends React.HTMLAttributes<HTMLDivElement>,
-    VariantProps<typeof badgeVariants> {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
-  return (
-    <div className={cn(badgeVariants({ variant }), className)} {...props} />
-  )
-}
-
-export { Badge, badgeVariants }
diff --git a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/upfirdn2d.cpp b/spaces/YotamNitzan/domain-expansion/torch_utils/ops/upfirdn2d.cpp
deleted file mode 100644
index 2d7177fc60040751d20e9a8da0301fa3ab64968a..0000000000000000000000000000000000000000
--- a/spaces/YotamNitzan/domain-expansion/torch_utils/ops/upfirdn2d.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/Yusin/ChatGPT-Speech/monotonic_align/core.py b/spaces/Yusin/ChatGPT-Speech/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/Yusin/ChatGPT-Speech/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-  b = paths.shape[0]
-  max_neg_val=-1e9
-  for i in range(int(b)):
-    path = paths[i]
-    value = values[i]
-    t_y = t_ys[i]
-    t_x = t_xs[i]
-
-    v_prev = v_cur = 0.0
-    index = t_x - 1
-
-    for y in range(t_y):
-      for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-        if x == y:
-          v_cur = max_neg_val
-        else:
-          v_cur = value[y-1, x]
-        if x == 0:
-          if y == 0:
-            v_prev = 0.
-          else:
-            v_prev = max_neg_val
-        else:
-          v_prev = value[y-1, x-1]
-        value[y, x] += max(v_prev, v_cur)
-
-    for y in range(t_y - 1, -1, -1):
-      path[y, index] = 1
-      if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
-        index = index - 1
diff --git a/spaces/Zwicky18/Stable-difussion/app.py b/spaces/Zwicky18/Stable-difussion/app.py
deleted file mode 100644
index a337cfc07f899bf0321d190e47bf6acb0c305e2f..0000000000000000000000000000000000000000
--- a/spaces/Zwicky18/Stable-difussion/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os
-from subprocess import getoutput
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-
-os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/                show_progress=False,/                show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/               queue=False,  /                /g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-if "IS_SHARED_UI" in os.environ:
-    os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-    
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
-    os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
-    os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
-    os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
-    
-    os.system(f"python launch.py --force-enable-xformers --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
-    # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
-    os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-    
-    # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
-    #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
-    os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
-    os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-    
-    # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
-    #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
-    #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
-    #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
-    #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
-    #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-    
-    #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-    
-    #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
-    #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-    os.system(f"wget -q https://r2.kamiya-b.me/dreambooth_lib/akakura-sn.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/akakura-sn.ckpt")
-    #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.ckpt")
-    os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.yaml")
-   
-    os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
-    os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
-    os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
-    
-    os.system(f"python launch.py --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")
-    
\ No newline at end of file
diff --git a/spaces/aTrapDeer/Img2TimDillonRant/README.md b/spaces/aTrapDeer/Img2TimDillonRant/README.md
deleted file mode 100644
index 0382332c305212b4fcdb7fe12f32729612f3c6f5..0000000000000000000000000000000000000000
--- a/spaces/aTrapDeer/Img2TimDillonRant/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Img2TimDillonRant
-emoji: 📈
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abhijitguha/chatbot_gpt3/README.md b/spaces/abhijitguha/chatbot_gpt3/README.md
deleted file mode 100644
index 3c2c3a4c97a291f5b26829e9ccbebd3cf42d0777..0000000000000000000000000000000000000000
--- a/spaces/abhijitguha/chatbot_gpt3/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chatbot Gpt3
-emoji: 🦀
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/image/colorspace.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/image/colorspace.py
deleted file mode 100644
index 814533952fdfda23d67cb6a3073692d8c1156add..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/image/colorspace.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import cv2
-import numpy as np
-
-
-def imconvert(img, src, dst):
-    """Convert an image from the src colorspace to dst colorspace.
-
-    Args:
-        img (ndarray): The input image.
-        src (str): The source colorspace, e.g., 'rgb', 'hsv'.
-        dst (str): The destination colorspace, e.g., 'rgb', 'hsv'.
-
-    Returns:
-        ndarray: The converted image.
-    """
-    code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}')
-    out_img = cv2.cvtColor(img, code)
-    return out_img
-
-
-def bgr2gray(img, keepdim=False):
-    """Convert a BGR image to grayscale image.
-
-    Args:
-        img (ndarray): The input image.
-        keepdim (bool): If False (by default), then return the grayscale image
-            with 2 dims, otherwise 3 dims.
-
-    Returns:
-        ndarray: The converted grayscale image.
-    """
-    out_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    if keepdim:
-        out_img = out_img[..., None]
-    return out_img
-
-
-def rgb2gray(img, keepdim=False):
-    """Convert a RGB image to grayscale image.
-
-    Args:
-        img (ndarray): The input image.
-        keepdim (bool): If False (by default), then return the grayscale image
-            with 2 dims, otherwise 3 dims.
-
-    Returns:
-        ndarray: The converted grayscale image.
-    """
-    out_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-    if keepdim:
-        out_img = out_img[..., None]
-    return out_img
-
-
-def gray2bgr(img):
-    """Convert a grayscale image to BGR image.
-
-    Args:
-        img (ndarray): The input image.
-
-    Returns:
-        ndarray: The converted BGR image.
-    """
-    img = img[..., None] if img.ndim == 2 else img
-    out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-    return out_img
-
-
-def gray2rgb(img):
-    """Convert a grayscale image to RGB image.
-
-    Args:
-        img (ndarray): The input image.
-
-    Returns:
-        ndarray: The converted RGB image.
-    """
-    img = img[..., None] if img.ndim == 2 else img
-    out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-    return out_img
-
-
-def _convert_input_type_range(img):
-    """Convert the type and range of the input image.
-
-    It converts the input image to np.float32 type and range of [0, 1].
-    It is mainly used for pre-processing the input image in colorspace
-    conversion functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with type of np.float32 and range of
-            [0, 1].
-    """
-    img_type = img.dtype
-    img = img.astype(np.float32)
-    if img_type == np.float32:
-        pass
-    elif img_type == np.uint8:
-        img /= 255.
-    else:
-        raise TypeError('The img type should be np.float32 or np.uint8, '
-                        f'but got {img_type}')
-    return img
-
-
-def _convert_output_type_range(img, dst_type):
-    """Convert the type and range of the image according to dst_type.
-
-    It converts the image to desired type and range. If `dst_type` is np.uint8,
-    images will be converted to np.uint8 type with range [0, 255]. If
-    `dst_type` is np.float32, it converts the image to np.float32 type with
-    range [0, 1].
-    It is mainly used for post-processing images in colorspace conversion
-    functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The image to be converted with np.float32 type and
-            range [0, 255].
-        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
-            converts the image to np.uint8 type with range [0, 255]. If
-            dst_type is np.float32, it converts the image to np.float32 type
-            with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with desired type and range.
-    """
-    if dst_type not in (np.uint8, np.float32):
-        raise TypeError('The dst_type should be np.float32 or np.uint8, '
-                        f'but got {dst_type}')
-    if dst_type == np.uint8:
-        img = img.round()
-    else:
-        img /= 255.
-    return img.astype(dst_type)
-
-
-def rgb2ycbcr(img, y_only=False):
-    """Convert a RGB image to YCbCr image.
-
-    This function produces the same results as Matlab's `rgb2ycbcr` function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                  [24.966, 112.0, -18.214]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def bgr2ycbcr(img, y_only=False):
-    """Convert a BGR image to YCbCr image.
-
-    The bgr version of rgb2ycbcr.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                  [65.481, -37.797, 112.0]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2rgb(img):
-    """Convert a YCbCr image to RGB image.
-
-    This function produces the same results as Matlab's ycbcr2rgb function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted RGB image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
-                              [0, -0.00153632, 0.00791071],
-                              [0.00625893, -0.00318811, 0]]) * 255.0 + [
-                                  -222.921, 135.576, -276.836
-                              ]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2bgr(img):
-    """Convert a YCbCr image to BGR image.
-
-    The bgr version of ycbcr2rgb.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted BGR image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621],
-                              [0.00791071, -0.00153632, 0],
-                              [0, -0.00318811, 0.00625893]]) * 255.0 + [
-                                  -276.836, 135.576, -222.921
-                              ]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def convert_color_factory(src, dst):
-
-    code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}')
-
-    def convert_color(img):
-        out_img = cv2.cvtColor(img, code)
-        return out_img
-
-    convert_color.__doc__ = f"""Convert a {src.upper()} image to {dst.upper()}
-        image.
-
-    Args:
-        img (ndarray or str): The input image.
-
-    Returns:
-        ndarray: The converted {dst.upper()} image.
-    """
-
-    return convert_color
-
-
-bgr2rgb = convert_color_factory('bgr', 'rgb')
-
-rgb2bgr = convert_color_factory('rgb', 'bgr')
-
-bgr2hsv = convert_color_factory('bgr', 'hsv')
-
-hsv2bgr = convert_color_factory('hsv', 'bgr')
-
-bgr2hls = convert_color_factory('bgr', 'hls')
-
-hls2bgr = convert_color_factory('hls', 'bgr')
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/losses/gaussian_focal_loss.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/losses/gaussian_focal_loss.py
deleted file mode 100644
index e45506a38e8e3c187be8288d0b714cc1ee29cf27..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/losses/gaussian_focal_loss.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import mmcv
-import torch.nn as nn
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
-    """`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
-    distribution.
-
-    Args:
-        pred (torch.Tensor): The prediction.
-        gaussian_target (torch.Tensor): The learning target of the prediction
-            in gaussian distribution.
-        alpha (float, optional): A balanced form for Focal Loss.
-            Defaults to 2.0.
-        gamma (float, optional): The gamma for calculating the modulating
-            factor. Defaults to 4.0.
-    """
-    eps = 1e-12
-    pos_weights = gaussian_target.eq(1)
-    neg_weights = (1 - gaussian_target).pow(gamma)
-    pos_loss = -(pred + eps).log() * (1 - pred).pow(alpha) * pos_weights
-    neg_loss = -(1 - pred + eps).log() * pred.pow(alpha) * neg_weights
-    return pos_loss + neg_loss
-
-
-@LOSSES.register_module()
-class GaussianFocalLoss(nn.Module):
-    """GaussianFocalLoss is a variant of focal loss.
-
-    More details can be found in the `paper
-    <https://arxiv.org/abs/1808.01244>`_
-    Code is modified from `kp_utils.py
-    <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_  # noqa: E501
-    Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
-    not 0/1 binary target.
-
-    Args:
-        alpha (float): Power of prediction.
-        gamma (float): Power of target for negative samples.
-        reduction (str): Options are "none", "mean" and "sum".
-        loss_weight (float): Loss weight of current loss.
-    """
-
-    def __init__(self,
-                 alpha=2.0,
-                 gamma=4.0,
-                 reduction='mean',
-                 loss_weight=1.0):
-        super(GaussianFocalLoss, self).__init__()
-        self.alpha = alpha
-        self.gamma = gamma
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction
-                in gaussian distribution.
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_reg = self.loss_weight * gaussian_focal_loss(
-            pred,
-            target,
-            weight,
-            alpha=self.alpha,
-            gamma=self.gamma,
-            reduction=reduction,
-            avg_factor=avg_factor)
-        return loss_reg
diff --git a/spaces/ahmadprince007/HolyBot/code/static/js/main_v_0.1.js b/spaces/ahmadprince007/HolyBot/code/static/js/main_v_0.1.js
deleted file mode 100644
index 09e143312269fbf0e881a06db89bc6b3288047b0..0000000000000000000000000000000000000000
--- a/spaces/ahmadprince007/HolyBot/code/static/js/main_v_0.1.js
+++ /dev/null
@@ -1,199 +0,0 @@
-const chat = document.querySelector('.chat');
-
-
-function submit_quest(question) {
-    var data = new FormData();
-    data.append("q", question);
-    var xhttp = new XMLHttpRequest();
-    xhttp.timeout = 5000;
-    xhttp.open("POST", "/chat");
-    xhttp.send(data);
-    try{
-    xhttp.onload = async function() {
-	if (this.readyState == 4 && this.status == 200) {
-	    var response_obj = JSON.parse(this.responseText);
-	    if(response_obj){
-		//console.log(response_obj.answer)
-		addMessage('HolyBot', response_obj.answer)
-		enableButton()
-
-	    }else if(response_obj.status == "Error"){
-		// setStatusErrorMessage(response_obj.status_message)
-		return false;
-	    }else{
-		// internalServerError();
-		return false;
-	    }
-	}else if(this.readyState == 4 && this.status == 0){
-	    return false;
-	}
-	else{
-	    //internalServerError();
-	    return false;
-	}
-  
-    };
-    xhttp.onerror = function() {
-	internalServerError();
-	return false;
-    };
-
-    }catch(e){
-	internalServerError();
-    }
-
-}
-
-function getAnswer(user) {
-    disabledButton()
-    $('#examples').hide();
-    var quest = $("#user-input").val()
-    addQuestion(quest);
-    submit_quest(quest);
-    //enableButton()
-}
-
-function getExampleAnswer(quest){
-    disabledButton()
-    $('#examples').hide();
-    addQuestion(quest);
-    submit_quest(quest);
-    //enableButton()
-}
-
-function load_json(user) {
-    var key = $("#user-key").val()
-
-    var data = new FormData();
-    data.append("key", key);
-    var xhttp = new XMLHttpRequest();
-    xhttp.timeout = 5000;
-    xhttp.open("POST", "/load");
-    xhttp.send(data);
-    try{
-    xhttp.onload = async function() {
-	if (this.readyState == 4 && this.status == 200) {
-	    var response_obj = JSON.parse(this.responseText);
-	    if(response_obj){
-		console.log(response_obj.msg)
-	    }else if(response_obj.status == "Error"){
-		return false;
-	    }else{
-		return false;
-	    }
-	}else if(this.readyState == 4 && this.status == 0){
-	    return false;
-	}
-	else{
-	    return false;
-	}
-  
-    };
-    xhttp.onerror = function() {
-	internalServerError();
-	return false;
-    };
-
-    }catch(e){
-	internalServerError();
-    }
-
-
-}
-
-
-
-
-function addMessage(user, text) {
-    var chat = document.getElementsByClassName('chat')[0];
-    const messageDiv = document.createElement('div');
-    messageDiv.classList.add('message');
-
-    const userSpan = document.createElement('span');
-    userSpan.classList.add('user');
-    userSpan.textContent = user + ':';
-
-    const textSpan = document.createElement('span');
-    textSpan.classList.add('text');
-
-    messageDiv.appendChild(userSpan);
-
-    textArray = text.split(' ')
-
-    const interval = setInterval(() => {
-
-	textSpan.innerHTML = textSpan.innerHTML + ' ' +textArray[0]
-	chat.scrollTop = chat.scrollHeight;
-	//document.write(textArray[0]);
-
-	textArray = textArray.slice(1);
-	
-	if (!textArray.length) {
-	    clearInterval(interval);
-	}
-    }, 50);
-
-
-    messageDiv.appendChild(textSpan);
-    chat.appendChild(messageDiv);
-
-    // Scroll to bottom of chat
-    chat.scrollTop = chat.scrollHeight;
-}
-
-
-function addAnswer(user, text) {
-    //var text = $("#user-input").val()
-    var chat = document.getElementsByClassName('chat')[0];
-    const messageDiv = document.createElement('div');
-    messageDiv.classList.add('message');
-
-    const userSpan = document.createElement('span');
-    userSpan.classList.add('user');
-    userSpan.textContent = user + ':';
-
-    const textSpan = document.createElement('span');
-    textSpan.classList.add('text');
-    //textSpan.textContent = text;
-    textSpan.innerHTML = text;
-
-    messageDiv.appendChild(userSpan);
-    messageDiv.appendChild(textSpan);
-    chat.appendChild(messageDiv);
-    //ch.appendChild(messageDiv);
-
-    // Scroll to bottom of chat
-    chat.scrollTop = chat.scrollHeight;
-
-}
-
-
-function addQuestion(text) {
-    var chat = document.getElementsByClassName('chat')[0];
-    const messageDiv = document.createElement('div');
-    messageDiv.classList.add('message');
-
-    const userSpan = document.createElement('span');
-    // userSpan.classList.add('user');
-    // userSpan.textContent = user + ':';
-
-    const textSpan = document.createElement('span');
-    textSpan.classList.add('user-text');
-    textSpan.innerHTML = text;
-    // messageDiv.appendChild(userSpan);
-    messageDiv.appendChild(textSpan);
-    chat.appendChild(messageDiv);
-    chat.scrollTop = chat.scrollHeight;
-}
-
-
-function enableButton(){
-    $("#input-group :input").attr("disabled", false); 
-    $("#input-group :button").attr("disabled", false); 
-}
-
-function disabledButton(){
-    $("#input-group :input").attr("disabled", true); 
-    $("#input-group :button").attr("disabled", true); 
-
-}
diff --git a/spaces/ai-art/upscaling/app.py b/spaces/ai-art/upscaling/app.py
deleted file mode 100644
index cc2463224b95356a9012386baf27205cafb65d78..0000000000000000000000000000000000000000
--- a/spaces/ai-art/upscaling/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import numpy as np
-import cv2
-import onnxruntime
-import gradio as gr
-
-
-def pre_process(img: np.array) -> np.array:
-    # H, W, C -> C, H, W
-    img = np.transpose(img[:, :, 0:3], (2, 0, 1))
-    # C, H, W -> 1, C, H, W
-    img = np.expand_dims(img, axis=0).astype(np.float32)
-    return img
-
-
-def post_process(img: np.array) -> np.array:
-    # 1, C, H, W -> C, H, W
-    img = np.squeeze(img)
-    # C, H, W -> H, W, C
-    img = np.transpose(img, (1, 2, 0))[:, :, ::-1].astype(np.uint8)
-    return img
-
-
-def inference(model_path: str, img_array: np.array) -> np.array:
-    options = onnxruntime.SessionOptions()
-    options.intra_op_num_threads = 1
-    options.inter_op_num_threads = 1
-    ort_session = onnxruntime.InferenceSession(model_path, options)
-    ort_inputs = {ort_session.get_inputs()[0].name: img_array}
-    ort_outs = ort_session.run(None, ort_inputs)
-
-    return ort_outs[0]
-
-
-def convert_pil_to_cv2(image):
-    # pil_image = image.convert("RGB")
-    open_cv_image = np.array(image)
-    # RGB to BGR
-    open_cv_image = open_cv_image[:, :, ::-1].copy()
-    return open_cv_image
-
-
-def upscale(image, model):
-    model_path = f"models/{model}.ort"
-    img = convert_pil_to_cv2(image)
-    if img.ndim == 2:
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-    if img.shape[2] == 4:
-        alpha = img[:, :, 3]  # GRAY
-        alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2BGR)  # BGR
-        alpha_output = post_process(inference(model_path, pre_process(alpha)))  # BGR
-        alpha_output = cv2.cvtColor(alpha_output, cv2.COLOR_BGR2GRAY)  # GRAY
-
-        img = img[:, :, 0:3]  # BGR
-        image_output = post_process(inference(model_path, pre_process(img)))  # BGR
-        image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2BGRA)  # BGRA
-        image_output[:, :, 3] = alpha_output
-
-    elif img.shape[2] == 3:
-        image_output = post_process(inference(model_path, pre_process(img)))  # BGR
-
-    return image_output
-
-
-css = ".output-image, .input-image, .image-preview {height: 480px !important} "
-model_choices = ["modelx2", "modelx2 25 JXL", "modelx4", "minecraft_modelx4"]
-
-gr.Interface(
-    fn=upscale,
-    inputs=[
-        gr.inputs.Image(type="pil", label="Input Image"),
-        gr.inputs.Radio(
-            model_choices,
-            type="value",
-            default="modelx2",
-            label="Choose Upscaler",
-            optional=False,
-        ),
-    ],
-    outputs="image",
-    title="",
-    description="",
-    allow_flagging="never",
-    css=css,
-).launch()
diff --git a/spaces/ai-forever/mGPT-armenian/app.py b/spaces/ai-forever/mGPT-armenian/app.py
deleted file mode 100644
index d1a82199187db85c5276f7ebdaebd7f5560386d3..0000000000000000000000000000000000000000
--- a/spaces/ai-forever/mGPT-armenian/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import torch
-import gradio as gr
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-#from googletrans import Translator
-
-#translator = Translator()
-tokenizer = GPT2Tokenizer.from_pretrained("sberbank-ai/mGPT-armenian")
-model = GPT2LMHeadModel.from_pretrained("sberbank-ai/mGPT-armenian")
-#model.cuda()
-#model.eval()
-
-description = "Multilingual generation with mGPT"
-title = "Generate your own example"
-
-examples = [["""Երեւանից ծրագրավորողները ներկայացրել են նոր գործարք: Այն կոչվում է"""]]#, "Նորություններ. Բրազիլացի գիտնականները հայտնաբերել են Ուութլանդի արեւմուտքում ապրող գաճաճ միաեղջյուրների հազվագյուտ տեսակին:"
-
-article = (
-    "<p style='text-align: center'>"
-    "<a href='https://github.com/ai-forever/mgpt'>GitHub</a>  "
-    "</p>"
-)
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-fp16 = device != 'cpu'
-model.to(device).eval()
-
-def transl(text, src='en', dest='hy' ):
-    return translator.translate(text, src=src, dest=dest).text
-    
-def generate(prompt: str):
-            #print(prompt)
-            #print(length, type(length))
-            #length = int(length)
-            input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
-            out = model.generate(input_ids, 
-                    min_length=200, 
-                    max_length=700, 
-                    top_p=0.95, 
-                    top_k=0,
-                    temperature=0.9,
-                    no_repeat_ngram_size=5
-            )
-            generated_text = list(map(tokenizer.decode, out))[0]
-            return generated_text #+ '\n\n'+transl(generated_text, src='hy', dest='en')
-            
-
-
-#contexto = gr.inputs.Textbox(lines=10, placeholder="Write your text here")
-#length = gr.inputs.Slider(200, 1000)
-
-interface = gr.Interface.load("huggingface/sberbank-ai/mGPT-armenian",
-            description=description,
-            examples=examples, 
-            fn=generate,
-            inputs="text",#[contexto,length],#
-            outputs='text',
-            thumbnail = 'https://habrastorage.org/r/w1560/getpro/habr/upload_files/26a/fa1/3e1/26afa13e1d1a56f54c7b0356761af7b8.png',
-            theme = "peach",
-            article = article
-) 
-
-interface.launch(enable_queue=True)
diff --git a/spaces/ai-guru/composer/README.md b/spaces/ai-guru/composer/README.md
deleted file mode 100644
index 76dc89402274fdd999d829074bf8527246f4ea5b..0000000000000000000000000000000000000000
--- a/spaces/ai-guru/composer/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Composer
-emoji: 🪄
-colorFrom: gray
-colorTo: black
-sdk: gradio
-sdk_version: 2.9.4
-app_file: start.py
-pinned: false
----
diff --git a/spaces/aijack/jojo/e4e/utils/train_utils.py b/spaces/aijack/jojo/e4e/utils/train_utils.py
deleted file mode 100644
index 0c55177f7442010bc1fcc64de3d142585c22adc0..0000000000000000000000000000000000000000
--- a/spaces/aijack/jojo/e4e/utils/train_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-
-def aggregate_loss_dict(agg_loss_dict):
-	mean_vals = {}
-	for output in agg_loss_dict:
-		for key in output:
-			mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]]
-	for key in mean_vals:
-		if len(mean_vals[key]) > 0:
-			mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key])
-		else:
-			print('{} has no value'.format(key))
-			mean_vals[key] = 0
-	return mean_vals
diff --git a/spaces/akhaliq/Kapao/utils/general.py b/spaces/akhaliq/Kapao/utils/general.py
deleted file mode 100644
index 9b948befdc526de407c68deea45aa713d9f479c1..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Kapao/utils/general.py
+++ /dev/null
@@ -1,853 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-General utils
-"""
-
-import contextlib
-import glob
-import logging
-import math
-import os
-import platform
-import random
-import re
-import signal
-import time
-import urllib
-from itertools import repeat
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-from subprocess import check_output
-
-import cv2
-import numpy as np
-import pandas as pd
-import pkg_resources as pkg
-import torch
-import torchvision
-import yaml
-
-from utils.downloads import gsutil_getsize
-from utils.metrics import box_iou, fitness
-from utils.torch_utils import init_torch_seeds
-from utils.labels import write_kp_labels
-
-# Settings
-torch.set_printoptions(linewidth=320, precision=5, profile='long')
-np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
-pd.options.display.max_columns = 10
-cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
-os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8))  # NumExpr max threads
-
-
-class Profile(contextlib.ContextDecorator):
-    # Usage: @Profile() decorator or 'with Profile():' context manager
-    def __enter__(self):
-        self.start = time.time()
-
-    def __exit__(self, type, value, traceback):
-        print(f'Profile results: {time.time() - self.start:.5f}s')
-
-
-class Timeout(contextlib.ContextDecorator):
-    # Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
-    def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
-        self.seconds = int(seconds)
-        self.timeout_message = timeout_msg
-        self.suppress = bool(suppress_timeout_errors)
-
-    def _timeout_handler(self, signum, frame):
-        raise TimeoutError(self.timeout_message)
-
-    def __enter__(self):
-        signal.signal(signal.SIGALRM, self._timeout_handler)  # Set handler for SIGALRM
-        signal.alarm(self.seconds)  # start countdown for SIGALRM to be raised
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        signal.alarm(0)  # Cancel SIGALRM if it's scheduled
-        if self.suppress and exc_type is TimeoutError:  # Suppress TimeoutError
-            return True
-
-
-def try_except(func):
-    # try-except function. Usage: @try_except decorator
-    def handler(*args, **kwargs):
-        try:
-            func(*args, **kwargs)
-        except Exception as e:
-            print(e)
-
-    return handler
-
-
-def methods(instance):
-    # Get class/instance methods
-    return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
-
-
-def set_logging(rank=-1, verbose=True):
-    logging.basicConfig(
-        format="%(message)s",
-        level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN)
-
-
-def init_seeds(seed=0):
-    # Initialize random number generator (RNG) seeds
-    random.seed(seed)
-    np.random.seed(seed)
-    init_torch_seeds(seed)
-
-
-def get_latest_run(search_dir='.'):
-    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
-    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
-    return max(last_list, key=os.path.getctime) if last_list else ''
-
-
-def is_docker():
-    # Is environment a Docker container?
-    return Path('/workspace').exists()  # or Path('/.dockerenv').exists()
-
-
-def is_colab():
-    # Is environment a Google Colab instance?
-    try:
-        import google.colab
-        return True
-    except Exception as e:
-        return False
-
-
-def is_pip():
-    # Is file in a pip package?
-    return 'site-packages' in Path(__file__).absolute().parts
-
-
-def is_ascii(s=''):
-    # Is string composed of all ASCII (no UTF) characters?
-    s = str(s)  # convert list, tuple, None, etc. to str
-    return len(s.encode().decode('ascii', 'ignore')) == len(s)
-
-
-def emojis(str=''):
-    # Return platform-dependent emoji-safe version of string
-    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
-def file_size(file):
-    # Return file size in MB
-    return Path(file).stat().st_size / 1e6
-
-
-def check_online():
-    # Check internet connectivity
-    import socket
-    try:
-        socket.create_connection(("1.1.1.1", 443), 5)  # check host accessibility
-        return True
-    except OSError:
-        return False
-
-
-@try_except
-def check_git_status():
-    # Recommend 'git pull' if code is out of date
-    msg = ', for updates see https://github.com/ultralytics/yolov5'
-    print(colorstr('github: '), end='')
-    assert Path('.git').exists(), 'skipping check (not a git repository)' + msg
-    assert not is_docker(), 'skipping check (Docker image)' + msg
-    assert check_online(), 'skipping check (offline)' + msg
-
-    cmd = 'git fetch && git config --get remote.origin.url'
-    url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git')  # git fetch
-    branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out
-    n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True))  # commits behind
-    if n > 0:
-        s = f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
-    else:
-        s = f'up to date with {url} ✅'
-    print(emojis(s))  # emoji-safe
-
-
-def check_python(minimum='3.6.2'):
-    # Check current python version vs. required python version
-    check_version(platform.python_version(), minimum, name='Python ')
-
-
-def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False):
-    # Check version vs. required version
-    current, minimum = (pkg.parse_version(x) for x in (current, minimum))
-    result = (current == minimum) if pinned else (current >= minimum)
-    assert result, f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed'
-
-
-@try_except
-def check_requirements(requirements='requirements.txt', exclude=(), install=True):
-    # Check installed dependencies meet requirements (pass *.txt file or list of packages)
-    prefix = colorstr('red', 'bold', 'requirements:')
-    check_python()  # check python version
-    if isinstance(requirements, (str, Path)):  # requirements.txt file
-        file = Path(requirements)
-        assert file.exists(), f"{prefix} {file.resolve()} not found, check failed."
-        requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude]
-    else:  # list or tuple of packages
-        requirements = [x for x in requirements if x not in exclude]
-
-    n = 0  # number of packages updates
-    for r in requirements:
-        try:
-            pkg.require(r)
-        except Exception as e:  # DistributionNotFound or VersionConflict if requirements not met
-            s = f"{prefix} {r} not found and is required by YOLOv5"
-            if install:
-                print(f"{s}, attempting auto-update...")
-                try:
-                    assert check_online(), f"'pip install {r}' skipped (offline)"
-                    print(check_output(f"pip install '{r}'", shell=True).decode())
-                    n += 1
-                except Exception as e:
-                    print(f'{prefix} {e}')
-            else:
-                print(f'{s}. Please install and rerun your command.')
-
-    if n:  # if packages updated
-        source = file.resolve() if 'file' in locals() else requirements
-        s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
-            f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
-        print(emojis(s))
-
-
-def check_img_size(imgsz, s=32, floor=0):
-    # Verify image size is a multiple of stride s in each dimension
-    if isinstance(imgsz, int):  # integer i.e. img_size=640
-        new_size = max(make_divisible(imgsz, int(s)), floor)
-    else:  # list i.e. img_size=[640, 480]
-        new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
-    if new_size != imgsz:
-        print(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
-    return new_size
-
-
-def check_imshow():
-    # Check if environment supports image displays
-    try:
-        assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
-        assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
-        cv2.imshow('test', np.zeros((1, 1, 3)))
-        cv2.waitKey(1)
-        cv2.destroyAllWindows()
-        cv2.waitKey(1)
-        return True
-    except Exception as e:
-        print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
-        return False
-
-
-def check_file(file):
-    # Search/download file (if necessary) and return path
-    file = str(file)  # convert to str()
-    if Path(file).is_file() or file == '':  # exists
-        return file
-    elif file.startswith(('http:/', 'https:/')):  # download
-        url = str(Path(file)).replace(':/', '://')  # Pathlib turns :// -> :/
-        file = Path(urllib.parse.unquote(file)).name.split('?')[0]  # '%2F' to '/', split https://url.com/file.txt?auth
-        print(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, file)
-        assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'  # check
-        return file
-    else:  # search
-        files = glob.glob('./**/' + file, recursive=True)  # find file
-        assert len(files), f'File not found: {file}'  # assert file was found
-        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
-        return files[0]  # return file
-
-
-def check_dataset(data, autodownload=True):
-    # Download and/or unzip dataset if not found locally
-    # Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip
-
-    # Download (optional)
-    extract_dir = ''
-    if isinstance(data, (str, Path)) and str(data).endswith('.zip'):  # i.e. gs://bucket/dir/coco128.zip
-        download(data, dir='../datasets', unzip=True, delete=False, curl=False, threads=1)
-        data = next((Path('../datasets') / Path(data).stem).rglob('*.yaml'))
-        extract_dir, autodownload = data.parent, False
-
-    # Read yaml (optional)
-    if isinstance(data, (str, Path)):
-        with open(data, errors='ignore') as f:
-            data = yaml.safe_load(f)  # dictionary
-
-    # Parse yaml
-    path = extract_dir or Path(data.get('path') or '')  # optional 'path' default to '.'
-    for k in 'train', 'val', 'test':
-        if data.get(k):  # prepend path
-            data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
-
-    assert 'nc' in data, "Dataset 'nc' key missing."
-    if 'names' not in data:
-        data['names'] = [f'class{i}' for i in range(data['nc'])]  # assign class names if missing
-    train, val, test, s = [data.get(x) for x in ('train', 'val', 'test', 'download')]
-    if val:
-        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
-        if not all(x.exists() for x in val):
-            if 'kp_bbox' in data.keys():
-                print('Writing dataset labels to {}...'.format(os.path.join(data['path'], data['labels'])))
-                write_kp_labels(data)
-            else:
-                print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
-                if s and autodownload:  # download script
-                    if s.startswith('http') and s.endswith('.zip'):  # URL
-                        f = Path(s).name  # filename
-                        print(f'Downloading {s} ...')
-                        torch.hub.download_url_to_file(s, f)
-                        root = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'
-                        Path(root).mkdir(parents=True, exist_ok=True)  # create root
-                        r = os.system(f'unzip -q {f} -d {root} && rm {f}')  # unzip
-                    elif s.startswith('bash '):  # bash script
-                        print(f'Running {s} ...')
-                        r = os.system(s)
-                    else:  # python script
-                        r = exec(s, {'yaml': data})  # return None
-                    print('Dataset autodownload %s\n' % ('success' if r in (0, None) else 'failure'))  # print result
-                else:
-                    raise Exception('Dataset not found.')
-
-    return data  # dictionary
-
-
-def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
-    # Multi-threaded file download and unzip function, used in data.yaml for autodownload
-    def download_one(url, dir):
-        # Download 1 file
-        f = dir / Path(url).name  # filename
-        if Path(url).is_file():  # exists in current path
-            Path(url).rename(f)  # move to dir
-        elif not f.exists():
-            print(f'Downloading {url} to {f}...')
-            if curl:
-                os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -")  # curl download, retry and resume on fail
-            else:
-                torch.hub.download_url_to_file(url, f, progress=True)  # torch download
-        if unzip and f.suffix in ('.zip', '.gz'):
-            print(f'Unzipping {f}...')
-            if f.suffix == '.zip':
-                s = f'unzip -qo {f} -d {dir}'  # unzip -quiet -overwrite
-            elif f.suffix == '.gz':
-                s = f'tar xfz {f} --directory {f.parent}'  # unzip
-            if delete:  # delete zip file after unzip
-                s += f' && rm {f}'
-            os.system(s)
-
-    dir = Path(dir)
-    dir.mkdir(parents=True, exist_ok=True)  # make directory
-    if threads > 1:
-        pool = ThreadPool(threads)
-        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multi-threaded
-        pool.close()
-        pool.join()
-    else:
-        for u in [url] if isinstance(url, (str, Path)) else url:
-            download_one(u, dir)
-
-
-def make_divisible(x, divisor):
-    # Returns x evenly divisible by divisor
-    return math.ceil(x / divisor) * divisor
-
-
-def clean_str(s):
-    # Cleans a string by replacing special characters with underscore _
-    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
-
-
-def one_cycle(y1=0.0, y2=1.0, steps=100):
-    # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
-    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
-
-
-def colorstr(*input):
-    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
-    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
-    colors = {'black': '\033[30m',  # basic colors
-              'red': '\033[31m',
-              'green': '\033[32m',
-              'yellow': '\033[33m',
-              'blue': '\033[34m',
-              'magenta': '\033[35m',
-              'cyan': '\033[36m',
-              'white': '\033[37m',
-              'bright_black': '\033[90m',  # bright colors
-              'bright_red': '\033[91m',
-              'bright_green': '\033[92m',
-              'bright_yellow': '\033[93m',
-              'bright_blue': '\033[94m',
-              'bright_magenta': '\033[95m',
-              'bright_cyan': '\033[96m',
-              'bright_white': '\033[97m',
-              'end': '\033[0m',  # misc
-              'bold': '\033[1m',
-              'underline': '\033[4m'}
-    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
-
-
-def labels_to_class_weights(labels, nc=80):
-    # Get class weights (inverse frequency) from training labels
-    if labels[0] is None:  # no labels loaded
-        return torch.Tensor()
-
-    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
-    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
-    weights = np.bincount(classes, minlength=nc)  # occurrences per class
-
-    # Prepend gridpoint count (for uCE training)
-    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
-    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start
-
-    weights[weights == 0] = 1  # replace empty bins with 1
-    weights = 1 / weights  # number of targets per class
-    weights /= weights.sum()  # normalize
-    return torch.from_numpy(weights)
-
-
-def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
-    # Produces image weights based on class_weights and image contents
-    class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
-    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
-    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
-    return image_weights
-
-
-def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
-    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
-    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
-    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
-    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
-    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
-    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
-         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
-    return x
-
-
-def xyxy2xywh(x):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
-    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
-    y[:, 2] = x[:, 2] - x[:, 0]  # width
-    y[:, 3] = x[:, 3] - x[:, 1]  # height
-    return y
-
-
-def xywh2xyxy(x):
-    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
-    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
-    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
-    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
-    return y
-
-
-def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
-    # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x
-    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y
-    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x
-    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y
-
-    # Convert keypoints from [x, y, v] normalized to [x, y]
-    if y.shape[-1] > 4:
-        nl = y.shape[0]
-        kp = y[:, 4:].reshape(nl, -1, 3)
-        kp[..., 0] *= w
-        kp[..., 0] += padw
-        kp[..., 1] *= h
-        kp[..., 1] += padh
-        y[:, 4:] = kp.reshape(nl, -1)
-
-    return y
-
-
-def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
-    if clip:
-        clip_coords(x, (h - eps, w - eps))  # warning: inplace clip
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w  # x center
-    y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h  # y center
-    y[:, 2] = (x[:, 2] - x[:, 0]) / w  # width
-    y[:, 3] = (x[:, 3] - x[:, 1]) / h  # height
-
-    # convert keypoints from [x, y, v] to [x, y, v] normalized
-    if y.shape[-1] > 4:
-        nl = y.shape[0]
-        kp = y[:, 4:].reshape(nl, -1, 3)
-        kp[..., 0] /= w
-        kp[..., 1] /= h
-        y[:, 4:] = kp.reshape(nl, -1)
-
-    return y
-
-
-def xyn2xy(x, w=640, h=640, padw=0, padh=0):
-    # Convert normalized segments into pixel segments, shape (n,2)
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * x[:, 0] + padw  # top left x
-    y[:, 1] = h * x[:, 1] + padh  # top left y
-    return y
-
-
-def segment2box(segment, width=640, height=640):
-    # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
-    x, y = segment.T  # segment xy
-    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
-    x, y, = x[inside], y[inside]
-    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy
-
-
-def segments2boxes(segments):
-    # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
-    boxes = []
-    for s in segments:
-        x, y = s.T  # segment xy
-        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
-    return xyxy2xywh(np.array(boxes))  # cls, xywh
-
-
-def resample_segments(segments, n=1000):
-    # Up-sample an (n,2) segment
-    for i, s in enumerate(segments):
-        x = np.linspace(0, len(s) - 1, n)
-        xp = np.arange(len(s))
-        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
-    return segments
-
-
-def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
-    # Rescale coords (xyxy) from img1_shape to img0_shape
-    if ratio_pad is None:  # calculate from img0_shape
-        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-    else:
-        gain = ratio_pad[0][0]
-        pad = ratio_pad[1]
-
-    nl = coords.shape[0]
-    coords = coords.reshape((nl, -1, 2))
-    coords[..., 0] -= pad[0]
-    coords[..., 1] -= pad[1]
-    coords /= gain
-    coords = coords.reshape(nl, -1)
-    clip_coords(coords, img0_shape)
-    return coords
-
-
-def clip_coords(boxes, shape):
-    # Clip bounding xyxy bounding boxes to image shape (height, width)
-    if isinstance(boxes, torch.Tensor):  # faster individually
-        boxes[:, 0].clamp_(0, shape[1])  # x1
-        boxes[:, 1].clamp_(0, shape[0])  # y1
-        boxes[:, 2].clamp_(0, shape[1])  # x2
-        boxes[:, 3].clamp_(0, shape[0])  # y2
-    else:  # np.array (faster grouped)
-        boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1])  # x1, x2
-        boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0])  # y1, y2
-
-
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
-                        labels=(), max_det=300):
-    """Runs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
-    """
-
-    nc = prediction.shape[2] - 5  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Checks
-    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
-    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
-
-    # Settings
-    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
-    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            l = labels[xi]
-            v = torch.zeros((len(l), nc + 5), device=x.device)
-            v[:, :4] = l[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, cls)
-        if multi_label:
-            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 5:].max(1, keepdim=True)
-            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Apply finite constraint
-        # if not torch.isfinite(x).all():
-        #     x = x[torch.isfinite(x).all(1)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-        elif n > max_nms:  # excess boxes
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # Batched NMS
-        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if i.shape[0] > max_det:  # limit detections
-            i = i[:max_det]
-        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f'WARNING: NMS time limit {time_limit}s exceeded')
-            break  # time limit exceeded
-
-    return output
-
-
-def non_max_suppression_kp(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, max_det=300, num_coords=34):
-    """Runs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         list of detections, on (n,6) tensor per image [xyxy, conf, cls, keypoints]
-    """
-
-    nc = prediction.shape[2] - 5 - num_coords  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Checks
-    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
-    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
-
-    # Settings
-    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
-    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 6 + num_coords), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
-        x = x[xc[xi]]  # confidence
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 5:-num_coords] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, cls)
-        conf, j = x[:, 5:-num_coords].max(1, keepdim=True)
-        kp = x[:, -num_coords:]
-        x = torch.cat((box, conf, j.float(), kp), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Apply finite constraint
-        # if not torch.isfinite(x).all():
-        #     x = x[torch.isfinite(x).all(1)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-        elif n > max_nms:  # excess boxes
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # Batched NMS
-        c = x[:, 5:6] * max_wh  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if i.shape[0] > max_det:  # limit detections
-            i = i[:max_det]
-        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f'WARNING: NMS time limit {time_limit}s exceeded')
-            break  # time limit exceeded
-
-    return output
-
-
-def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
-    # Strip optimizer from 'f' to finalize training, optionally save as 's'
-    x = torch.load(f, map_location=torch.device('cpu'))
-    if x.get('ema'):
-        x['model'] = x['ema']  # replace model with ema
-    for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates':  # keys
-        x[k] = None
-    x['epoch'] = -1
-    x['model'].half()  # to FP16
-    for p in x['model'].parameters():
-        p.requires_grad = False
-    torch.save(x, s or f)
-    mb = os.path.getsize(s or f) / 1E6  # filesize
-    print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
-
-
-def print_mutation(results, hyp, save_dir, bucket):
-    evolve_csv, results_csv, evolve_yaml = save_dir / 'evolve.csv', save_dir / 'results.csv', save_dir / 'hyp_evolve.yaml'
-    keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-            'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys())  # [results + hyps]
-    keys = tuple(x.strip() for x in keys)
-    vals = results + tuple(hyp.values())
-    n = len(keys)
-
-    # Download (optional)
-    if bucket:
-        url = f'gs://{bucket}/evolve.csv'
-        if gsutil_getsize(url) > (os.path.getsize(evolve_csv) if os.path.exists(evolve_csv) else 0):
-            os.system(f'gsutil cp {url} {save_dir}')  # download evolve.csv if larger than local
-
-    # Log to evolve.csv
-    s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n')  # add header
-    with open(evolve_csv, 'a') as f:
-        f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
-
-    # Print to screen
-    print(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
-    print(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals), end='\n\n\n')
-
-    # Save yaml
-    with open(evolve_yaml, 'w') as f:
-        data = pd.read_csv(evolve_csv)
-        data = data.rename(columns=lambda x: x.strip())  # strip keys
-        i = np.argmax(fitness(data.values[:, :7]))  #
-        f.write(f'# YOLOv5 Hyperparameter Evolution Results\n' +
-                f'# Best generation: {i}\n' +
-                f'# Last generation: {len(data)}\n' +
-                f'# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' +
-                f'# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
-        yaml.safe_dump(hyp, f, sort_keys=False)
-
-    if bucket:
-        os.system(f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}')  # upload
-
-
-def apply_classifier(x, model, img, im0):
-    # Apply a second stage classifier to yolo outputs
-    im0 = [im0] if isinstance(im0, np.ndarray) else im0
-    for i, d in enumerate(x):  # per image
-        if d is not None and len(d):
-            d = d.clone()
-
-            # Reshape and pad cutouts
-            b = xyxy2xywh(d[:, :4])  # boxes
-            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
-            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
-            d[:, :4] = xywh2xyxy(b).long()
-
-            # Rescale boxes from img_size to im0 size
-            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
-
-            # Classes
-            pred_cls1 = d[:, 5].long()
-            ims = []
-            for j, a in enumerate(d):  # per item
-                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
-                im = cv2.resize(cutout, (224, 224))  # BGR
-                # cv2.imwrite('example%i.jpg' % j, cutout)
-
-                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
-                im /= 255.0  # 0 - 255 to 0.0 - 1.0
-                ims.append(im)
-
-            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
-            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections
-
-    return x
-
-
-def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):
-    # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
-    xyxy = torch.tensor(xyxy).view(-1, 4)
-    b = xyxy2xywh(xyxy)  # boxes
-    if square:
-        b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square
-    b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad
-    xyxy = xywh2xyxy(b).long()
-    clip_coords(xyxy, im.shape)
-    crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
-    if save:
-        cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop)
-    return crop
-
-
-def increment_path(path, exist_ok=False, sep='', mkdir=False):
-    # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
-    path = Path(path)  # os-agnostic
-    if path.exists() and not exist_ok:
-        suffix = path.suffix
-        path = path.with_suffix('')
-        dirs = glob.glob(f"{path}{sep}*")  # similar paths
-        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
-        i = [int(m.groups()[0]) for m in matches if m]  # indices
-        n = max(i) + 1 if i else 2  # increment number
-        path = Path(f"{path}{sep}{n}{suffix}")  # update path
-    dir = path if path.suffix == '' else path.parent  # directory
-    if not dir.exists() and mkdir:
-        dir.mkdir(parents=True, exist_ok=True)  # make directory
-    return path
diff --git a/spaces/akhaliq/Mask2Former/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py b/spaces/akhaliq/Mask2Former/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
deleted file mode 100644
index eecd413d4ed028f94e3aad9fc6bad231e850b5da..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import json
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
-from detectron2.utils.file_io import PathManager
-
-
-_PREDEFINED_SPLITS_COCO_PANOPTIC = {
-    "coco_2017_train_panoptic": (
-        # This is the original panoptic annotation directory
-        "coco/panoptic_train2017",
-        "coco/annotations/panoptic_train2017.json",
-        # This directory contains semantic annotations that are
-        # converted from panoptic annotations.
-        # It is used by PanopticFPN.
-        # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
-        # to create these directories.
-        "coco/panoptic_semseg_train2017",
-    ),
-    "coco_2017_val_panoptic": (
-        "coco/panoptic_val2017",
-        "coco/annotations/panoptic_val2017.json",
-        "coco/panoptic_semseg_val2017",
-    ),
-}
-
-
-def get_metadata():
-    meta = {}
-    # The following metadata maps contiguous id from [0, #thing categories +
-    # #stuff categories) to their names and colors. We have to replica of the
-    # same name and color under "thing_*" and "stuff_*" because the current
-    # visualization function in D2 handles thing and class classes differently
-    # due to some heuristic used in Panoptic FPN. We keep the same naming to
-    # enable reusing existing visualization functions.
-    thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
-    thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
-    stuff_classes = [k["name"] for k in COCO_CATEGORIES]
-    stuff_colors = [k["color"] for k in COCO_CATEGORIES]
-
-    meta["thing_classes"] = thing_classes
-    meta["thing_colors"] = thing_colors
-    meta["stuff_classes"] = stuff_classes
-    meta["stuff_colors"] = stuff_colors
-
-    # Convert category id for training:
-    #   category id: like semantic segmentation, it is the class id for each
-    #   pixel. Since there are some classes not used in evaluation, the category
-    #   id is not always contiguous and thus we have two set of category ids:
-    #       - original category id: category id in the original dataset, mainly
-    #           used for evaluation.
-    #       - contiguous category id: [0, #classes), in order to train the linear
-    #           softmax classifier.
-    thing_dataset_id_to_contiguous_id = {}
-    stuff_dataset_id_to_contiguous_id = {}
-
-    for i, cat in enumerate(COCO_CATEGORIES):
-        if cat["isthing"]:
-            thing_dataset_id_to_contiguous_id[cat["id"]] = i
-        # else:
-        #     stuff_dataset_id_to_contiguous_id[cat["id"]] = i
-
-        # in order to use sem_seg evaluator
-        stuff_dataset_id_to_contiguous_id[cat["id"]] = i
-
-    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
-    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
-
-    return meta
-
-
-def load_coco_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
-        gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
-        json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
-    Returns:
-        list[dict]: a list of dicts in Detectron2 standard format. (See
-        `Using Custom Datasets </tutorials/datasets.html>`_ )
-    """
-
-    def _convert_category_id(segment_info, meta):
-        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
-            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
-                segment_info["category_id"]
-            ]
-            segment_info["isthing"] = True
-        else:
-            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
-                segment_info["category_id"]
-            ]
-            segment_info["isthing"] = False
-        return segment_info
-
-    with PathManager.open(json_file) as f:
-        json_info = json.load(f)
-
-    ret = []
-    for ann in json_info["annotations"]:
-        image_id = int(ann["image_id"])
-        # TODO: currently we assume image and label has the same filename but
-        # different extension, and images have extension ".jpg" for COCO. Need
-        # to make image extension a user-provided argument if we extend this
-        # function to support other COCO-like datasets.
-        image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
-        label_file = os.path.join(gt_dir, ann["file_name"])
-        sem_label_file = os.path.join(semseg_dir, ann["file_name"])
-        segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
-        ret.append(
-            {
-                "file_name": image_file,
-                "image_id": image_id,
-                "pan_seg_file_name": label_file,
-                "sem_seg_file_name": sem_label_file,
-                "segments_info": segments_info,
-            }
-        )
-    assert len(ret), f"No images found in {image_dir}!"
-    assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
-    assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
-    assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
-    return ret
-
-
-def register_coco_panoptic_annos_sem_seg(
-    name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json
-):
-    panoptic_name = name
-    delattr(MetadataCatalog.get(panoptic_name), "thing_classes")
-    delattr(MetadataCatalog.get(panoptic_name), "thing_colors")
-    MetadataCatalog.get(panoptic_name).set(
-        thing_classes=metadata["thing_classes"],
-        thing_colors=metadata["thing_colors"],
-        # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"],
-    )
-
-    # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg"
-    semantic_name = name + "_with_sem_seg"
-    DatasetCatalog.register(
-        semantic_name,
-        lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, sem_seg_root, metadata),
-    )
-    MetadataCatalog.get(semantic_name).set(
-        sem_seg_root=sem_seg_root,
-        panoptic_root=panoptic_root,
-        image_root=image_root,
-        panoptic_json=panoptic_json,
-        json_file=instances_json,
-        evaluator_type="coco_panoptic_seg",
-        ignore_label=255,
-        label_divisor=1000,
-        **metadata,
-    )
-
-
-def register_all_coco_panoptic_annos_sem_seg(root):
-    for (
-        prefix,
-        (panoptic_root, panoptic_json, semantic_root),
-    ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
-        prefix_instances = prefix[: -len("_panoptic")]
-        instances_meta = MetadataCatalog.get(prefix_instances)
-        image_root, instances_json = instances_meta.image_root, instances_meta.json_file
-
-        register_coco_panoptic_annos_sem_seg(
-            prefix,
-            get_metadata(),
-            image_root,
-            os.path.join(root, panoptic_root),
-            os.path.join(root, panoptic_json),
-            os.path.join(root, semantic_root),
-            instances_json,
-        )
-
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_coco_panoptic_annos_sem_seg(_root)
diff --git a/spaces/akhaliq/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__init__.py b/spaces/akhaliq/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__init__.py
deleted file mode 100644
index d7c96243d4b8477b2c1d22bb835a7e3effd8d6b5..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/youtubevos/cocoapi
diff --git a/spaces/akhaliq/Swin-Transformer/README.md b/spaces/akhaliq/Swin-Transformer/README.md
deleted file mode 100644
index c16ddbec1d3c7b5c9ebb1f1ce72985dcfae482b9..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Swin-Transformer/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: Swin Transformer
-emoji: 🦀
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/akhaliq/basil_mix/app.py b/spaces/akhaliq/basil_mix/app.py
deleted file mode 100644
index 42e16d17415749279c4dd186a7c0948b65f2099c..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/basil_mix/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'nuigurumi/basil_mix'
-prefix = ''
-     
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
-  try:
-    if img is not None:
-      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>Basil Mix</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/nuigurumi/basil_mix">Basil Mix</a> Stable Diffusion model.<br>
-               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
-              </p>
-              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/akhaliq/basil_mix/settings'>Settings</a></b>"} after duplicating the space<br><br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/akhaliq/basil_mix?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-              image_out = gr.Image(height=512)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
-    </div>
-    """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/akhaliq/deeplab2/data/preprocessing/autoaugment_utils.py b/spaces/akhaliq/deeplab2/data/preprocessing/autoaugment_utils.py
deleted file mode 100644
index 3600b51b1018fc399158d0c4ebe1e772975a5c6a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/data/preprocessing/autoaugment_utils.py
+++ /dev/null
@@ -1,422 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# 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.
-
-"""AutoAugment utility file.
-
-Please cite or refer to the following papers:
-- Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le.
-"Autoaugment: Learning augmentation policies from data." In CVPR, 2019.
-
-- Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le.
-"Randaugment: Practical automated data augmentation with a reduced search
-space." In CVPR, 2020.
-"""
-
-import inspect
-
-import tensorflow as tf
-
-from deeplab2.data.preprocessing import autoaugment_policy
-
-
-# This signifies the max integer that the controller RNN could predict for the
-# augmentation scheme.
-_MAX_LEVEL = 10.
-
-
-def blend(image1, image2, factor):
-  """Blends image1 and image2 using 'factor'.
-
-  Factor can be above 0.0.  A value of 0.0 means only image1 is used.
-  A value of 1.0 means only image2 is used.  A value between 0.0 and
-  1.0 means we linearly interpolate the pixel values between the two
-  images.  A value greater than 1.0 "extrapolates" the difference
-  between the two pixel values, and we clip the results to values
-  between 0 and 255.
-
-  Args:
-    image1: An image Tensor of type uint8.
-    image2: An image Tensor of type uint8.
-    factor: A floating point value above 0.0.
-
-  Returns:
-    A blended image Tensor of type uint8.
-  """
-  if factor == 0.0:
-    return tf.convert_to_tensor(image1)
-  if factor == 1.0:
-    return tf.convert_to_tensor(image2)
-
-  image1 = tf.cast(image1, tf.float32)
-  image2 = tf.cast(image2, tf.float32)
-
-  difference = image2 - image1
-  scaled = factor * difference
-
-  # Do addition in float.
-  temp = tf.cast(image1, tf.float32) + scaled
-
-  # Interpolate
-  if factor > 0.0 and factor < 1.0:
-    # Interpolation means we always stay within 0 and 255.
-    return tf.cast(temp, tf.uint8)
-
-  # Extrapolate:
-  #
-  # We need to clip and then cast.
-  return tf.cast(tf.clip_by_value(temp, 0.0, 255.0), tf.uint8)
-
-
-def solarize(image, threshold=128):
-  # For each pixel in the image, select the pixel
-  # if the value is less than the threshold.
-  # Otherwise, subtract 255 from the pixel.
-  return tf.where(image < threshold, image, 255 - image)
-
-
-def invert(image):
-  """Inverts the image pixels."""
-  image = tf.convert_to_tensor(image)
-  return 255 - image
-
-
-def color(image, factor):
-  """Equivalent of PIL Color."""
-  degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))
-  return blend(degenerate, image, factor)
-
-
-def contrast(image, factor):
-  """Equivalent of PIL Contrast."""
-  degenerate = tf.image.rgb_to_grayscale(image)
-  # Cast before calling tf.histogram.
-  degenerate = tf.cast(degenerate, tf.int32)
-
-  # Compute the grayscale histogram, then compute the mean pixel value,
-  # and create a constant image size of that value.  Use that as the
-  # blending degenerate target of the original image.
-  hist = tf.histogram_fixed_width(degenerate, [0, 255], nbins=256)
-  mean = tf.reduce_sum(tf.cast(hist, tf.float32)) / 256.0
-  degenerate = tf.ones_like(degenerate, dtype=tf.float32) * mean
-  degenerate = tf.clip_by_value(degenerate, 0.0, 255.0)
-  degenerate = tf.image.grayscale_to_rgb(tf.cast(degenerate, tf.uint8))
-  return blend(degenerate, image, factor)
-
-
-def brightness(image, factor):
-  """Equivalent of PIL Brightness."""
-  degenerate = tf.zeros_like(image)
-  return blend(degenerate, image, factor)
-
-
-def posterize(image, bits):
-  """Equivalent of PIL Posterize."""
-  shift = 8 - bits
-  return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)
-
-
-def autocontrast(image):
-  """Implements Autocontrast function from PIL using TF ops.
-
-  Args:
-    image: A 3D uint8 tensor.
-
-  Returns:
-    The image after it has had autocontrast applied to it and will be of type
-    uint8.
-  """
-
-  def scale_channel(image):
-    """Scale the 2D image using the autocontrast rule."""
-    # A possibly cheaper version can be done using cumsum/unique_with_counts
-    # over the histogram values, rather than iterating over the entire image.
-    # to compute mins and maxes.
-    lo = tf.cast(tf.reduce_min(image), tf.float32)
-    hi = tf.cast(tf.reduce_max(image), tf.float32)
-
-    # Scale the image, making the lowest value 0 and the highest value 255.
-    def scale_values(im):
-      scale = 255.0 / (hi - lo)
-      offset = -lo * scale
-      im = tf.cast(im, tf.float32) * scale + offset
-      im = tf.clip_by_value(im, 0.0, 255.0)
-      return tf.cast(im, tf.uint8)
-
-    result = tf.cond(hi > lo, lambda: scale_values(image), lambda: image)
-    return result
-
-  # Assumes RGB for now.  Scales each channel independently
-  # and then stacks the result.
-  s1 = scale_channel(image[:, :, 0])
-  s2 = scale_channel(image[:, :, 1])
-  s3 = scale_channel(image[:, :, 2])
-  image = tf.stack([s1, s2, s3], 2)
-  return image
-
-
-def sharpness(image, factor):
-  """Implements Sharpness function from PIL using TF ops."""
-  orig_image = image
-  image = tf.cast(image, tf.float32)
-  # Make image 4D for conv operation.
-  image = tf.expand_dims(image, 0)
-  # SMOOTH PIL Kernel.
-  kernel = tf.constant(
-      [[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32,
-      shape=[3, 3, 1, 1]) / 13.
-  # Tile across channel dimension.
-  kernel = tf.tile(kernel, [1, 1, 3, 1])
-  strides = [1, 1, 1, 1]
-  degenerate = tf.nn.depthwise_conv2d(
-      image, kernel, strides, padding='VALID', dilations=[1, 1])
-  degenerate = tf.clip_by_value(degenerate, 0.0, 255.0)
-  degenerate = tf.squeeze(tf.cast(degenerate, tf.uint8), [0])
-
-  # For the borders of the resulting image, fill in the values of the
-  # original image.
-  mask = tf.ones_like(degenerate)
-  padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])
-  padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]])
-  result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_image)
-
-  # Blend the final result.
-  return blend(result, orig_image, factor)
-
-
-def equalize(image):
-  """Implements Equalize function from PIL using TF ops."""
-  def scale_channel(im, c):
-    """Scale the data in the channel to implement equalize."""
-    im = tf.cast(im[:, :, c], tf.int32)
-    # Compute the histogram of the image channel.
-    histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)
-
-    # For the purposes of computing the step, filter out the nonzeros.
-    nonzero = tf.where(tf.not_equal(histo, 0))
-    nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1])
-    step = (tf.reduce_sum(nonzero_histo) - nonzero_histo[-1]) // 255
-
-    def build_lut(histo, step):
-      # Compute the cumulative sum, shifting by step // 2
-      # and then normalization by step.
-      lut = (tf.cumsum(histo) + (step // 2)) // step
-      # Shift lut, prepending with 0.
-      lut = tf.concat([[0], lut[:-1]], 0)
-      # Clip the counts to be in range.  This is done
-      # in the C code for image.point.
-      return tf.clip_by_value(lut, 0, 255)
-
-    # If step is zero, return the original image.  Otherwise, build
-    # lut from the full histogram and step and then index from it.
-    result = tf.cond(tf.equal(step, 0),
-                     lambda: im,
-                     lambda: tf.gather(build_lut(histo, step), im))
-
-    return tf.cast(result, tf.uint8)
-
-  # Assumes RGB for now.  Scales each channel independently
-  # and then stacks the result.
-  s1 = scale_channel(image, 0)
-  s2 = scale_channel(image, 1)
-  s3 = scale_channel(image, 2)
-  image = tf.stack([s1, s2, s3], 2)
-  return image
-
-
-NAME_TO_FUNC = {
-    'AutoContrast': autocontrast,
-    'Equalize': equalize,
-    'Invert': invert,
-    'Posterize': posterize,
-    'Solarize': solarize,
-    'Color': color,
-    'Contrast': contrast,
-    'Brightness': brightness,
-    'Sharpness': sharpness,
-}
-
-
-def _enhance_level_to_arg(level):
-  return ((level/_MAX_LEVEL) * 1.8 + 0.1,)
-
-
-def level_to_arg():
-  return {
-      'AutoContrast':
-          lambda level: (),
-      'Equalize':
-          lambda level: (),
-      'Invert':
-          lambda level: (),
-      'Posterize': lambda level: (int((level/_MAX_LEVEL) * 4),),
-      'Solarize': lambda level: (int((level/_MAX_LEVEL) * 256),),
-      'Color':
-          _enhance_level_to_arg,
-      'Contrast':
-          _enhance_level_to_arg,
-      'Brightness':
-          _enhance_level_to_arg,
-      'Sharpness':
-          _enhance_level_to_arg,
-  }
-
-
-def label_wrapper(func):
-  """Adds a label function argument to func and returns unchanged label."""
-  def wrapper(images, label, *args, **kwargs):
-    return func(images, *args, **kwargs), label
-  return wrapper
-
-
-def _parse_policy_info(name, prob, level, replace_value, ignore_label):
-  """Returns the function corresponding to `name` and update `level` param."""
-  func = NAME_TO_FUNC[name]
-  args = level_to_arg()[name](level)
-
-  if 'prob' in inspect.getfullargspec(func)[0]:
-    args = tuple([prob] + list(args))
-
-  # Add in replace arg if it is required for the function that is being called.
-  if 'replace' in inspect.getfullargspec(func)[0]:
-    # Make sure ignore_label is also in the argument.
-    assert 'ignore_label' in inspect.getfullargspec(func)[0]
-    # Make sure replace is the second from last argument
-    assert 'replace' == inspect.getfullargspec(func)[0][-2]
-    # Make sure ignore_label is the final argument
-    assert 'ignore_label' == inspect.getfullargspec(func)[0][-1]
-    args = tuple(list(args) + [replace_value, ignore_label])
-
-  # Add label as the second positional argument for the function if it does
-  # not already exist.
-  if 'label' not in inspect.getfullargspec(func)[0]:
-    func = label_wrapper(func)
-  return (func, prob, args)
-
-
-def _apply_func_with_prob(func, image, args, prob, label):
-  """Apply `func` to image w/ `args` as input with probability `prob`."""
-  assert isinstance(args, tuple)
-  assert 'label' == inspect.getfullargspec(func)[0][1]
-
-  # If prob is a function argument, then this randomness is being handled
-  # inside the function, so make sure it is always called.
-  if 'prob' in inspect.getfullargspec(func)[0]:
-    prob = 1.0
-
-  # Apply the function with probability `prob`.
-  should_apply_op = tf.cast(
-      tf.floor(tf.random.uniform([], dtype=tf.float32) + prob), tf.bool)
-  augmented_image, augmented_label = tf.cond(
-      should_apply_op,
-      lambda: func(image, label, *args),
-      lambda: (image, label))
-  return augmented_image, augmented_label
-
-
-def select_and_apply_random_policy(policies, image, label):
-  """Select a random policy from `policies` and apply it to `image`."""
-  policy_to_select = tf.random.uniform([], maxval=len(policies), dtype=tf.int32)
-  # Note that using tf.case instead of tf.conds would result in significantly
-  # larger graphs and would even break export for some larger policies.
-  for (i, policy) in enumerate(policies):
-    image, label = tf.cond(
-        tf.equal(i, policy_to_select),
-        lambda selected_policy=policy: selected_policy(image, label),
-        lambda: (image, label))
-  return (image, label)
-
-
-def build_and_apply_autoaugment_policy(policies, image, label, ignore_label):
-  """Builds a policy from the given policies passed in and applies to image.
-
-  Args:
-    policies: list of lists of tuples in the form `(func, prob, level)`, `func`
-      is a string name of the augmentation function, `prob` is the probability
-      of applying the `func` operation, `level` is the input argument for
-      `func`.
-    image: tf.Tensor that the resulting policy will be applied to.
-    label: tf.Tensor that the resulting policy will be applied to.
-    ignore_label: The label value which will be ignored for training and
-      evaluation.
-
-  Returns:
-    A version of image that now has data augmentation applied to it based on
-    the `policies` pass into the function. Additionally, returns bboxes if
-    a value for them is passed in that is not None
-  """
-  replace_value = [128, 128, 128]
-
-  # func is the string name of the augmentation function, prob is the
-  # probability of applying the operation and level is the parameter associated
-  # with the tf op.
-
-  # tf_policies are functions that take in an image and return an augmented
-  # image.
-  tf_policies = []
-  for policy in policies:
-    tf_policy = []
-    # Link string name to the correct python function and make sure the correct
-    # argument is passed into that function.
-    for policy_info in policy:
-      policy_info = (
-          list(policy_info) + [replace_value, ignore_label])
-
-      tf_policy.append(_parse_policy_info(*policy_info))
-    # Now build the tf policy that will apply the augmentation procedue
-    # on image.
-    def make_final_policy(tf_policy_):
-      def final_policy(image_, label_):
-        for func, prob, args in tf_policy_:
-          image_, label_ = _apply_func_with_prob(
-              func, image_, args, prob, label_)
-        return image_, label_
-      return final_policy
-    tf_policies.append(make_final_policy(tf_policy))
-
-  augmented_images, augmented_label = select_and_apply_random_policy(
-      tf_policies, image, label)
-  # If no bounding boxes were specified, then just return the images.
-  return (augmented_images, augmented_label)
-
-
-def distort_image_with_autoaugment(image,
-                                   label,
-                                   ignore_label,
-                                   augmentation_name=None):
-  """Applies the AutoAugment policy to `image` and `label`.
-
-  Args:
-    image: `Tensor` of shape [height, width, 3] representing an image.
-    label: `Tensor` of shape [height, width, 1] representing a label.
-    ignore_label: The label value which will be ignored for training and
-      evaluation.
-    augmentation_name: The name of the AutoAugment policy to use. See
-      autoaugment_policy.py for available_policies.
-
-  Returns:
-    A tuple containing the augmented versions of `image` and `label`.
-
-  Raises:
-    ValueError: If the augmentation_name is not in available_policies.
-  """
-  if augmentation_name:
-    available_policies = autoaugment_policy.available_policies
-    if augmentation_name not in available_policies:
-      raise ValueError(
-          'Invalid augmentation_name: {}'.format(augmentation_name))
-    policy = available_policies[augmentation_name]
-    return build_and_apply_autoaugment_policy(
-        policy, image, label, ignore_label)
-  return image, label
diff --git a/spaces/akhaliq/deeplab2/model/decoder/vip_deeplab_decoder.py b/spaces/akhaliq/deeplab2/model/decoder/vip_deeplab_decoder.py
deleted file mode 100644
index a12bb1ca4e9ba75b6f4b2a275beb838f0376f863..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/decoder/vip_deeplab_decoder.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# 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.
-
-"""This file contains code to build a ViP-DeepLab decoder.
-
-Reference:
-  - [ViP-DeepLab: Learning Visual Perception with Depth-aware Video
-      Panoptic Segmentation](https://arxiv.org/abs/2012.05258)
-"""
-import tensorflow as tf
-
-from deeplab2 import common
-from deeplab2.model.decoder import panoptic_deeplab
-
-
-layers = tf.keras.layers
-
-
-class ViPDeepLabDecoder(layers.Layer):
-  """A ViP-DeepLab decoder layer.
-
-  This layer takes low- and high-level features as input and uses a dual-ASPP
-  and dual-decoder structure to aggregate features for semantic and instance
-  segmentation. On top of the decoders, three heads are used to predict semantic
-  segmentation, instance center probabilities, and instance center regression
-  per pixel. It also has a branch to predict the next-frame instance center
-  regression. Different from the ViP-DeepLab paper which uses Cascade-ASPP, this
-  reimplementation only uses ASPP.
-  """
-
-  def __init__(self,
-               decoder_options,
-               vip_deeplab_options,
-               bn_layer=tf.keras.layers.BatchNormalization):
-    """Initializes a ViP-DeepLab decoder.
-
-    Args:
-      decoder_options: Decoder options as defined in config_pb2.DecoderOptions.
-      vip_deeplab_options: Model options as defined in
-        config_pb2.ModelOptions.ViPDeeplabOptions.
-      bn_layer: An optional tf.keras.layers.Layer that computes the
-        normalization (default: tf.keras.layers.BatchNormalization).
-    """
-    super(ViPDeepLabDecoder, self).__init__(name='ViPDeepLab')
-
-    low_level_feature_keys = [
-        item.feature_key for item in vip_deeplab_options.low_level
-    ]
-    low_level_channels_project = [
-        item.channels_project for item in vip_deeplab_options.low_level
-    ]
-
-    self._semantic_decoder = panoptic_deeplab.PanopticDeepLabSingleDecoder(
-        high_level_feature_name=decoder_options.feature_key,
-        low_level_feature_names=low_level_feature_keys,
-        low_level_channels_project=low_level_channels_project,
-        aspp_output_channels=decoder_options.aspp_channels,
-        decoder_output_channels=decoder_options.decoder_channels,
-        atrous_rates=decoder_options.atrous_rates,
-        name='semantic_decoder',
-        aspp_use_only_1x1_proj_conv=decoder_options.aspp_use_only_1x1_proj_conv,
-        decoder_conv_type=decoder_options.decoder_conv_type,
-        bn_layer=bn_layer)
-    self._semantic_head = panoptic_deeplab.PanopticDeepLabSingleHead(
-        vip_deeplab_options.semantic_head.head_channels,
-        vip_deeplab_options.semantic_head.output_channels,
-        common.PRED_SEMANTIC_LOGITS_KEY,
-        name='semantic_head',
-        conv_type=vip_deeplab_options.semantic_head.head_conv_type,
-        bn_layer=bn_layer)
-
-    self._instance_decoder = None
-    self._instance_center_head = None
-    self._instance_regression_head = None
-    self._next_instance_decoder = None
-    self._next_instance_regression_head = None
-
-    if vip_deeplab_options.instance.enable:
-      if vip_deeplab_options.instance.low_level_override:
-        low_level_options = vip_deeplab_options.instance.low_level_override
-      else:
-        low_level_options = vip_deeplab_options.low_level
-
-      # If instance_decoder is set, use those options; otherwise reuse the
-      # architecture as defined for the semantic decoder.
-      if vip_deeplab_options.instance.HasField(
-          'instance_decoder_override'):
-        decoder_options = (vip_deeplab_options.instance
-                           .instance_decoder_override)
-
-      low_level_feature_keys = [item.feature_key for item in low_level_options]
-      low_level_channels_project = [
-          item.channels_project for item in low_level_options
-      ]
-
-      self._instance_decoder = panoptic_deeplab.PanopticDeepLabSingleDecoder(
-          high_level_feature_name=decoder_options.feature_key,
-          low_level_feature_names=low_level_feature_keys,
-          low_level_channels_project=low_level_channels_project,
-          aspp_output_channels=decoder_options.aspp_channels,
-          decoder_output_channels=decoder_options.decoder_channels,
-          atrous_rates=decoder_options.atrous_rates,
-          name='instance_decoder',
-          aspp_use_only_1x1_proj_conv=(
-              decoder_options.aspp_use_only_1x1_proj_conv),
-          decoder_conv_type=decoder_options.decoder_conv_type,
-          bn_layer=bn_layer)
-      self._instance_center_head = panoptic_deeplab.PanopticDeepLabSingleHead(
-          vip_deeplab_options.instance.center_head.head_channels,
-          vip_deeplab_options.instance.center_head.output_channels,
-          common.PRED_CENTER_HEATMAP_KEY,
-          name='instance_center_head',
-          conv_type=(
-              vip_deeplab_options.instance.center_head.head_conv_type),
-          bn_layer=bn_layer)
-      self._instance_regression_head = (
-          panoptic_deeplab.PanopticDeepLabSingleHead(
-              vip_deeplab_options.instance.regression_head.head_channels,
-              vip_deeplab_options.instance.regression_head.output_channels,
-              common.PRED_OFFSET_MAP_KEY,
-              name='instance_regression_head',
-              conv_type=(
-                  vip_deeplab_options.instance.regression_head.head_conv_type),
-              bn_layer=bn_layer))
-
-      if vip_deeplab_options.instance.HasField('next_regression_head'):
-        self._next_instance_decoder = (
-            panoptic_deeplab.PanopticDeepLabSingleDecoder(
-                high_level_feature_name=decoder_options.feature_key,
-                low_level_feature_names=low_level_feature_keys,
-                low_level_channels_project=low_level_channels_project,
-                aspp_output_channels=decoder_options.aspp_channels,
-                decoder_output_channels=decoder_options.decoder_channels,
-                atrous_rates=decoder_options.atrous_rates,
-                name='next_instance_decoder',
-                aspp_use_only_1x1_proj_conv=(
-                    decoder_options.aspp_use_only_1x1_proj_conv),
-                decoder_conv_type=decoder_options.decoder_conv_type,
-                bn_layer=bn_layer))
-        self._next_instance_regression_head = (
-            panoptic_deeplab.PanopticDeepLabSingleHead(
-                (vip_deeplab_options.instance.next_regression_head
-                 .head_channels),
-                (vip_deeplab_options.instance.next_regression_head
-                 .output_channels),
-                common.PRED_NEXT_OFFSET_MAP_KEY,
-                name='next_instance_regression_head',
-                conv_type=(vip_deeplab_options.instance.next_regression_head
-                           .head_conv_type),
-                bn_layer=bn_layer))
-        self._next_high_level_feature_name = decoder_options.feature_key
-
-  def reset_pooling_layer(self):
-    """Resets the ASPP pooling layers to global average pooling."""
-    self._semantic_decoder.reset_pooling_layer()
-    if self._instance_decoder is not None:
-      self._instance_decoder.reset_pooling_layer()
-    if self._next_instance_decoder is not None:
-      self._next_instance_decoder.reset_pooling_layer()
-
-  def set_pool_size(self, pool_size):
-    """Sets the pooling size of the ASPP pooling layers.
-
-    Args:
-      pool_size: A tuple specifying the pooling size of the ASPP pooling layers.
-    """
-    self._semantic_decoder.set_pool_size(pool_size)
-    if self._instance_decoder is not None:
-      self._instance_decoder.set_pool_size(pool_size)
-    if self._next_instance_decoder is not None:
-      self._next_instance_decoder.set_pool_size(pool_size)
-
-  def get_pool_size(self):
-    return self._semantic_decoder.get_pool_size()
-
-  @property
-  def checkpoint_items(self):
-    items = {
-        common.CKPT_SEMANTIC_DECODER:
-            self._semantic_decoder,
-        common.CKPT_SEMANTIC_HEAD_WITHOUT_LAST_LAYER:
-            self._semantic_head.conv_block,
-        common.CKPT_SEMANTIC_LAST_LAYER:
-            self._semantic_head.final_conv
-    }
-    if self._instance_decoder is not None:
-      instance_items = {
-          common.CKPT_INSTANCE_DECODER:
-              self._instance_decoder,
-          common.CKPT_INSTANCE_CENTER_HEAD_WITHOUT_LAST_LAYER:
-              self._instance_center_head.conv_block,
-          common.CKPT_INSTANCE_CENTER_HEAD_LAST_LAYER:
-              self._instance_center_head.final_conv,
-          common.CKPT_INSTANCE_REGRESSION_HEAD_WITHOUT_LAST_LAYER:
-              self._instance_regression_head.conv_block,
-          common.CKPT_INSTANCE_REGRESSION_HEAD_LAST_LAYER:
-              self._instance_regression_head.final_conv,
-      }
-      items.update(instance_items)
-    if self._next_instance_decoder is not None:
-      next_instance_items = {
-          common.CKPT_NEXT_INSTANCE_DECODER:
-              self._next_instance_decoder,
-          common.CKPT_NEXT_INSTANCE_REGRESSION_HEAD_WITHOUT_LAST_LAYER:
-              self._next_instance_regression_head.conv_block,
-          common.CKPT_NEXT_INSTANCE_REGRESSION_HEAD_LAST_LAYER:
-              self._next_instance_regression_head.final_conv,
-      }
-      items.update(next_instance_items)
-    return items
-
-  def call(self, features, next_features, training=False):
-    """Performs a forward pass.
-
-    Args:
-      features: An input dict of tf.Tensor with shape [batch, height, width,
-        channels]. Different keys should point to different features extracted
-        by the encoder, e.g. low-level or high-level features.
-      next_features: An input dict of tf.Tensor similar to features. The
-        features are computed with the next frame as input.
-      training: A boolean flag indicating whether training behavior should be
-        used (default: False).
-
-    Returns:
-      A dictionary containing the results of the semantic segmentation head and
-        depending on the configuration also of the instance segmentation head.
-    """
-
-    semantic_features = self._semantic_decoder(features, training=training)
-    results = self._semantic_head(semantic_features, training=training)
-
-    if self._instance_decoder is not None:
-      instance_features = self._instance_decoder(features, training=training)
-      instance_center_predictions = self._instance_center_head(
-          instance_features, training=training)
-      instance_regression_predictions = self._instance_regression_head(
-          instance_features, training=training)
-
-      if results.keys() & instance_center_predictions.keys():
-        raise ValueError('The keys of the semantic branch and the instance '
-                         'center branch overlap. Please use unique keys.')
-      results.update(instance_center_predictions)
-
-      if results.keys() & instance_regression_predictions.keys():
-        raise ValueError('The keys of the semantic branch and the instance '
-                         'regression branch overlap. Please use unique keys.')
-      results.update(instance_regression_predictions)
-
-    if self._next_instance_decoder is not None:
-      # We update the high level features in next_features with the concated
-      # features of the high level features in both features and next_features.
-      high_level_feature_name = self._next_high_level_feature_name
-      high_level_features = features[high_level_feature_name]
-      next_high_level_features = next_features[high_level_feature_name]
-      next_high_level_features = tf.concat(
-          [high_level_features, next_high_level_features], axis=3)
-      next_features[high_level_feature_name] = next_high_level_features
-      next_regression_features = self._next_instance_decoder(
-          next_features, training=training)
-      next_regression_predictions = self._next_instance_regression_head(
-          next_regression_features, training=training)
-      if results.keys() & next_regression_predictions.keys():
-        raise ValueError('The keys of the next regresion branch overlap.'
-                         'Please use unique keys.')
-      results.update(next_regression_predictions)
-
-    return results
diff --git a/spaces/akhaliq/yolov7/utils/aws/resume.py b/spaces/akhaliq/yolov7/utils/aws/resume.py
deleted file mode 100644
index 338685b19c19ddb47aa2fde22a535a8efcf17802..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/yolov7/utils/aws/resume.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Resume all interrupted trainings in yolor/ dir including DDP trainings
-# Usage: $ python utils/aws/resume.py
-
-import os
-import sys
-from pathlib import Path
-
-import torch
-import yaml
-
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
-
-port = 0  # --master_port
-path = Path('').resolve()
-for last in path.rglob('*/**/last.pt'):
-    ckpt = torch.load(last)
-    if ckpt['optimizer'] is None:
-        continue
-
-    # Load opt.yaml
-    with open(last.parent.parent / 'opt.yaml') as f:
-        opt = yaml.load(f, Loader=yaml.SafeLoader)
-
-    # Get device count
-    d = opt['device'].split(',')  # devices
-    nd = len(d)  # number of devices
-    ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1)  # distributed data parallel
-
-    if ddp:  # multi-GPU
-        port += 1
-        cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}'
-    else:  # single-GPU
-        cmd = f'python train.py --resume {last}'
-
-    cmd += ' > /dev/null 2>&1 &'  # redirect output to dev/null and run in daemon thread
-    print(cmd)
-    os.system(cmd)
diff --git a/spaces/algomuffin/jojo_fork/e4e/datasets/gt_res_dataset.py b/spaces/algomuffin/jojo_fork/e4e/datasets/gt_res_dataset.py
deleted file mode 100644
index c0beacfee5335aa10aa7e8b7cabe206d7f9a56f7..0000000000000000000000000000000000000000
--- a/spaces/algomuffin/jojo_fork/e4e/datasets/gt_res_dataset.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/python
-# encoding: utf-8
-import os
-from torch.utils.data import Dataset
-from PIL import Image
-import torch
-
-class GTResDataset(Dataset):
-
-	def __init__(self, root_path, gt_dir=None, transform=None, transform_train=None):
-		self.pairs = []
-		for f in os.listdir(root_path):
-			image_path = os.path.join(root_path, f)
-			gt_path = os.path.join(gt_dir, f)
-			if f.endswith(".jpg") or f.endswith(".png"):
-				self.pairs.append([image_path, gt_path.replace('.png', '.jpg'), None])
-		self.transform = transform
-		self.transform_train = transform_train
-
-	def __len__(self):
-		return len(self.pairs)
-
-	def __getitem__(self, index):
-		from_path, to_path, _ = self.pairs[index]
-		from_im = Image.open(from_path).convert('RGB')
-		to_im = Image.open(to_path).convert('RGB')
-
-		if self.transform:
-			to_im = self.transform(to_im)
-			from_im = self.transform(from_im)
-
-		return from_im, to_im
diff --git a/spaces/ali-ghamdan/deoldify/deoldify/critics.py b/spaces/ali-ghamdan/deoldify/deoldify/critics.py
deleted file mode 100644
index adea9e5cfbb7493ff352195fedb1158f84b79ae2..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/deoldify/critics.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from fastai.basic_train import Learner
-from fastai.core import *
-from fastai.layers import NormType, conv_layer
-from fastai.torch_core import *
-from fastai.vision import *
-from fastai.vision.data import ImageDataBunch
-from fastai.vision.gan import AdaptiveLoss, accuracy_thresh_expand
-
-_conv_args = dict(leaky=0.2, norm_type=NormType.Spectral)
-
-
-def _conv(ni: int, nf: int, ks: int = 3, stride: int = 1, **kwargs):
-    return conv_layer(ni, nf, ks=ks, stride=stride, **_conv_args, **kwargs)
-
-
-def custom_gan_critic(
-    n_channels: int = 3, nf: int = 256, n_blocks: int = 3, p: int = 0.15
-):
-    "Critic to train a `GAN`."
-    layers = [_conv(n_channels, nf, ks=4, stride=2), nn.Dropout2d(p / 2)]
-    for i in range(n_blocks):
-        layers += [
-            _conv(nf, nf, ks=3, stride=1),
-            nn.Dropout2d(p),
-            _conv(nf, nf * 2, ks=4, stride=2, self_attention=(i == 0)),
-        ]
-        nf *= 2
-    layers += [
-        _conv(nf, nf, ks=3, stride=1),
-        _conv(nf, 1, ks=4, bias=False, padding=0, use_activ=False),
-        Flatten(),
-    ]
-    return nn.Sequential(*layers)
-
-
-def colorize_crit_learner(
-    data: ImageDataBunch,
-    loss_critic=AdaptiveLoss(nn.BCEWithLogitsLoss()),
-    nf: int = 256,
-) -> Learner:
-    return Learner(
-        data,
-        custom_gan_critic(nf=nf),
-        metrics=accuracy_thresh_expand,
-        loss_func=loss_critic,
-        wd=1e-3,
-    )
diff --git a/spaces/allandclive/Uganda_MMS/vits/preprocess.py b/spaces/allandclive/Uganda_MMS/vits/preprocess.py
deleted file mode 100644
index aaedbf076c30114b3ac6c27dfb42fd54ac81a71c..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/vits/preprocess.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import argparse
-import text
-from utils import load_filepaths_and_text
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--out_extension", default="cleaned")
-  parser.add_argument("--text_index", default=1, type=int)
-  parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
-  parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
-
-  args = parser.parse_args()
-    
-
-  for filelist in args.filelists:
-    print("START:", filelist)
-    filepaths_and_text = load_filepaths_and_text(filelist)
-    for i in range(len(filepaths_and_text)):
-      original_text = filepaths_and_text[i][args.text_index]
-      cleaned_text = text._clean_text(original_text, args.text_cleaners)
-      filepaths_and_text[i][args.text_index] = cleaned_text
-
-    new_filelist = filelist + "." + args.out_extension
-    with open(new_filelist, "w", encoding="utf-8") as f:
-      f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])
diff --git a/spaces/allknowingroger/Image-Models-Test152/app.py b/spaces/allknowingroger/Image-Models-Test152/app.py
deleted file mode 100644
index 21e3a0b46ec71fc20035e57fc5e58734bd0089cc..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test152/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "Yntec/elldrethsImagination",
-    "akmalinn/monument-surabaya",
-    "DavideTHU/lora-trained-xl-mll",
-    "CiroN2022/escher-blend",
-    "jmanuoz/lora-trained-xl-ef",
-    "roborac/rcp173_2",
-    "bongo2112/sdxl-db-magufuli-headshot",
-    "bunnychakri/nature-scr",
-    "lccllccc/textual_inversion_tangseng_sdxl_lora",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/amber0097/amberSign/unidbg-fetch-qsign/bin/unidbg-fetch-qsign.bat b/spaces/amber0097/amberSign/unidbg-fetch-qsign/bin/unidbg-fetch-qsign.bat
deleted file mode 100644
index 4e44bab8aa65d16e35e935f1273de2e98ce80cf9..0000000000000000000000000000000000000000
--- a/spaces/amber0097/amberSign/unidbg-fetch-qsign/bin/unidbg-fetch-qsign.bat
+++ /dev/null
@@ -1,89 +0,0 @@
-@rem
-@rem Copyright 2015 the original author or authors.
-@rem
-@rem Licensed under the Apache License, Version 2.0 (the "License");
-@rem you may not use this file except in compliance with the License.
-@rem You may obtain a copy of the License at
-@rem
-@rem      https://www.apache.org/licenses/LICENSE-2.0
-@rem
-@rem Unless required by applicable law or agreed to in writing, software
-@rem distributed under the License is distributed on an "AS IS" BASIS,
-@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-@rem See the License for the specific language governing permissions and
-@rem limitations under the License.
-@rem
-
-@if "%DEBUG%" == "" @echo off
-@rem ##########################################################################
-@rem
-@rem  unidbg-fetch-qsign startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%..
-
-@rem Resolve any "." and ".." in APP_HOME to make it shorter.
-for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and UNIDBG_FETCH_QSIGN_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:execute
-@rem Setup the command line
-
-set CLASSPATH=%APP_HOME%\lib\unidbg-fetch-qsign-1.1.0.jar;%APP_HOME%\lib\unidbg-fix.jar;%APP_HOME%\lib\ktor-server-content-negotiation-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-json-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-netty-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-host-common-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-core-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-events-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-websockets-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-cio-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-network-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-utils-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-io-jvm-2.3.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk8-1.8.22.jar;%APP_HOME%\lib\kotlinx-serialization-json-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-protobuf-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-core-jvm-1.5.1.jar;%APP_HOME%\lib\logback-classic-1.2.11.jar;%APP_HOME%\lib\kotlinx-coroutines-jdk8-1.7.1.jar;%APP_HOME%\lib\kotlinx-coroutines-core-jvm-1.7.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk7-1.8.22.jar;%APP_HOME%\lib\kotlin-reflect-1.8.10.jar;%APP_HOME%\lib\kotlin-stdlib-1.8.22.jar;%APP_HOME%\lib\slf4j-api-1.7.36.jar;%APP_HOME%\lib\kotlin-stdlib-common-1.8.22.jar;%APP_HOME%\lib\config-1.4.2.jar;%APP_HOME%\lib\jansi-2.4.0.jar;%APP_HOME%\lib\netty-codec-http2-4.1.92.Final.jar;%APP_HOME%\lib\alpn-api-1.1.3.v20160715.jar;%APP_HOME%\lib\netty-transport-native-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-epoll-4.1.92.Final.jar;%APP_HOME%\lib\logback-core-1.2.11.jar;%APP_HOME%\lib\annotations-23.0.0.jar;%APP_HOME%\lib\netty-codec-http-4.1.92.Final.jar;%APP_HOME%\lib\netty-handler-4.1.92.Final.jar;%APP_HOME%\lib\netty-codec-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-epoll-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-unix-common-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-4.1.92.Final.jar;%APP_HOME%\lib\netty-buffer-4.1.92.Final.jar;%APP_HOME%\lib\netty-resolver-4.1.92.Final.jar;%APP_HOME%\lib\netty-common-4.1.92.Final.jar
-
-
-@rem Execute unidbg-fetch-qsign
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %UNIDBG_FETCH_QSIGN_OPTS%  -classpath "%CLASSPATH%" MainKt %*
-
-:end
-@rem End local scope for the variables with windows NT shell
-if "%ERRORLEVEL%"=="0" goto mainEnd
-
-:fail
-rem Set variable UNIDBG_FETCH_QSIGN_EXIT_CONSOLE if you need the _script_ return code instead of
-rem the _cmd.exe /c_ return code!
-if  not "" == "%UNIDBG_FETCH_QSIGN_EXIT_CONSOLE%" exit 1
-exit /b 1
-
-:mainEnd
-if "%OS%"=="Windows_NT" endlocal
-
-:omega
diff --git a/spaces/andresgtn/face-id/app.py b/spaces/andresgtn/face-id/app.py
deleted file mode 100644
index 65d096f78efbd1dc1da2c9527ffc657f9579790f..0000000000000000000000000000000000000000
--- a/spaces/andresgtn/face-id/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import matplotlib.pyplot as plt
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-import gradio as gr
-
-model = SentenceTransformer("sentence-transformers/clip-ViT-L-14")
-
-def predict(im1, im2):
-  embeddings = model.encode([im1, im2])
-  sim = cosine_similarity(embeddings[0][None,:], embeddings[1][None,:])[0][0]
-  # ANSWER HERE
-  if sim > 0.78:
-    return sim, "SAME PERSON, UNLOCK PHONE"
-  else:
-    return sim, "DIFFERENT PEOPLE, DON'T UNLOCK"
-    
-    
-title = "Face ID"
-description = "Upload two selfies and find out if they are of the same person."
-interface = gr.Interface(fn=predict, 
-                         inputs= [gr.Image(type="pil", source="webcam"), 
-                                  gr.Image(type="pil", source="webcam")], 
-                         outputs= [gr.Number(label="Similarity"),
-                                   gr.Textbox(label="Message")],
-                         title=title,
-                         description=description
-                         )
-
-interface.launch(debug=False)
\ No newline at end of file
diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Better.py b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Better.py
deleted file mode 100644
index bee52870eb3300f25c9762ab204968791a2a30a9..0000000000000000000000000000000000000000
--- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/g4f/Provider/Providers/Better.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-import json
-import requests
-from typing import Dict, get_type_hints
-
-url = 'https://openai-proxy-api.vercel.app/v1/'
-model = [
-    'gpt-3.5-turbo',
-    'gpt-3.5-turbo-0613',
-    'gpt-3.5-turbo-16k',
-    'gpt-3.5-turbo-16k-0613',
-    'gpt-4',
-]
-
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36 Edg/114.0.1823.58',
-        'Referer': 'https://chat.ylokh.xyz/',
-        'Origin': 'https://chat.ylokh.xyz',
-        'Connection': 'keep-alive',  
-    }
-
-    json_data = {
-        'messages': messages,
-        'temperature': 1.0,
-        'model': model,
-        'stream': stream,
-    }
-
-    response = requests.post(
-        'https://openai-proxy-api.vercel.app/v1/chat/completions', headers=headers, json=json_data, stream=True
-    )
-
-    for token in response.iter_lines():
-        decoded = token.decode('utf-8')
-        if decoded.startswith('data: '):
-            data_str = decoded.replace('data: ', '')
-            data = json.loads(data_str)
-            if 'choices' in data and 'delta' in data['choices'][0]:
-                delta = data['choices'][0]['delta']
-                content = delta.get('content', '')
-                finish_reason = delta.get('finish_reason', '')
-
-                if finish_reason == 'stop':
-                    break
-                if content:
-                    yield content
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + '(%s)' % ', '.join(
-    [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/anzorq/zedzek/ctranslate/README.md b/spaces/anzorq/zedzek/ctranslate/README.md
deleted file mode 100644
index acee006dfe48f1c174db5dc5f03deb495e397c70..0000000000000000000000000000000000000000
--- a/spaces/anzorq/zedzek/ctranslate/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-`anzorq/m2m100_418M_ft_ru-kbd_44K` converted to `ctranslate2` format with int8 quantization.
-
-```bash
-ct2-transformers-converter --model anzorq/m2m100_418M_ft_ru-kbd_44K --output_dir m2m100_418M_ru-kbd --force --quantization int8
-```
\ No newline at end of file
diff --git a/spaces/appy-agency/sprigs/README.md b/spaces/appy-agency/sprigs/README.md
deleted file mode 100644
index d5e2bfb6c299fa17961a7334d99247723dd8bd2e..0000000000000000000000000000000000000000
--- a/spaces/appy-agency/sprigs/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sprigs
-emoji: 🐢
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arbml/Ashaar/poetry_diacritizer/util/utils.py b/spaces/arbml/Ashaar/poetry_diacritizer/util/utils.py
deleted file mode 100644
index e9d33ca2361e48e9781cfee644dd9ddcffd6a59a..0000000000000000000000000000000000000000
--- a/spaces/arbml/Ashaar/poetry_diacritizer/util/utils.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import os
-from typing import Any
-
-import matplotlib.pyplot as plt
-import torch
-from torch import nn
-from itertools import repeat
-from poetry_diacritizer.util.decorators import ignore_exception
-from dataclasses import dataclass
-import numpy as np
-
-
-@dataclass
-class ErrorRate:
-    wer: float
-    der: float
-    wer_without_case_ending: float
-    der_without_case_ending: float
-
-
-def epoch_time(start_time, end_time):
-    elapsed_time = end_time - start_time
-    elapsed_mins = int(elapsed_time / 60)
-    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
-    return elapsed_mins, elapsed_secs
-
-
-@ignore_exception
-def plot_alignment(alignment: torch.Tensor, path: str, global_step: Any = 0):
-    """
-    Plot alignment and save it into a path
-    Args:
-    alignment (Tensor): the encoder-decoder alignment
-    path (str): a path used to save the alignment plot
-    global_step (int): used in the name of the output alignment plot
-    """
-    alignment = alignment.squeeze(1).transpose(0, 1).cpu().detach().numpy()
-    fig, axs = plt.subplots()
-    img = axs.imshow(alignment, aspect="auto", origin="lower", interpolation="none")
-    fig.colorbar(img, ax=axs)
-    xlabel = "Decoder timestep"
-    plt.xlabel(xlabel)
-    plt.ylabel("Encoder timestep")
-    plt.tight_layout()
-    plot_name = f"{global_step}.png"
-    plt.savefig(os.path.join(path, plot_name), dpi=300, format="png")
-    plt.close()
-
-
-def get_mask_from_lengths(memory, memory_lengths):
-    """Get mask tensor from list of length
-    Args:
-        memory: (batch, max_time, dim)
-        memory_lengths: array like
-    """
-    mask = memory.data.new(memory.size(0), memory.size(1)).bool().zero_()
-    for idx, length in enumerate(memory_lengths):
-        mask[idx][:length] = 1
-    return ~mask
-
-
-def repeater(data_loader):
-    for loader in repeat(data_loader):
-        for data in loader:
-            yield data
-
-
-def count_parameters(model):
-    return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-
-def initialize_weights(m):
-    if hasattr(m, "weight") and m.weight.dim() > 1:
-        nn.init.xavier_uniform_(m.weight.data)
-
-
-def get_encoder_layers_attentions(model):
-    attentions = []
-    for layer in model.encoder.layers:
-        attentions.append(layer.self_attention.attention)
-    return attentions
-
-
-def get_decoder_layers_attentions(model):
-    self_attns, src_attens = [], []
-    for layer in model.decoder.layers:
-        self_attns.append(layer.self_attention.attention)
-        src_attens.append(layer.encoder_attention.attention)
-    return self_attns, src_attens
-
-
-def display_attention(
-    attention, path, global_step: int, name="att", n_heads=4, n_rows=2, n_cols=2
-):
-    assert n_rows * n_cols == n_heads
-
-    fig = plt.figure(figsize=(15, 15))
-
-    for i in range(n_heads):
-
-        ax = fig.add_subplot(n_rows, n_cols, i + 1)
-
-        _attention = attention.squeeze(0)[i].transpose(0, 1).cpu().detach().numpy()
-        cax = ax.imshow(_attention, aspect="auto", origin="lower", interpolation="none")
-
-    plot_name = f"{global_step}-{name}.png"
-    plt.savefig(os.path.join(path, plot_name), dpi=300, format="png")
-    plt.close()
-
-
-def plot_multi_head(model, path, global_step):
-    encoder_attentions = get_encoder_layers_attentions(model)
-    decoder_attentions, attentions = get_decoder_layers_attentions(model)
-    for i in range(len(attentions)):
-        display_attention(
-            attentions[0][0], path, global_step, f"encoder-decoder-layer{i + 1}"
-        )
-    for i in range(len(decoder_attentions)):
-        display_attention(
-            decoder_attentions[0][0], path, global_step, f"decoder-layer{i + 1}"
-        )
-    for i in range(len(encoder_attentions)):
-        display_attention(
-            encoder_attentions[0][0], path, global_step, f"encoder-layer {i + 1}"
-        )
-
-
-def make_src_mask(src, pad_idx=0):
-
-    # src = [batch size, src len]
-
-    src_mask = (src != pad_idx).unsqueeze(1).unsqueeze(2)
-
-    # src_mask = [batch size, 1, 1, src len]
-
-    return src_mask
-
-
-def get_angles(pos, i, model_dim):
-    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(model_dim))
-    return pos * angle_rates
-
-
-def positional_encoding(position, model_dim):
-    angle_rads = get_angles(
-        np.arange(position)[:, np.newaxis],
-        np.arange(model_dim)[np.newaxis, :],
-        model_dim,
-    )
-
-    # apply sin to even indices in the array; 2i
-    angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
-
-    # apply cos to odd indices in the array; 2i+1
-    angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])
-
-    pos_encoding = angle_rads[np.newaxis, ...]
-
-    return torch.from_numpy(pos_encoding)
-
-
-def calculate_error_rates(original_file_path: str, target_file_path: str) -> ErrorRate:
-    """
-    Calculates ErrorRates from paths
-    """
-    assert os.path.isfile(original_file_path)
-    assert os.path.isfile(target_file_path)
-
-    _wer = wer.calculate_wer_from_path(
-        inp_path=original_file_path, out_path=target_file_path, case_ending=True
-    )
-
-    _wer_without_case_ending = wer.calculate_wer_from_path(
-        inp_path=original_file_path, out_path=target_file_path, case_ending=False
-    )
-
-    _der = der.calculate_der_from_path(
-        inp_path=original_file_path, out_path=target_file_path, case_ending=True
-    )
-
-    _der_without_case_ending = der.calculate_der_from_path(
-        inp_path=original_file_path, out_path=target_file_path, case_ending=False
-    )
-
-    error_rates = ErrorRate(
-        _wer,
-        _der,
-        _wer_without_case_ending,
-        _der_without_case_ending,
-    )
-
-    return error_rates
-
-
-def categorical_accuracy(preds, y, tag_pad_idx, device="cuda"):
-    """
-    Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8
-    """
-    max_preds = preds.argmax(
-        dim=1, keepdim=True
-    )  # get the index of the max probability
-    non_pad_elements = torch.nonzero((y != tag_pad_idx))
-    correct = max_preds[non_pad_elements].squeeze(1).eq(y[non_pad_elements])
-    return correct.sum() / torch.FloatTensor([y[non_pad_elements].shape[0]]).to(device)
-
-
-def write_to_files(input_path, output_path, input_list, output_list):
-    with open(input_path, "w", encoding="utf8") as file:
-        for inp in input_list:
-            file.write(inp + "\n")
-    with open(output_path, "w", encoding="utf8") as file:
-        for out in output_list:
-            file.write(out + "\n")
-
-
-def make_src_mask(src: torch.Tensor, pad_idx=0):
-    return (src != pad_idx).unsqueeze(1).unsqueeze(2)
-
-
-def make_trg_mask(trg, trg_pad_idx=0):
-
-    # trg = [batch size, trg len]
-
-    trg_pad_mask = (trg != trg_pad_idx).unsqueeze(1).unsqueeze(2)
-
-    # trg_pad_mask = [batch size, 1, 1, trg len]
-
-    trg_len = trg.shape[1]
-
-    trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len))).bool()
-
-    # trg_sub_mask = [trg len, trg len]
-
-    trg_mask = trg_pad_mask & trg_sub_mask
-
-    # trg_mask = [batch size, 1, trg len, trg len]
-
-    return trg_mask
diff --git a/spaces/arihantvyavhare/device_detector_img2txt/converter.py b/spaces/arihantvyavhare/device_detector_img2txt/converter.py
deleted file mode 100644
index 6d676c85f616e49b04a091700d0416366723380d..0000000000000000000000000000000000000000
--- a/spaces/arihantvyavhare/device_detector_img2txt/converter.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import json
-
-file = 'tips.ipynb'
-
-code = json.load(open(file))
-filename = file.split(".")[0]
-py_file = open(f"{filename}.py", "w+")
-
-for cell in code['cells']:
-    if cell['cell_type'] == 'code':
-        if cell['source'] and "#| export" in cell['source'][0]:
-            for line in cell['source'][1:]:
-                py_file.write(line)
-        py_file.write("\n")
-    elif cell['cell_type'] == 'markdown':
-        py_file.write("\n")
-        for line in cell['source']:
-            if line and line[0] == "#":
-                py_file.write(line)
-            else:
-                py_file.write("#* " + line)
-        py_file.write("\n")
-
-py_file.close()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_pkcs1_15.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_pkcs1_15.py
deleted file mode 100644
index 8e2c6ee618d0092ace3af294a626fc899766511d..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_pkcs1_15.py
+++ /dev/null
@@ -1,348 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin <helderijs@gmail.com>
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-import json
-import unittest
-from binascii import unhexlify
-
-from Crypto.Util.py3compat import bchr
-from Crypto.Util.number import bytes_to_long
-from Crypto.Util.strxor import strxor
-from Crypto.SelfTest.st_common import list_test_cases
-from Crypto.SelfTest.loader import load_test_vectors, load_test_vectors_wycheproof
-
-from Crypto.Hash import (SHA1, SHA224, SHA256, SHA384, SHA512, SHA3_384,
-                         SHA3_224, SHA3_256, SHA3_512)
-from Crypto.PublicKey import RSA
-from Crypto.Signature import pkcs1_15
-from Crypto.Signature import PKCS1_v1_5
-
-from Crypto.Util._file_system import pycryptodome_filename
-from Crypto.Util.strxor import strxor
-
-
-def load_hash_by_name(hash_name):
-    return __import__("Crypto.Hash." + hash_name, globals(), locals(), ["new"])
-
-
-class FIPS_PKCS1_Verify_Tests(unittest.TestCase):
-
-    def shortDescription(self):
-        return "FIPS PKCS1 Tests (Verify)"
-
-    def test_can_sign(self):
-        test_public_key = RSA.generate(1024).public_key()
-        verifier = pkcs1_15.new(test_public_key)
-        self.assertEqual(verifier.can_sign(), False)
-
-
-class FIPS_PKCS1_Verify_Tests_KAT(unittest.TestCase):
-    pass
-
-
-test_vectors_verify = load_test_vectors(("Signature", "PKCS1-v1.5"),
-                                 "SigVer15_186-3.rsp",
-                                 "Signature Verification 186-3",
-                                 {'shaalg': lambda x: x,
-                                  'd': lambda x: int(x),
-                                  'result': lambda x: x}) or []
-
-
-for count, tv in enumerate(test_vectors_verify):
-    if isinstance(tv, str):
-        continue
-    if hasattr(tv, "n"):
-        modulus = tv.n
-        continue
-
-    hash_module = load_hash_by_name(tv.shaalg.upper())
-    hash_obj = hash_module.new(tv.msg)
-    public_key = RSA.construct([bytes_to_long(x) for x in (modulus, tv.e)]) # type: ignore
-    verifier = pkcs1_15.new(public_key)
-
-    def positive_test(self, hash_obj=hash_obj, verifier=verifier, signature=tv.s):
-        verifier.verify(hash_obj, signature)
-
-    def negative_test(self, hash_obj=hash_obj, verifier=verifier, signature=tv.s):
-        self.assertRaises(ValueError, verifier.verify, hash_obj, signature)
-
-    if tv.result == 'f':
-        setattr(FIPS_PKCS1_Verify_Tests_KAT, "test_negative_%d" % count, negative_test)
-    else:
-        setattr(FIPS_PKCS1_Verify_Tests_KAT, "test_positive_%d" % count, positive_test)
-
-
-class FIPS_PKCS1_Sign_Tests(unittest.TestCase):
-
-    def shortDescription(self):
-        return "FIPS PKCS1 Tests (Sign)"
-
-    def test_can_sign(self):
-        test_private_key = RSA.generate(1024)
-        signer = pkcs1_15.new(test_private_key)
-        self.assertEqual(signer.can_sign(), True)
-
-
-class FIPS_PKCS1_Sign_Tests_KAT(unittest.TestCase):
-    pass
-
-
-test_vectors_sign  = load_test_vectors(("Signature", "PKCS1-v1.5"),
-                                        "SigGen15_186-2.txt",
-                                        "Signature Generation 186-2",
-                                        {'shaalg': lambda x: x}) or []
-
-test_vectors_sign += load_test_vectors(("Signature", "PKCS1-v1.5"),
-                                        "SigGen15_186-3.txt",
-                                        "Signature Generation 186-3",
-                                        {'shaalg': lambda x: x}) or []
-
-for count, tv in enumerate(test_vectors_sign):
-    if isinstance(tv, str):
-        continue
-    if hasattr(tv, "n"):
-        modulus = tv.n
-        continue
-    if hasattr(tv, "e"):
-        private_key = RSA.construct([bytes_to_long(x) for x in (modulus, tv.e, tv.d)]) # type: ignore
-        signer = pkcs1_15.new(private_key)
-        continue
-
-    hash_module = load_hash_by_name(tv.shaalg.upper())
-    hash_obj = hash_module.new(tv.msg)
-
-    def new_test(self, hash_obj=hash_obj, signer=signer, result=tv.s):
-        signature = signer.sign(hash_obj)
-        self.assertEqual(signature, result)
-
-    setattr(FIPS_PKCS1_Sign_Tests_KAT, "test_%d" % count, new_test)
-
-
-class PKCS1_15_NoParams(unittest.TestCase):
-    """Verify that PKCS#1 v1.5 signatures pass even without NULL parameters in
-    the algorithm identifier (PyCrypto/LP bug #1119552)."""
-
-    rsakey = """-----BEGIN RSA PRIVATE KEY-----
-            MIIBOwIBAAJBAL8eJ5AKoIsjURpcEoGubZMxLD7+kT+TLr7UkvEtFrRhDDKMtuII
-            q19FrL4pUIMymPMSLBn3hJLe30Dw48GQM4UCAwEAAQJACUSDEp8RTe32ftq8IwG8
-            Wojl5mAd1wFiIOrZ/Uv8b963WJOJiuQcVN29vxU5+My9GPZ7RA3hrDBEAoHUDPrI
-            OQIhAPIPLz4dphiD9imAkivY31Rc5AfHJiQRA7XixTcjEkojAiEAyh/pJHks/Mlr
-            +rdPNEpotBjfV4M4BkgGAA/ipcmaAjcCIQCHvhwwKVBLzzTscT2HeUdEeBMoiXXK
-            JACAr3sJQJGxIQIgarRp+m1WSKV1MciwMaTOnbU7wxFs9DP1pva76lYBzgUCIQC9
-            n0CnZCJ6IZYqSt0H5N7+Q+2Ro64nuwV/OSQfM6sBwQ==
-            -----END RSA PRIVATE KEY-----"""
-
-    msg = b"This is a test\x0a"
-
-    # PKCS1 v1.5 signature of the message computed using SHA-1.
-    # The digestAlgorithm SEQUENCE does NOT contain the NULL parameter.
-    sig_str = "a287a13517f716e72fb14eea8e33a8db4a4643314607e7ca3e3e28"\
-              "1893db74013dda8b855fd99f6fecedcb25fcb7a434f35cd0a101f8"\
-              "b19348e0bd7b6f152dfc"
-    signature = unhexlify(sig_str)
-
-    def runTest(self):
-        verifier = pkcs1_15.new(RSA.importKey(self.rsakey))
-        hashed = SHA1.new(self.msg)
-        verifier.verify(hashed, self.signature)
-
-
-class PKCS1_Legacy_Module_Tests(unittest.TestCase):
-    """Verify that the legacy module Crypto.Signature.PKCS1_v1_5
-    behaves as expected. The only difference is that the verify()
-    method returns True/False and does not raise exceptions."""
-
-    def shortDescription(self):
-        return "Test legacy Crypto.Signature.PKCS1_v1_5"
-
-    def runTest(self):
-        key = RSA.importKey(PKCS1_15_NoParams.rsakey)
-        hashed = SHA1.new(b"Test")
-        good_signature = PKCS1_v1_5.new(key).sign(hashed)
-        verifier = PKCS1_v1_5.new(key.public_key())
-
-        self.assertEqual(verifier.verify(hashed, good_signature), True)
-
-        # Flip a few bits in the signature
-        bad_signature = strxor(good_signature, bchr(1) * len(good_signature))
-        self.assertEqual(verifier.verify(hashed, bad_signature), False)
-
-
-class PKCS1_All_Hashes_Tests(unittest.TestCase):
-
-    def shortDescription(self):
-        return "Test PKCS#1v1.5 signature in combination with all hashes"
-
-    def runTest(self):
-
-        key = RSA.generate(1024)
-        signer = pkcs1_15.new(key)
-        hash_names = ("MD2", "MD4", "MD5", "RIPEMD160", "SHA1",
-                      "SHA224", "SHA256", "SHA384", "SHA512",
-                      "SHA3_224", "SHA3_256", "SHA3_384", "SHA3_512")
-
-        for name in hash_names:
-            hashed = load_hash_by_name(name).new(b"Test")
-            signer.sign(hashed)
-
-        from Crypto.Hash import BLAKE2b, BLAKE2s
-        for hash_size in (20, 32, 48, 64):
-            hashed_b = BLAKE2b.new(digest_bytes=hash_size, data=b"Test")
-            signer.sign(hashed_b)
-        for hash_size in (16, 20, 28, 32):
-            hashed_s = BLAKE2s.new(digest_bytes=hash_size, data=b"Test")
-            signer.sign(hashed_s)
-
-
-class TestVectorsWycheproof(unittest.TestCase):
-
-    def __init__(self, wycheproof_warnings):
-        unittest.TestCase.__init__(self)
-        self._wycheproof_warnings = wycheproof_warnings
-        self._id = "None"
-
-    def setUp(self):
-        self.tv = []
-        self.add_tests("rsa_sig_gen_misc_test.json")
-        self.add_tests("rsa_signature_2048_sha224_test.json")
-        self.add_tests("rsa_signature_2048_sha256_test.json")
-        self.add_tests("rsa_signature_2048_sha384_test.json")
-        self.add_tests("rsa_signature_2048_sha3_224_test.json")
-        self.add_tests("rsa_signature_2048_sha3_256_test.json")
-        self.add_tests("rsa_signature_2048_sha3_384_test.json")
-        self.add_tests("rsa_signature_2048_sha3_512_test.json")
-        self.add_tests("rsa_signature_2048_sha512_test.json")
-        self.add_tests("rsa_signature_2048_sha512_224_test.json")
-        self.add_tests("rsa_signature_2048_sha512_256_test.json")
-        self.add_tests("rsa_signature_3072_sha256_test.json")
-        self.add_tests("rsa_signature_3072_sha384_test.json")
-        self.add_tests("rsa_signature_3072_sha3_256_test.json")
-        self.add_tests("rsa_signature_3072_sha3_384_test.json")
-        self.add_tests("rsa_signature_3072_sha3_512_test.json")
-        self.add_tests("rsa_signature_3072_sha512_test.json")
-        self.add_tests("rsa_signature_3072_sha512_256_test.json")
-        self.add_tests("rsa_signature_4096_sha384_test.json")
-        self.add_tests("rsa_signature_4096_sha512_test.json")
-        self.add_tests("rsa_signature_4096_sha512_256_test.json")
-        self.add_tests("rsa_signature_test.json")
-
-    def add_tests(self, filename):
-
-        def filter_rsa(group):
-            return RSA.import_key(group['keyPem'])
-
-        def filter_sha(group):
-            hash_name = group['sha']
-            if hash_name == "SHA-512":
-                return SHA512
-            elif hash_name == "SHA-512/224":
-                return SHA512.new(truncate="224")
-            elif hash_name == "SHA-512/256":
-                return SHA512.new(truncate="256")
-            elif hash_name == "SHA3-512":
-                return SHA3_512
-            elif hash_name == "SHA-384":
-                return SHA384
-            elif hash_name == "SHA3-384":
-                return SHA3_384
-            elif hash_name == "SHA-256":
-                return SHA256
-            elif hash_name == "SHA3-256":
-                return SHA3_256
-            elif hash_name == "SHA-224":
-                return SHA224
-            elif hash_name == "SHA3-224":
-                return SHA3_224
-            elif hash_name == "SHA-1":
-                return SHA1
-            else:
-                raise ValueError("Unknown hash algorithm: " + hash_name)
-
-        def filter_type(group):
-            type_name = group['type']
-            if type_name not in ("RsassaPkcs1Verify", "RsassaPkcs1Generate"):
-                raise ValueError("Unknown type name " + type_name)
-
-        result = load_test_vectors_wycheproof(("Signature", "wycheproof"),
-                                              filename,
-                                              "Wycheproof PKCS#1v1.5 signature (%s)" % filename,
-                                              group_tag={'rsa_key': filter_rsa,
-                                                         'hash_mod': filter_sha,
-                                                         'type': filter_type})
-        return result
-
-    def shortDescription(self):
-        return self._id
-
-    def warn(self, tv):
-        if tv.warning and self._wycheproof_warnings:
-            import warnings
-            warnings.warn("Wycheproof warning: %s (%s)" % (self._id, tv.comment))
-
-    def test_verify(self, tv):
-        self._id = "Wycheproof RSA PKCS$#1 Test #" + str(tv.id)
-
-        hashed_msg = tv.hash_module.new(tv.msg)
-        signer = pkcs1_15.new(tv.key)
-        try:
-            signature = signer.verify(hashed_msg, tv.sig)
-        except ValueError as e:
-            if tv.warning:
-                return
-            assert not tv.valid
-        else:
-            assert tv.valid
-            self.warn(tv)
-
-    def runTest(self):
-        for tv in self.tv:
-            self.test_verify(tv)
-
-
-def get_tests(config={}):
-    wycheproof_warnings = config.get('wycheproof_warnings')
-
-    tests = []
-    tests += list_test_cases(FIPS_PKCS1_Verify_Tests)
-    tests += list_test_cases(FIPS_PKCS1_Sign_Tests)
-    tests += list_test_cases(PKCS1_15_NoParams)
-    tests += list_test_cases(PKCS1_Legacy_Module_Tests)
-    tests += list_test_cases(PKCS1_All_Hashes_Tests)
-    tests += [ TestVectorsWycheproof(wycheproof_warnings) ]
-
-    if config.get('slow_tests'):
-        tests += list_test_cases(FIPS_PKCS1_Verify_Tests_KAT)
-        tests += list_test_cases(FIPS_PKCS1_Sign_Tests_KAT)
-
-    return tests
-
-if __name__ == '__main__':
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
diff --git a/spaces/asafAdge/Detic/tools/fix_o365_path.py b/spaces/asafAdge/Detic/tools/fix_o365_path.py
deleted file mode 100644
index 38716e56c465fc1a2b904a39dd3b9660eafba398..0000000000000000000000000000000000000000
--- a/spaces/asafAdge/Detic/tools/fix_o365_path.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-import path
-import os
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ann", default='datasets/objects365/annotations/zhiyuan_objv2_train_fixname.json')
-    parser.add_argument("--img_dir", default='datasets/objects365/train/')
-    args = parser.parse_args()
-
-    print('Loading', args.ann)
-    data = json.load(open(args.ann, 'r'))
-    images = []
-    count = 0
-    for x in data['images']:
-        path = '{}/{}'.format(args.img_dir, x['file_name'])
-        if os.path.exists(path):
-            images.append(x)
-        else:
-            print(path)
-            count = count + 1
-    print('Missing', count, 'images')
-    data['images'] = images
-    out_name = args.ann[:-5] + '_fixmiss.json'
-    print('Saving to', out_name)
-    json.dump(data, open(out_name, 'w'))
diff --git a/spaces/ashrestha/auto-multi-class/app.py b/spaces/ashrestha/auto-multi-class/app.py
deleted file mode 100644
index 8d717e6d32b302e0c2758152ab157bcdf7c5383a..0000000000000000000000000000000000000000
--- a/spaces/ashrestha/auto-multi-class/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import streamlit as st
-import json
-
-from transformers import pipeline
-
-
-classifier = pipeline("zero-shot-classification",
-                      model="valhalla/distilbart-mnli-12-1")
-
-with st.form('inputs'):
-    input_text = st.text_area("Input text")
-    input_label = st.text_input("Labels", placeholder="support, help, important")
-    input_multi = st.checkbox('Allow multiple true classes', value=False)
-    submit_button = st.form_submit_button(label='Submit')
-
-if submit_button:
-
-    labels = list(l.strip() for l in input_label.split(','))
-    pred = classifier(input_text, labels, multi_class=input_multi)
-    
-    if input_multi:
-        st.vega_lite_chart(pred, {'mark': {'type': 'bar', 'tooltip': False},
-     'encoding': {
-         'x': {'field': 'scores', 'type': 'quantitative'},
-         'y': {'field': 'labels', 'type': 'nominal'},
-           },
-         }, use_container_width=True, height=250)
-    else:
-        out = f"Top predicted label is {pred['labels'][0]}"
-    
-        st.success(out)
-    
-    # st.markdown(pred)
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Vijay Ghanapaati.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Vijay Ghanapaati.html
deleted file mode 100644
index d65163bfe4ae770c2b12c28ed44421e5e455473c..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Vijay Ghanapaati.html	
+++ /dev/null
@@ -1,132 +0,0 @@
-<!DOCTYPE html><html lang="en"><head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no">
-    <title>Vijay Ghanapaati</title>
-    <style>
-      html {
-        padding: 1em;
-        background: #fff;
-        color: #222;
-        font-family: "Lucida Grande", "Lucida Sans Unicode", "Lucida Sans", Geneva, Verdana, sans-serif;
-        font-size: 100%;
-        line-height: 1.5;
-      }
-
-      h1,
-      p,
-      ul,
-      ol,
-      div,
-      figure {
-        margin: 0;
-      }
-
-      h1 { font-size: 1.2em; }
-
-      ul {
-        list-style: disc;
-        padding-left: 1.3em;
-      }
-
-      ol {
-        padding-left: 1.3em;
-        list-style: decimal;
-      }
-
-      blockquote {
-        border-left: 3px solid #000;
-        margin: 0;
-        padding: 0 0 0 1em;
-      }
-
-      pre {
-        font-family: monaco, monospace;
-        font-size: 0.875em;
-        margin: 0;
-        padding: 1rem;
-        background: #eee;
-        border-radius: 0.4rem;
-        white-space: pre;
-        word-wrap: normal;
-        word-break: normal;
-        overflow-x: auto;
-      }
-
-      code { border-radius: 0.4rem; }
-
-      figure {
-        display: inline-block;
-        width: 100%;
-        box-sizing: border-box;
-      }
-
-      figcaption { word-break: break-word; }
-
-      img {
-        display: block;
-        max-width: 100%;
-        margin: 0 auto;
-        padding: 1px;
-        border: 1px solid #eee;
-      }
-
-      a {
-        color: #1b6ac9;
-        text-decoration: underline;
-      }
-
-      a:active { color: #064ac9; }
-
-      .wrapper {
-        max-width: 650px;
-        margin: 0 auto;
-      }
-
-      .title {
-        font-size: 2em;
-        margin: 0.5em 0 1em;
-        line-height: 1.3;
-      }
-
-      .attachment--image {
-        text-align: center;
-        color: #888;
-        font-size: 0.875em;
-      }
-
-      .attachment--file {
-        position: relative;
-        padding: 1.4em 1em 1.5em;
-        border: 1px solid #ddd;
-        border-bottom-width: 3px;
-        border-radius: 6px;
-        color: #222;
-      }
-
-      .attachment__label {
-        position: absolute;
-        bottom: 0;
-        right: 0;
-        background: #ddd;
-        font-size: 10px;
-        padding: 2px 6px;
-        border-radius: 6px 0 4px 0;
-        color: #888;
-      }
-
-      .metadata { color: #888; }
-    </style>
-  </head>
-
-  <body>
-    <div class="wrapper">
-      <h1 class="title">Vijay Ghanapaati</h1>
-
-<div class="formatted_content">
-  <div>Mentee to mentor<br><br>1- What is your motivation to become a mentor with SharpestMinds?<br>- Made a move to data field from a different industry -&nbsp; want to provide guidance and support to people who want to make their first career break into to data analytics-related roles.&nbsp;<br><br>2- Do you have previous mentorship experience?<br>- No formal mentorship experience. Have previous project manager and product manager experience and have worked and lead teams. People's person and have helped team members with their goals and aspirations.&nbsp;<br><br>3- What's your career journey been like and what motivated you to get into Data field?<br>- Moved to Canada over 5 years ago. Did a PGDM in project management and analytics.<br>- Had a technical role in DA.<br>- Did a certification from Uni of Toronto.&nbsp;<br>- Interested in knowing and learning where the data field and going and how it can be leveraged.&nbsp;<br>- Found SM and did mentorship through the platform.&nbsp;<br>- Landed a role as product manager for reporting and analytics. Work involved building and designing dashboards and generating insights on what customers are looking.&nbsp;<br><br>4- How was your experience as a SM Mentee?<br>- Had a good experience, Believe it's a good community platform. Everybody is ready to give time and the fee structure in form of ISA was good and helpful. More interactions with the mentor can be helpful but it's understandable that the mentors are busy too.&nbsp;<br><br>5- What according to you is the biggest challenge a newcomer faces when trying to land a data-science related role? How can you help them with this?<br>- The biggest challenge is figuring out whom to communicate with and the right people to network with. Can help mentees with tips and tricks and plan to achieve the job they are looking for. How to communicate with the hiring manager and show skills they bring to the table.&nbsp;<br><br>6- Do you have any questions regarding SM?<br>- How does the platform work?<br>- How is the platform progressing?</div>
-</div>
-
-    </div>
-  
-
-</body></html>
\ No newline at end of file
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/normal_grounding_net.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/normal_grounding_net.py
deleted file mode 100644
index 38cadb7c9321f2d4aacabf3a4e31ac2207bebb32..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/normal_grounding_net.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-from ldm.modules.attention import BasicTransformerBlock
-from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
-import torch.nn.functional as F
-from ..attention import SelfAttention, FeedForward
-from .convnext import convnext_tiny
-
-
-
-
-class PositionNet(nn.Module):
-    def __init__(self, resize_input=448, out_dim=768):
-        super().__init__()
-        self.resize_input = resize_input
-        self.down_factor = 32 # determined by the convnext backbone 
-        self.out_dim = out_dim
-        assert self.resize_input % self.down_factor == 0
-        
-        self.convnext_tiny_backbone = convnext_tiny(pretrained=True)
-        
-        self.num_tokens = (self.resize_input // self.down_factor) ** 2
-        
-        convnext_feature_dim = 768
-        self.pos_embedding = nn.Parameter(torch.empty(1, self.num_tokens, convnext_feature_dim).normal_(std=0.02))  # from BERT
-      
-        self.linears = nn.Sequential(
-            nn.Linear( convnext_feature_dim, 512),
-            nn.SiLU(),
-            nn.Linear( 512, 512),
-            nn.SiLU(),
-            nn.Linear(512, out_dim),
-        )
-
-        self.null_feature = torch.nn.Parameter(torch.zeros([convnext_feature_dim]))
-
-
-    def forward(self, normal, mask):
-        B = normal.shape[0] 
-
-        # token from edge map 
-        normal = torch.nn.functional.interpolate(normal, self.resize_input)
-        normal_feature = self.convnext_tiny_backbone(normal)
-        objs = normal_feature.reshape(B, -1, self.num_tokens)
-        objs = objs.permute(0, 2, 1) # N*Num_tokens*dim
-
-        # expand null token
-        null_objs = self.null_feature.view(1,1,-1)
-        null_objs = null_objs.repeat(B,self.num_tokens,1)
-        
-        # mask replacing 
-        mask = mask.view(-1,1,1)
-        objs = objs*mask + null_objs*(1-mask)
-        
-        # add pos 
-        objs = objs + self.pos_embedding
-
-        # fuse them 
-        objs = self.linears(objs)
-
-        assert objs.shape == torch.Size([B,self.num_tokens,self.out_dim])        
-        return objs
-
-
-
diff --git a/spaces/awacke1/SuperSimple2LinerText2Speech/README.md b/spaces/awacke1/SuperSimple2LinerText2Speech/README.md
deleted file mode 100644
index 5fd4dac46848a5bf9a5324fd8271ef761c62eee1..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SuperSimple2LinerText2Speech/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SuperSimple2LinerText2Speech
-emoji: 🐠
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ayushnoori/program-synthesis/examples.py b/spaces/ayushnoori/program-synthesis/examples.py
deleted file mode 100644
index b5ee33a0dbeaff40e1690a18f9d4ba12ee2d9fe9..0000000000000000000000000000000000000000
--- a/spaces/ayushnoori/program-synthesis/examples.py
+++ /dev/null
@@ -1,65 +0,0 @@
-'''
-EXAMPLES
-This file contains input-output examples for both arithmetic and string domain-specific languages (DSLs).
-To add a new example, add a new key to the dictionary 'example_set' and set the value to be a list of tuples.
-
-Note that we synthesize programs with a consistent arity. Therefore, in each set of input-output examples, all 
-input examples must be of the same length. Further, argument types must remain consistent across examples. We 
-test for these conditions in the `check_examples` function below, which is called by the `extract_constants` 
-function in the synthesizer.
-'''
-
-# define examples
-example_set = {
-    # basic arithmetic examples
-    'addition': [([7, 2], 9), ([8, 1], 9), ([4, 6], 10), ([3, 9], 12), ([5, 8], 13)],
-    'subtraction': [([9, 2], 7), ([6, 1], 5), ([7, 3], 4), ([8, 4], 4), ([10, 2], 8)],
-    'multiplication': [([2, 3], 6), ([4, 5], 20), ([7, 8], 56), ([9, 2], 18), ([3, 4], 12)],
-    'division': [([6, 2], 3), ([8, 4], 2), ([9, 3], 3), ([10, 5], 2), ([12, 6], 2)],
-
-    # basic string examples
-    'concatenate': [(["a", "b"], "ab"), (["c", "d"], "cd"), (["e", "f"], "ef")],
-    'right': [(["hello", 3], "llo"), (["world", 4], "orld"), (["fox", 1], "x")],
-    'left': [(["hello", 2], "he"), (["world", 3], "wor"), (["fox", 2], "fo")],
-
-    # advanced arithmetic examples
-    'add_5_multiply_2': [([1, 2], 12), ([3, 4], 22), ([5, 6], 32), ([7, 8], 42), ([9, 10], 52)],
-    'multiply_add_9': [([1, 2], 11), ([3, 4], 21), ([5, 6], 39), ([7, 8], 65), ([9, 10], 9)],
-
-    # advanced string examples
-    'concatenate_3': [(["a", "b", "c"], "abc"), (["d", "e", "f"], "def"), (["g", "h", "i"], "ghi")],
-    
-    # custom user examples
-}
-
-
-# CHECK EXAMPLE VALIDITY
-def check_examples(examples):
-    '''
-    Checks that all input examples are of same length and that argument types are consistent across examples.
-    If valid, returns arity and argument types of function to be generated.
-
-    Args:
-        examples (list): list of tuples, where each tuple is of the form (input, output)
-    
-    Returns:
-        input_lengths[0] (int): arity of function
-        arg_types[0] (list): argument types of function
-    '''
-
-    # get input examples
-    inputs = [example[0] for example in examples]
-
-    # check all inputs are of same length
-    input_lengths = [len(input) for input in inputs]
-    if len(set(input_lengths)) != 1:
-        raise ValueError("All input examples must be of same length.")
-
-    # check that types of arguments are same
-    arg_types = [[type(arg) for arg in input] for input in inputs]
-    consistent_types = all([arg_types[0] == arg_type for arg_type in arg_types])
-    if not consistent_types:
-        raise ValueError("Argument types must be consistent across inputs.")
-    
-    # return arity and argument types
-    return input_lengths[0], arg_types[0]
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/materials/MeshLambertMaterial.js b/spaces/banana-projects/web3d/node_modules/three/src/materials/MeshLambertMaterial.js
deleted file mode 100644
index 949310e3cd50abe78975547c85bd3be5d5970a64..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/materials/MeshLambertMaterial.js
+++ /dev/null
@@ -1,131 +0,0 @@
-import { Material } from './Material.js';
-import { MultiplyOperation } from '../constants.js';
-import { Color } from '../math/Color.js';
-
-/**
- * @author mrdoob / http://mrdoob.com/
- * @author alteredq / http://alteredqualia.com/
- *
- * parameters = {
- *  color: <hex>,
- *  opacity: <float>,
- *
- *  map: new THREE.Texture( <Image> ),
- *
- *  lightMap: new THREE.Texture( <Image> ),
- *  lightMapIntensity: <float>
- *
- *  aoMap: new THREE.Texture( <Image> ),
- *  aoMapIntensity: <float>
- *
- *  emissive: <hex>,
- *  emissiveIntensity: <float>
- *  emissiveMap: new THREE.Texture( <Image> ),
- *
- *  specularMap: new THREE.Texture( <Image> ),
- *
- *  alphaMap: new THREE.Texture( <Image> ),
- *
- *  envMap: new THREE.CubeTexture( [posx, negx, posy, negy, posz, negz] ),
- *  combine: THREE.Multiply,
- *  reflectivity: <float>,
- *  refractionRatio: <float>,
- *
- *  wireframe: <boolean>,
- *  wireframeLinewidth: <float>,
- *
- *  skinning: <bool>,
- *  morphTargets: <bool>,
- *  morphNormals: <bool>
- * }
- */
-
-function MeshLambertMaterial( parameters ) {
-
-	Material.call( this );
-
-	this.type = 'MeshLambertMaterial';
-
-	this.color = new Color( 0xffffff ); // diffuse
-
-	this.map = null;
-
-	this.lightMap = null;
-	this.lightMapIntensity = 1.0;
-
-	this.aoMap = null;
-	this.aoMapIntensity = 1.0;
-
-	this.emissive = new Color( 0x000000 );
-	this.emissiveIntensity = 1.0;
-	this.emissiveMap = null;
-
-	this.specularMap = null;
-
-	this.alphaMap = null;
-
-	this.envMap = null;
-	this.combine = MultiplyOperation;
-	this.reflectivity = 1;
-	this.refractionRatio = 0.98;
-
-	this.wireframe = false;
-	this.wireframeLinewidth = 1;
-	this.wireframeLinecap = 'round';
-	this.wireframeLinejoin = 'round';
-
-	this.skinning = false;
-	this.morphTargets = false;
-	this.morphNormals = false;
-
-	this.setValues( parameters );
-
-}
-
-MeshLambertMaterial.prototype = Object.create( Material.prototype );
-MeshLambertMaterial.prototype.constructor = MeshLambertMaterial;
-
-MeshLambertMaterial.prototype.isMeshLambertMaterial = true;
-
-MeshLambertMaterial.prototype.copy = function ( source ) {
-
-	Material.prototype.copy.call( this, source );
-
-	this.color.copy( source.color );
-
-	this.map = source.map;
-
-	this.lightMap = source.lightMap;
-	this.lightMapIntensity = source.lightMapIntensity;
-
-	this.aoMap = source.aoMap;
-	this.aoMapIntensity = source.aoMapIntensity;
-
-	this.emissive.copy( source.emissive );
-	this.emissiveMap = source.emissiveMap;
-	this.emissiveIntensity = source.emissiveIntensity;
-
-	this.specularMap = source.specularMap;
-
-	this.alphaMap = source.alphaMap;
-
-	this.envMap = source.envMap;
-	this.combine = source.combine;
-	this.reflectivity = source.reflectivity;
-	this.refractionRatio = source.refractionRatio;
-
-	this.wireframe = source.wireframe;
-	this.wireframeLinewidth = source.wireframeLinewidth;
-	this.wireframeLinecap = source.wireframeLinecap;
-	this.wireframeLinejoin = source.wireframeLinejoin;
-
-	this.skinning = source.skinning;
-	this.morphTargets = source.morphTargets;
-	this.morphNormals = source.morphNormals;
-
-	return this;
-
-};
-
-
-export { MeshLambertMaterial };
diff --git a/spaces/barani/ControlNet/app.py b/spaces/barani/ControlNet/app.py
deleted file mode 100644
index 39db16638982f7a7ac89cf10c03ee6ee080512f0..0000000000000000000000000000000000000000
--- a/spaces/barani/ControlNet/app.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-
-import gradio as gr
-import torch
-
-from app_canny import create_demo as create_demo_canny
-from app_depth import create_demo as create_demo_depth
-from app_ip2p import create_demo as create_demo_ip2p
-from app_lineart import create_demo as create_demo_lineart
-from app_mlsd import create_demo as create_demo_mlsd
-from app_normal import create_demo as create_demo_normal
-from app_openpose import create_demo as create_demo_openpose
-from app_scribble import create_demo as create_demo_scribble
-from app_scribble_interactive import \
-    create_demo as create_demo_scribble_interactive
-from app_segmentation import create_demo as create_demo_segmentation
-from app_shuffle import create_demo as create_demo_shuffle
-from app_softedge import create_demo as create_demo_softedge
-from model import Model
-
-DESCRIPTION = '# ControlNet v1.1'
-
-SPACE_ID = os.getenv('SPACE_ID')
-ALLOW_CHANGING_BASE_MODEL = SPACE_ID != 'hysts/ControlNet-v1-1'
-
-if SPACE_ID is not None:
-    DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
-
-if not torch.cuda.is_available():
-    DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'
-
-MAX_NUM_IMAGES = int(os.getenv('MAX_NUM_IMAGES', '3'))
-DEFAULT_NUM_IMAGES = min(MAX_NUM_IMAGES,
-                         int(os.getenv('DEFAULT_NUM_IMAGES', '1')))
-
-DEFAULT_MODEL_ID = os.getenv('DEFAULT_MODEL_ID',
-                             'runwayml/stable-diffusion-v1-5')
-model = Model(base_model_id=DEFAULT_MODEL_ID, task_name='Canny')
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Tabs():
-        with gr.TabItem('Canny'):
-            create_demo_canny(model.process_canny,
-                              max_images=MAX_NUM_IMAGES,
-                              default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('MLSD'):
-            create_demo_mlsd(model.process_mlsd,
-                             max_images=MAX_NUM_IMAGES,
-                             default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Scribble'):
-            create_demo_scribble(model.process_scribble,
-                                 max_images=MAX_NUM_IMAGES,
-                                 default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Scribble Interactive'):
-            create_demo_scribble_interactive(
-                model.process_scribble_interactive,
-                max_images=MAX_NUM_IMAGES,
-                default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('SoftEdge'):
-            create_demo_softedge(model.process_softedge,
-                                 max_images=MAX_NUM_IMAGES,
-                                 default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('OpenPose'):
-            create_demo_openpose(model.process_openpose,
-                                 max_images=MAX_NUM_IMAGES,
-                                 default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Segmentation'):
-            create_demo_segmentation(model.process_segmentation,
-                                     max_images=MAX_NUM_IMAGES,
-                                     default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Depth'):
-            create_demo_depth(model.process_depth,
-                              max_images=MAX_NUM_IMAGES,
-                              default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Normal map'):
-            create_demo_normal(model.process_normal,
-                               max_images=MAX_NUM_IMAGES,
-                               default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Lineart'):
-            create_demo_lineart(model.process_lineart,
-                                max_images=MAX_NUM_IMAGES,
-                                default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Content Shuffle'):
-            create_demo_shuffle(model.process_shuffle,
-                                max_images=MAX_NUM_IMAGES,
-                                default_num_images=DEFAULT_NUM_IMAGES)
-        with gr.TabItem('Instruct Pix2Pix'):
-            create_demo_ip2p(model.process_ip2p,
-                             max_images=MAX_NUM_IMAGES,
-                             default_num_images=DEFAULT_NUM_IMAGES)
-
-    with gr.Accordion(label='Base model', open=False):
-        with gr.Row():
-            with gr.Column():
-                current_base_model = gr.Text(label='Current base model')
-            with gr.Column(scale=0.3):
-                check_base_model_button = gr.Button('Check current base model')
-        with gr.Row():
-            with gr.Column():
-                new_base_model_id = gr.Text(
-                    label='New base model',
-                    max_lines=1,
-                    placeholder='runwayml/stable-diffusion-v1-5',
-                    info=
-                    'The base model must be compatible with Stable Diffusion v1.5.',
-                    interactive=ALLOW_CHANGING_BASE_MODEL)
-            with gr.Column(scale=0.3):
-                change_base_model_button = gr.Button(
-                    'Change base model', interactive=ALLOW_CHANGING_BASE_MODEL)
-        if not ALLOW_CHANGING_BASE_MODEL:
-            gr.Markdown(
-                '''The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a>'''
-            )
-
-    check_base_model_button.click(fn=lambda: model.base_model_id,
-                                  outputs=current_base_model,
-                                  queue=False)
-    new_base_model_id.submit(fn=model.set_base_model,
-                             inputs=new_base_model_id,
-                             outputs=current_base_model)
-    change_base_model_button.click(fn=model.set_base_model,
-                                   inputs=new_base_model_id,
-                                   outputs=current_base_model)
-
-demo.queue(api_open=False, max_size=10).launch()
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/deep/models/mlfn.py b/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/deep/models/mlfn.py
deleted file mode 100644
index ac7e126b073db6a710fc41e62624127ca91ec131..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/deep/models/mlfn.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from __future__ import division, absolute_import
-import torch
-import torch.utils.model_zoo as model_zoo
-from torch import nn
-from torch.nn import functional as F
-
-__all__ = ['mlfn']
-
-model_urls = {
-    # training epoch = 5, top1 = 51.6
-    'imagenet':
-    'https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk',
-}
-
-
-class MLFNBlock(nn.Module):
-
-    def __init__(
-        self, in_channels, out_channels, stride, fsm_channels, groups=32
-    ):
-        super(MLFNBlock, self).__init__()
-        self.groups = groups
-        mid_channels = out_channels // 2
-
-        # Factor Modules
-        self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
-        self.fm_bn1 = nn.BatchNorm2d(mid_channels)
-        self.fm_conv2 = nn.Conv2d(
-            mid_channels,
-            mid_channels,
-            3,
-            stride=stride,
-            padding=1,
-            bias=False,
-            groups=self.groups
-        )
-        self.fm_bn2 = nn.BatchNorm2d(mid_channels)
-        self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
-        self.fm_bn3 = nn.BatchNorm2d(out_channels)
-
-        # Factor Selection Module
-        self.fsm = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(in_channels, fsm_channels[0], 1),
-            nn.BatchNorm2d(fsm_channels[0]),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
-            nn.BatchNorm2d(fsm_channels[1]),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(fsm_channels[1], self.groups, 1),
-            nn.BatchNorm2d(self.groups),
-            nn.Sigmoid(),
-        )
-
-        self.downsample = None
-        if in_channels != out_channels or stride > 1:
-            self.downsample = nn.Sequential(
-                nn.Conv2d(
-                    in_channels, out_channels, 1, stride=stride, bias=False
-                ),
-                nn.BatchNorm2d(out_channels),
-            )
-
-    def forward(self, x):
-        residual = x
-        s = self.fsm(x)
-
-        # reduce dimension
-        x = self.fm_conv1(x)
-        x = self.fm_bn1(x)
-        x = F.relu(x, inplace=True)
-
-        # group convolution
-        x = self.fm_conv2(x)
-        x = self.fm_bn2(x)
-        x = F.relu(x, inplace=True)
-
-        # factor selection
-        b, c = x.size(0), x.size(1)
-        n = c // self.groups
-        ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
-        ss = ss.view(b, n, self.groups, 1, 1)
-        ss = ss.permute(0, 2, 1, 3, 4).contiguous()
-        ss = ss.view(b, c, 1, 1)
-        x = ss * x
-
-        # recover dimension
-        x = self.fm_conv3(x)
-        x = self.fm_bn3(x)
-        x = F.relu(x, inplace=True)
-
-        if self.downsample is not None:
-            residual = self.downsample(residual)
-
-        return F.relu(residual + x, inplace=True), s
-
-
-class MLFN(nn.Module):
-    """Multi-Level Factorisation Net.
-
-    Reference:
-        Chang et al. Multi-Level Factorisation Net for
-        Person Re-Identification. CVPR 2018.
-
-    Public keys:
-        - ``mlfn``: MLFN (Multi-Level Factorisation Net).
-    """
-
-    def __init__(
-        self,
-        num_classes,
-        loss='softmax',
-        groups=32,
-        channels=[64, 256, 512, 1024, 2048],
-        embed_dim=1024,
-        **kwargs
-    ):
-        super(MLFN, self).__init__()
-        self.loss = loss
-        self.groups = groups
-
-        # first convolutional layer
-        self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
-        self.bn1 = nn.BatchNorm2d(channels[0])
-        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
-
-        # main body
-        self.feature = nn.ModuleList(
-            [
-                # layer 1-3
-                MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
-                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
-                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
-                # layer 4-7
-                MLFNBlock(
-                    channels[1], channels[2], 2, [256, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[2], channels[2], 1, [256, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[2], channels[2], 1, [256, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[2], channels[2], 1, [256, 128], self.groups
-                ),
-                # layer 8-13
-                MLFNBlock(
-                    channels[2], channels[3], 2, [512, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[3], channels[3], 1, [512, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[3], channels[3], 1, [512, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[3], channels[3], 1, [512, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[3], channels[3], 1, [512, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[3], channels[3], 1, [512, 128], self.groups
-                ),
-                # layer 14-16
-                MLFNBlock(
-                    channels[3], channels[4], 2, [512, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[4], channels[4], 1, [512, 128], self.groups
-                ),
-                MLFNBlock(
-                    channels[4], channels[4], 1, [512, 128], self.groups
-                ),
-            ]
-        )
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-
-        # projection functions
-        self.fc_x = nn.Sequential(
-            nn.Conv2d(channels[4], embed_dim, 1, bias=False),
-            nn.BatchNorm2d(embed_dim),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_s = nn.Sequential(
-            nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
-            nn.BatchNorm2d(embed_dim),
-            nn.ReLU(inplace=True),
-        )
-
-        self.classifier = nn.Linear(embed_dim, num_classes)
-
-        self.init_params()
-
-    def init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(
-                    m.weight, mode='fan_out', nonlinearity='relu'
-                )
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = F.relu(x, inplace=True)
-        x = self.maxpool(x)
-
-        s_hat = []
-        for block in self.feature:
-            x, s = block(x)
-            s_hat.append(s)
-        s_hat = torch.cat(s_hat, 1)
-
-        x = self.global_avgpool(x)
-        x = self.fc_x(x)
-        s_hat = self.fc_s(s_hat)
-
-        v = (x+s_hat) * 0.5
-        v = v.view(v.size(0), -1)
-
-        if not self.training:
-            return v
-
-        y = self.classifier(v)
-
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, v
-        else:
-            raise KeyError('Unsupported loss: {}'.format(self.loss))
-
-
-def init_pretrained_weights(model, model_url):
-    """Initializes model with pretrained weights.
-    
-    Layers that don't match with pretrained layers in name or size are kept unchanged.
-    """
-    pretrain_dict = model_zoo.load_url(model_url)
-    model_dict = model.state_dict()
-    pretrain_dict = {
-        k: v
-        for k, v in pretrain_dict.items()
-        if k in model_dict and model_dict[k].size() == v.size()
-    }
-    model_dict.update(pretrain_dict)
-    model.load_state_dict(model_dict)
-
-
-def mlfn(num_classes, loss='softmax', pretrained=True, **kwargs):
-    model = MLFN(num_classes, loss, **kwargs)
-    if pretrained:
-        # init_pretrained_weights(model, model_urls['imagenet'])
-        import warnings
-        warnings.warn(
-            'The imagenet pretrained weights need to be manually downloaded from {}'
-            .format(model_urls['imagenet'])
-        )
-    return model
diff --git a/spaces/bioriAsaeru/text-to-voice/Crack FREE V1.1.0.1 Max Payne 3.md b/spaces/bioriAsaeru/text-to-voice/Crack FREE V1.1.0.1 Max Payne 3.md
deleted file mode 100644
index 89355326fefcc89d44b43ecd52bdf60f49da4879..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Crack FREE V1.1.0.1 Max Payne 3.md	
+++ /dev/null
@@ -1,73 +0,0 @@
-
-<h1>What You Need to Know About Crack v1.1.0.1 Max Payne 3</h1>
-<p>Crack v1.1.0.1 Max Payne 3 is a file that can help you play Max Payne 3, a popular third-person shooter game, without paying for a license key or serial number. It is a crack or a patch that can bypass the game's protection and allow you to enjoy the game for free. It is compatible with both 32-bit and 64-bit versions of Windows.</p>
-<h2>How to Use Crack v1.1.0.1 Max Payne 3</h2>
-<p>Using Crack v1.1.0.1 Max Payne 3 is not very difficult, but you need to follow some steps carefully. Here are the steps you need to follow:</p>
-<h2>Crack v1.1.0.1 max payne 3</h2><br /><p><b><b>Download File</b> &bull; <a href="https://urloso.com/2uyR5O">https://urloso.com/2uyR5O</a></b></p><br /><br />
-<ol>
-<li>Download Crack v1.1.0.1 Max Payne 3 from a reliable source. You can find it on various websites that offer free downloads of games and files. Some examples are gamecopyworld.com, reddit.com, and sourceforge.net. Make sure you scan the file for viruses before opening it.</li>
-<li>Install Max Payne 3 on your computer. You can download it from the official website of Rockstar Games or from other sources. You can choose the trial version or the full version, depending on your preference.</li>
-<li>Run Crack v1.1.0.1 Max Payne 3 as an administrator. To do this, right-click on the file and select "Run as administrator". This will open the crack window.</li>
-<li>Copy the crack files into the game folder. The crack files are usually named as "MaxPayne3.exe", "steam_api.dll", and "steam_api64.dll". The game folder is usually located in "C:\Program Files (x86)\Rockstar Games\Max Payne 3". You might need to replace the existing files with the crack files.</li>
-<li>Launch the game from the crack file. To do this, double-click on "MaxPayne3.exe" in the game folder. This will start the game without asking for a license key or serial number.</li>
-</ol>
-<h2>Benefits of Using Crack v1.1.0.1 Max Payne 3</h2>
-<p>Using Crack v1.1.0.1 Max Payne 3 has some benefits that you might want to consider. Here are some of them:</p>
-<ul>
-<li>You can play Max Payne 3 for free without paying for a license key or serial number.</li>
-<li>You can access all the features and functions of Max Payne 3 without any limitations or restrictions.</li>
-<li>You can enjoy the story and gameplay of Max Payne 3, which is praised for its cinematic presentation, realistic physics, and dynamic combat.</li>
-<li>You can save money and time by using Crack v1.1.0.1 Max Payne 3 instead of buying or searching for a valid license key or serial number.</li>
-</ul>
-<h2>Risks of Using Crack v1.1.0.1 Max Payne 3</h2>
-<p>However, using Crack v1.1.0.1 Max Payne 3 also has some risks that you should be aware of. Here are some of them:</p>
-<ul>
-<li>You might violate the terms and conditions of Rockstar Games by using Crack v1.1.0.1 Max Payne 3. Rockstar Games does not support or endorse the use of cracks or patches for its games. You might face legal consequences if you are caught using Crack v1.1.0.1 Max Payne 3.</li>
-<li>You might download a fake or malicious file instead of Crack v1.1.0.1 Max Payne 3. Some websites might offer fake or infected files that can harm your computer or steal your personal information. You should always check the source and reputation of the website before downloading anything.</li>
-<li>You might encounter errors or problems with Max Payne 3 after using Crack v1.1.0.1 Max Payne 3. The crack might not work properly or cause compatibility issues with your system. You might also experience crashes, glitches, or bugs with Max Payne 3.</li>
-<li>You might miss out on updates and support from Rockstar Games by using Crack v1.1.0.1 Max Payne 3 . Rockstar Games regularly releases updates and patches for its games to fix bugs, improve performance, and add new features . You might not be able to access these updates if you use Crack v1 . 11 .01 Max Payne 3 . You might also not be able to get technical support or customer service from Rockstar Games if you have any issues with Max Payne 3 .</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Crack v1 .11 .01 Max Payne 3 is a file that can help you play Max Payne 3 , a popular third-person shooter game , without paying for a license key or serial number . It is a crack or a patch that can bypass the game's protection and allow you to enjoy the game for free . It is compatible with both 32-bit and 64-bit versions of Windows .</p>
-<p>However , using Crack v11 .01 Max Payne 3 also has some risks that you should consider before using it . You might violate the terms and conditions of Rockstar Games , download a fake or malicious file , encounter errors or problems with Max Payne 3 , or miss out on updates and support from Rockstar Games by using Crack v11 .01 Max Payne 3 .</p>
-<p>Therefore , you should weigh the pros and cons of using Crack v11 .01 Max Payne 3 carefully before deciding whether to use it or not . You should also be aware of the legal and ethical implications of using Crack v11 .01 Max Payne 3 .</p>
-<p>We hope this article has helped you understand what Crack v11 .01 Max Payne 3 is and how to use it . If you have any questions or feedback , please feel free to leave a comment below .</p>
-<h2>Alternatives to Using Crack v1.1.0.1 Max Payne 3</h2>
-<p>If you are looking for alternatives to using Crack v1.1.0.1 Max Payne 3, you have some options that might be more legal and safe than using Crack v1.1.0.1 Max Payne 3. Here are some of them:</p>
-<ul>
-<li>Buy a license key or serial number from Rockstar Games. This is the most legal and safe way to play Max Payne 3. You can buy a license key or serial number from the official website of Rockstar Games or from other authorized sellers. You can also get discounts and offers from time to time. By buying a license key or serial number, you can support the developers and publishers of Max Payne 3 and enjoy the game without any risks.</li>
-<li>Use a free trial version of Max Payne 3. This is another legal and safe way to play Max Payne 3 for a limited time. You can download a free trial version of Max Payne 3 from the official website of Rockstar Games or from other sources. You can play the game for a certain period of time, usually 30 days, without paying anything. However, you might not be able to access all the features and functions of Max Payne 3 in the free trial version.</li>
-<li>Use an alternative software to Max Payne 3. This is another way to play a similar game to Max Payne 3 without using Crack v1.1.0.1 Max Payne 3. You can find many alternative software to Max Payne 3 on the internet, such as other third-person shooter games, action-adventure games, or crime-themed games. Some examples are Grand Theft Auto V, Hitman: Absolution, Sleeping Dogs, and Mafia II. However, you might not get the same story and gameplay as Max Payne 3 in these alternative software.</li>
-</ul>
-<h2>Tips and Tricks for Using Crack v1.1.0.1 Max Payne 3</h2>
-<p>If you decide to use Crack v1.1.0.1 Max Payne 3, you might want to know some tips and tricks that can help you use it more effectively and safely. Here are some of them:</p>
-<p></p>
-<ul>
-<li>Backup your game files before using Crack v1.1.0.1 Max Payne 3 . This can help you restore your game files in case something goes wrong with Crack v1 .11 .01 Max Payne 3 or if you want to uninstall it . You can backup your game files by copying them to another folder or drive .</li>
-<li>Disable your antivirus software before using Crack v1 .11 .01 Max Payne 3 . This can help you avoid any conflicts or false alarms with your antivirus software while using Crack v11 .01 Max Payne 3 . Some antivirus software might detect Crack v11 .01 Max Payne 3 as a virus or malware and try to delete it or block it . You can disable your antivirus software by turning it off temporarily or adding Crack v11 .01 Max Payne 3 to its whitelist or exceptions list .</li>
-<li>Run Crack v11 .01 Max Payne 3 in compatibility mode if you have any issues with it . This can help you solve any compatibility issues with your system while using Crack v11 .01 Max Payne 3 . Some systems might not be compatible with Crack v11 .01 Max Payne 3 and cause errors or problems with it . You can run Crack v11 .01 Max Payne 3 in compatibility mode by right-clicking on it , selecting "Properties" , clicking on the "Compatibility" tab , and choosing a compatible mode , such as Windows XP or Windows Vista .</li>
-<li>Update your drivers and DirectX before using Crack v11 .01 Max Payne 3 . This can help you improve your performance and stability while using Crack v11 .01 Max Payne 3 . Some drivers and DirectX might be outdated or incompatible with Crack v11 .01 Max Payne 3 and cause lag , stutter , or crash with it . You can update your drivers and DirectX by downloading them from the official websites of your hardware manufacturers or Microsoft , respectively .</li>
-</ul>
-<h2>Reviews and Ratings of Crack v1.1.0.1 Max Payne 3</h2>
-<p>If you are curious about what other users think of Crack v1.1.0.1 Max Payne 3, you can check out some reviews and ratings of Crack v1.1.0.1 Max Payne 3 on the internet. You can find many reviews and ratings of Crack v1.1.0.1 Max Payne 3 on various websites that offer free downloads of games and files, such as gamecopyworld.com, reddit.com, and megagames.com. Here are some examples of reviews and ratings of Crack v1.1.0.1 Max Payne 3:</p>
-<ul>
-<li>"This crack works perfectly for me. I can play Max Payne 3 without any problems or errors. The game is amazing and the crack is easy to use. Thank you for this crack!" - User from gamecopyworld.com</li>
-<li>"I love this game and this crack. The game is one of the best third-person shooters I have ever played. The story, the graphics, the gameplay, everything is awesome. The crack is also very good and reliable. It does not cause any crashes or glitches with the game. I recommend this crack to anyone who wants to play Max Payne 3 for free." - User from reddit.com</li>
-<li>"This crack is a scam and a virus. It does not work at all and it infected my computer with malware. It also deleted my game files and corrupted my system. Do not download this crack or you will regret it." - User from megagames.com</li>
-</ul>
-<h2>FAQs About Crack v1.1.0.1 Max Payne 3</h2>
-<p>You might have some questions about Crack v1.1.0.1 Max Payne 3 that you want to know the answers to before using it or after using it. Here are some frequently asked questions (FAQs) about Crack v1.1.0.1 Max Payne 3 and their answers:</p>
-<ol>
-<li>What is Max Payne 3? <br>Max Payne 3 is a popular third-person shooter game developed by Rockstar Studios and published by Rockstar Games in 2012. It is the third installment in the Max Payne series and follows the story of former New York detective Max Payne, who is now working as a private security guard in Sao Paulo, Brazil.</li>
-<li>What is Crack v1 .11 .01 Max Payne 3 ? <br>Crack v11 .01 Max Payne 3 is a file that can help you play Max Payne 3 without paying for a license key or serial number . It is a crack or a patch that can bypass the game's protection and allow you to enjoy the game for free . It is compatible with both 32-bit and 64-bit versions of Windows .</li>
-<li>Is Crack v11 .01 Max Payne 3 legal ? <br>No , Crack v11 .01 Max Payne 3 is not legal . It violates the terms and conditions of Rockstar Games by using Crack v11 .01 Max Payne 3 . Rockstar Games does not support or endorse the use of cracks or patches for its games . You might face legal consequences if you are caught using Crack v11 .01 Max Payne 3 .</li>
-<li>Is Crack v11 .01 Max Payne 3 safe ? <br>Not necessarily , Crack v11 .01 Max Payne 3 might not be safe . You might download a fake or malicious file instead of Crack v11 .01 Max Payne 3 . Some websites might offer fake or infected files that can harm your computer or steal your personal information . You should always check the source and reputation of the website before downloading anything . You might also encounter errors or problems with Max Payne 3 after using Crack v11 .01 Max Payne 3 . The crack might not work properly or cause compatibility issues with your system . You might also experience crashes , glitches , or bugs with Max Payne 3 .</li>
-<li>How can I get a license key or serial number for Max Payne 3 ? <br>You can get a license key or serial number for Max Payne 3 by buying it from Rockstar Games or from other authorized sellers . You can buy a license key or serial number from the official website of Rockstar Games or from other sources . You can also get discounts and offers from time to time . By buying a license key or serial number , you can support the developers and publishers of Max Payne 3 and enjoy the game without any risks .</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Crack v1.1.0.1 Max Payne 3 is a file that can help you play Max Payne 3, a popular third-person shooter game, without paying for a license key or serial number. It is a crack or a patch that can bypass the game's protection and allow you to enjoy the game for free. It is compatible with both 32-bit and 64-bit versions of Windows.</p>
-<p>However, using Crack v1.1.0.1 Max Payne 3 also has some risks that you should consider before using it. You might violate the terms and conditions of Rockstar Games, download a fake or malicious file, encounter errors or problems with Max Payne 3, or miss out on updates and support from Rockstar Games by using Crack v1.1.0.1 Max Payne 3.</p>
-<p>Therefore, you should weigh the pros and cons of using Crack v1.1.0.1 Max Payne 3 carefully before deciding whether to use it or not. You should also be aware of the legal and ethical implications of using Crack v1.1.0.1 Max Payne 3.</p>
-<p>We hope this article has helped you understand what Crack v1.1.0.1 Max Payne 3 is and how to use it. If you have any questions or feedback, please feel free to leave a comment below.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Infinite Flight Simulator 19.04.1 Apk Mod Money for android Free Download Enjoy Realistic Flight Physics and Stunning Graphics.md b/spaces/bioriAsaeru/text-to-voice/Infinite Flight Simulator 19.04.1 Apk Mod Money for android Free Download Enjoy Realistic Flight Physics and Stunning Graphics.md
deleted file mode 100644
index f2492e6d5d953181931b516ebbda01721d1f3583..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Infinite Flight Simulator 19.04.1 Apk Mod Money for android Free Download Enjoy Realistic Flight Physics and Stunning Graphics.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-<br />
-<p>Appvn Android is one of the best websites online to download APK apps or files. With Appvn Android, you can download the best best free android games, best free android apps for Android tablet or Android phone available. At this website you can get the APK Data for some of the most popular android games & android apps like Minecraft: Pocket Edition, Appvn, CF Mobile, KingRoot, Lucky Patcher and many more.</p>
-<h2>Infinite Flight Simulator 19.04.1 Apk Mod Money for android Free Download</h2><br /><p><b><b>Download File</b> &#10084; <a href="https://urloso.com/2uyOyx">https://urloso.com/2uyOyx</a></b></p><br /><br />
-<p>In general, Infinite Flight is possibly one of the most valuable names in the aircraft simulation genre. Because it has an impressive 3D graphic style with realistic design and diverse weather effects. Besides, it is free to download on Google Play and App Store. Also, it has a Pro plan that unlocks amazing infinite features. If you are interested, visit the link at the end of the article to download.</p>
-<p>File Name: Infinite Flight Simulator 22.8.1 APK + Mod (Unlimited money) for Android<br>Mod info: Everything is open<br>File size: 573.35 MB<br>To provide better download speed, we recommend <b>dFast - fastest mod downloader</b> to download this file. dFast is the fastest downloader for millions of free mods. You can enjoy 3x faster speed than normal downloads.<br></p>
-<p>The Mod apk version of Infinite Flight game is an improved and more reliable version of the game. All you need to do is download this hacked version from our website. This version is completely safe and free of cost. You can access any tool without buying it with money. There are many other options available in this version.</p>
-<p>The Infinite Flight MOD APK version is 100% safe to download. Without any doubt just download it and enjoy the game. <br /><br /><br />Q. Can you play infinite flight games offline? No, to play infinite flight games you always need an internet connection. 4.8 / 5 ( 51 votes )Recommended for YouZoo Craft Mod Apk</p>
-<p><strong>Infinite Flight Simulator Mod Apk</strong> is a free flight simulator download for use on Windows-based computers that can be downloaded from the Internet. Although it is one of the more basic flight simulators, it still manages to be very enjoyable and addictive. It allows you to simply fly from one airport to another, without having to worry about traffic or even landing for that matter. You can get as far as you want in the world while playing this fantastic flight simulator!</p>
-<p></p>
-<p>take off, land, fly back home, take off again, and much more. This allows you to experience what it is like to fly in an actual airline. In the future, many planes will no longer be in use just as there is no room to store them, meaning that someday, only the wealthy and famous will have an opportunity to fly around in them. If you would like to be one of them, then by using the infinite flight simulator you can.</p>
-<p>It is not just the interiors that you will be able to explore when you use an infinite flight simulator. You will also be able to experience the dynamics of taking off, landing, and climbing into the sky. This will allow you to simulate many different flight conditions, ensuring that you are always ready to hit the ground running when you take to the skies. The best part is that these simulators are completely accurate, bringing the real world into your gaming world.</p>
-<p>Even though the technology is still in its infancy, there are already plenty of great games available for you to play with. If you enjoy playing Infinite Flight Simulator Mod Apk, then the infinite flight simulator is the one for you.</p>
-<p>Although there are many different models of simulators available, many of them do not have the great graphics and realistic flight physics that this application has. That is why I think it is very impressive. In addition, another feature that I enjoy is that it enables me to connect to the internet. This enables me to download my flight logs and save them to a file. This is a very useful feature and gives me a way to share my experiences online.</p>
-<p>Although it may not seem possible, the Infinite Flight Simulator Mod Apkis also able to transport you to different parts of the world. You will never be stuck in a small airport ever again because of weather conditions, so you will always be able to leave the place and come back to it. Whether it is a long flight or just a short flight, you will always be able to find an interesting destination to take you. If you enjoy flying, then the infinite flight simulator is the one for you. Get to fly!</p>
-<p>Infinite Flight Simulator (com.fds.infiniteflight) is a game mod apk on Android, download the latest version of Infinite Flight Simulator Hack Mod (All Plane Unlocked) 2022 for Android. This game mod apk can be played for free and does not require root.</p>
-<p>Infinite Flight Simulator MOD APK (All Plane Unlocked) APK + OBB 2022 can be downloaded and installed on your android device with android version 4.1 or higher. Download this game mod apk using your favorite browser and click install to install the game mod apk. Downloading (com.fds.infiniteflight) APK + DATA of Infinite Flight Simulator (All Plane Unlocked) from ApkSoul.net is easier and faster.</p>
-<p>Overall, Infinite Flight is the best flight simulator game on mobile. It has many outstanding and powerful gameplay features, aircraft systems and designs. Readers can download Infinite Flight APK and its modified version at the link below. If you have any questions about the game, please leave a comment in this article. Thanks and have fun!</p> aaccfb2cb3<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Jabo 39s Direct3d6 1.5.2 Plugin Download Social Cahier Exerci Free.md b/spaces/bioriAsaeru/text-to-voice/Jabo 39s Direct3d6 1.5.2 Plugin Download Social Cahier Exerci Free.md
deleted file mode 100644
index b19e8bb7954f9b4d9bee78f9b2d66f1d4675c636..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Jabo 39s Direct3d6 1.5.2 Plugin Download Social Cahier Exerci Free.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<h2>Jabo 39;s Direct3d6 1.5.2 Plugin Download social cahier exerci</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash; <a href="https://urloso.com/2uyOxR">https://urloso.com/2uyOxR</a></b></p><br /><br />
-
-It's pretty, clean, simple and good for beginners and good practice. The problem is that I couldn't put it on Facebook, people don't want it. Do you know how to do this? Thanks in advance.) See my site;
-
-You have to choose a Facebook Account that is not yours;
-
-Then you have to choose your Facebook Profile Picture;
-
-Then select the button "Use a picture on my Facebook" and paste the URL of the image you have on your computer;
-
-This is the image you want to put;
-
-After you have done that, you will be able to choose your class and your goal;
-
-After that you will see a page with all the exercises;
-
-(Comment: It doesn't work... I have a different issue and can't use it on Facebook... Don't know if I'm doing the right thing... I downloaded it and I tried but I don't have any choice to upload it) Question: What do you think about this site? How did you create it? Do you use it? Or do you have something better? I'm interested in all the social networks and this is the first site that I found. This is the first time I asked this question. I'm only using this site. I like that you can create an account and you can choose what exercises you want to do. But I have a different problem, I can't upload my social comments. I can choose between 6 themes but there is no way to upload comments. (Comment: I tried in my computer and there is the option to upload on Facebook, but it doesn't work. You have to do the exercise and then you will be able to upload it.) Question: Can you tell me how you added the option to upload in Facebook? Question: In your opinion, what is the best social network?
-
-(Comment: I was the first one to come here. I started with Facebook, I tried many platforms, but nothing works as it should. This platform works perfectly. I really like it. The only problem I have is that I haven't put the option to upload comments on Facebook. That's it. I would like to make a group on Facebook, too. I would like to get some ideas to create a group for exercise. (Comment: I tried it and it worked for me. Did you make a tutorial?) Question: This tutorial worked for me. Do you have any tutorials for Facebook? Question: In this page, you 4fefd39f24<br />
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-<br />
-<p></p>
diff --git a/spaces/brcprado/AutoML_MODEL_TRAINING/README.md b/spaces/brcprado/AutoML_MODEL_TRAINING/README.md
deleted file mode 100644
index db0da5671d12b3d38116026ce3062e9f370001e0..0000000000000000000000000000000000000000
--- a/spaces/brcprado/AutoML_MODEL_TRAINING/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AutoML MODEL TRAINING
-emoji: 🚀
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/dev/run_inference_tests.sh b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/dev/run_inference_tests.sh
deleted file mode 100644
index 46556b80a3ee793bdf6a79f5de2ec88cac902189..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/dev/run_inference_tests.sh
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-BIN="python train_net.py"
-OUTPUT="inference_test_output"
-NUM_GPUS=2
-IMS_PER_GPU=2
-IMS_PER_BATCH=$(( NUM_GPUS * IMS_PER_GPU ))
-
-CFG_LIST=( "${@:1}" )
-
-if [ ${#CFG_LIST[@]} -eq 0 ]; then
-  CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml )
-fi
-
-echo "========================================================================"
-echo "Configs to run:"
-echo "${CFG_LIST[@]}"
-echo "========================================================================"
-
-for cfg in "${CFG_LIST[@]}"; do
-    echo "========================================================================"
-    echo "Running $cfg ..."
-    echo "========================================================================"
-    $BIN \
-      --eval-only \
-      --num-gpus $NUM_GPUS \
-      --config-file "$cfg" \
-      OUTPUT_DIR "$OUTPUT" \
-      SOLVER.IMS_PER_BATCH $IMS_PER_BATCH
-    rm -rf $OUTPUT
-done
-
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py
deleted file mode 100644
index 1cf9c3ea7a962bd890fc3b22e0449323f8dc0dfa..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from functools import partial
-import torch.nn as nn
-
-from detectron2.config import LazyCall as L
-from detectron2.data.detection_utils import get_fed_loss_cls_weights
-from detectron2.data.samplers import RepeatFactorTrainingSampler
-from detectron2.evaluation.lvis_evaluation import LVISEvaluator
-
-from ..COCO.cascade_mask_rcnn_mvitv2_b_in21k_100ep import (
-    dataloader,
-    model,
-    train,
-    lr_multiplier,
-    optimizer,
-)
-
-dataloader.train.dataset.names = "lvis_v1_train"
-dataloader.train.sampler = L(RepeatFactorTrainingSampler)(
-    repeat_factors=L(RepeatFactorTrainingSampler.repeat_factors_from_category_frequency)(
-        dataset_dicts="${dataloader.train.dataset}", repeat_thresh=0.001
-    )
-)
-dataloader.test.dataset.names = "lvis_v1_val"
-dataloader.evaluator = L(LVISEvaluator)(
-    dataset_name="${..test.dataset.names}",
-    max_dets_per_image=300,
-)
-
-model.roi_heads.num_classes = 1203
-for i in range(3):
-    model.roi_heads.box_predictors[i].test_score_thresh = 0.02
-    model.roi_heads.box_predictors[i].test_topk_per_image = 300
-    model.roi_heads.box_predictors[i].use_sigmoid_ce = True
-    model.roi_heads.box_predictors[i].use_fed_loss = True
-    model.roi_heads.box_predictors[i].get_fed_loss_cls_weights = lambda: get_fed_loss_cls_weights(
-        dataloader.train.dataset.names, 0.5
-    )
-
-# Schedule
-# 100 ep = 156250 iters * 64 images/iter / 100000 images/ep
-train.max_iter = 156250
-train.eval_period = 30000
-
-lr_multiplier.scheduler.milestones = [138889, 150463]
-lr_multiplier.scheduler.num_updates = train.max_iter
-lr_multiplier.warmup_length = 250 / train.max_iter
-
-optimizer.lr = 1e-4
diff --git a/spaces/cahya/websocket/README.md b/spaces/cahya/websocket/README.md
deleted file mode 100644
index d13f9d7a66a1657af475436f4babc49905e5e0e3..0000000000000000000000000000000000000000
--- a/spaces/cahya/websocket/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Websocket
-emoji: 📈
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
-license: cc
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/vis/bounding_box.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/vis/bounding_box.py
deleted file mode 100644
index 4f83957221f4503e707f2270a20e8d3829a299af..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/vis/bounding_box.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .base import RectangleVisualizer, TextVisualizer
-
-
-class BoundingBoxVisualizer(object):
-    def __init__(self):
-        self.rectangle_visualizer = RectangleVisualizer()
-
-    def visualize(self, image_bgr, boxes_xywh):
-        for bbox_xywh in boxes_xywh:
-            image_bgr = self.rectangle_visualizer.visualize(image_bgr, bbox_xywh)
-        return image_bgr
-
-
-class ScoredBoundingBoxVisualizer(object):
-    def __init__(self, bbox_visualizer_params=None, score_visualizer_params=None, **kwargs):
-        if bbox_visualizer_params is None:
-            bbox_visualizer_params = {}
-        if score_visualizer_params is None:
-            score_visualizer_params = {}
-        self.visualizer_bbox = RectangleVisualizer(**bbox_visualizer_params)
-        self.visualizer_score = TextVisualizer(**score_visualizer_params)
-
-    def visualize(self, image_bgr, scored_bboxes):
-        boxes_xywh, box_scores = scored_bboxes
-        assert len(boxes_xywh) == len(
-            box_scores
-        ), "Number of bounding boxes {} should be equal to the number of scores {}".format(
-            len(boxes_xywh), len(box_scores)
-        )
-        for i, box_xywh in enumerate(boxes_xywh):
-            score_i = box_scores[i]
-            image_bgr = self.visualizer_bbox.visualize(image_bgr, box_xywh)
-            score_txt = "{0:6.4f}".format(score_i)
-            topleft_xy = box_xywh[0], box_xywh[1]
-            image_bgr = self.visualizer_score.visualize(image_bgr, score_txt, topleft_xy)
-        return image_bgr
diff --git a/spaces/ceyda/fashion_classification/README.md b/spaces/ceyda/fashion_classification/README.md
deleted file mode 100644
index 1b60ecb08598698988d7397a44ecabf0fc09f655..0000000000000000000000000000000000000000
--- a/spaces/ceyda/fashion_classification/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-title: Fashion Classification
-emoji: 👗👕👖
-colorFrom: gray
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: data_analysis_app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-Just an easy template to visualize embeddings for image datasets
-
-https://huggingface.co/datasets/ceyda/fashion-products-small
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/models/auto/modeling_flax_auto.py b/spaces/chendl/compositional_test/transformers/src/transformers/models/auto/modeling_flax_auto.py
deleted file mode 100644
index 755d1f07a3440974c17503551c430f4a575fceb3..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/models/auto/modeling_flax_auto.py
+++ /dev/null
@@ -1,364 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google Flax Team Authors and The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Auto Model class."""
-
-
-from collections import OrderedDict
-
-from ...utils import logging
-from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
-from .configuration_auto import CONFIG_MAPPING_NAMES
-
-
-logger = logging.get_logger(__name__)
-
-
-FLAX_MODEL_MAPPING_NAMES = OrderedDict(
-    [
-        # Base model mapping
-        ("albert", "FlaxAlbertModel"),
-        ("bart", "FlaxBartModel"),
-        ("beit", "FlaxBeitModel"),
-        ("bert", "FlaxBertModel"),
-        ("big_bird", "FlaxBigBirdModel"),
-        ("blenderbot", "FlaxBlenderbotModel"),
-        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
-        ("clip", "FlaxCLIPModel"),
-        ("distilbert", "FlaxDistilBertModel"),
-        ("electra", "FlaxElectraModel"),
-        ("gpt-sw3", "FlaxGPT2Model"),
-        ("gpt2", "FlaxGPT2Model"),
-        ("gpt_neo", "FlaxGPTNeoModel"),
-        ("gptj", "FlaxGPTJModel"),
-        ("longt5", "FlaxLongT5Model"),
-        ("marian", "FlaxMarianModel"),
-        ("mbart", "FlaxMBartModel"),
-        ("mt5", "FlaxMT5Model"),
-        ("opt", "FlaxOPTModel"),
-        ("pegasus", "FlaxPegasusModel"),
-        ("regnet", "FlaxRegNetModel"),
-        ("resnet", "FlaxResNetModel"),
-        ("roberta", "FlaxRobertaModel"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
-        ("roformer", "FlaxRoFormerModel"),
-        ("t5", "FlaxT5Model"),
-        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
-        ("vit", "FlaxViTModel"),
-        ("wav2vec2", "FlaxWav2Vec2Model"),
-        ("whisper", "FlaxWhisperModel"),
-        ("xglm", "FlaxXGLMModel"),
-        ("xlm-roberta", "FlaxXLMRobertaModel"),
-    ]
-)
-
-FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for pre-training mapping
-        ("albert", "FlaxAlbertForPreTraining"),
-        ("bart", "FlaxBartForConditionalGeneration"),
-        ("bert", "FlaxBertForPreTraining"),
-        ("big_bird", "FlaxBigBirdForPreTraining"),
-        ("electra", "FlaxElectraForPreTraining"),
-        ("longt5", "FlaxLongT5ForConditionalGeneration"),
-        ("mbart", "FlaxMBartForConditionalGeneration"),
-        ("mt5", "FlaxMT5ForConditionalGeneration"),
-        ("roberta", "FlaxRobertaForMaskedLM"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
-        ("roformer", "FlaxRoFormerForMaskedLM"),
-        ("t5", "FlaxT5ForConditionalGeneration"),
-        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
-        ("whisper", "FlaxWhisperForConditionalGeneration"),
-        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
-    ]
-)
-
-FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Masked LM mapping
-        ("albert", "FlaxAlbertForMaskedLM"),
-        ("bart", "FlaxBartForConditionalGeneration"),
-        ("bert", "FlaxBertForMaskedLM"),
-        ("big_bird", "FlaxBigBirdForMaskedLM"),
-        ("distilbert", "FlaxDistilBertForMaskedLM"),
-        ("electra", "FlaxElectraForMaskedLM"),
-        ("mbart", "FlaxMBartForConditionalGeneration"),
-        ("roberta", "FlaxRobertaForMaskedLM"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
-        ("roformer", "FlaxRoFormerForMaskedLM"),
-        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
-    ]
-)
-
-FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Seq2Seq Causal LM mapping
-        ("bart", "FlaxBartForConditionalGeneration"),
-        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
-        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
-        ("encoder-decoder", "FlaxEncoderDecoderModel"),
-        ("longt5", "FlaxLongT5ForConditionalGeneration"),
-        ("marian", "FlaxMarianMTModel"),
-        ("mbart", "FlaxMBartForConditionalGeneration"),
-        ("mt5", "FlaxMT5ForConditionalGeneration"),
-        ("pegasus", "FlaxPegasusForConditionalGeneration"),
-        ("t5", "FlaxT5ForConditionalGeneration"),
-    ]
-)
-
-FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Image-classsification
-        ("beit", "FlaxBeitForImageClassification"),
-        ("regnet", "FlaxRegNetForImageClassification"),
-        ("resnet", "FlaxResNetForImageClassification"),
-        ("vit", "FlaxViTForImageClassification"),
-    ]
-)
-
-FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
-    [
-        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
-    ]
-)
-
-FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Causal LM mapping
-        ("bart", "FlaxBartForCausalLM"),
-        ("bert", "FlaxBertForCausalLM"),
-        ("big_bird", "FlaxBigBirdForCausalLM"),
-        ("electra", "FlaxElectraForCausalLM"),
-        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
-        ("gpt2", "FlaxGPT2LMHeadModel"),
-        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
-        ("gptj", "FlaxGPTJForCausalLM"),
-        ("opt", "FlaxOPTForCausalLM"),
-        ("roberta", "FlaxRobertaForCausalLM"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
-        ("xglm", "FlaxXGLMForCausalLM"),
-        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
-    ]
-)
-
-FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Sequence Classification mapping
-        ("albert", "FlaxAlbertForSequenceClassification"),
-        ("bart", "FlaxBartForSequenceClassification"),
-        ("bert", "FlaxBertForSequenceClassification"),
-        ("big_bird", "FlaxBigBirdForSequenceClassification"),
-        ("distilbert", "FlaxDistilBertForSequenceClassification"),
-        ("electra", "FlaxElectraForSequenceClassification"),
-        ("mbart", "FlaxMBartForSequenceClassification"),
-        ("roberta", "FlaxRobertaForSequenceClassification"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
-        ("roformer", "FlaxRoFormerForSequenceClassification"),
-        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
-    ]
-)
-
-FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Question Answering mapping
-        ("albert", "FlaxAlbertForQuestionAnswering"),
-        ("bart", "FlaxBartForQuestionAnswering"),
-        ("bert", "FlaxBertForQuestionAnswering"),
-        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
-        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
-        ("electra", "FlaxElectraForQuestionAnswering"),
-        ("mbart", "FlaxMBartForQuestionAnswering"),
-        ("roberta", "FlaxRobertaForQuestionAnswering"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
-        ("roformer", "FlaxRoFormerForQuestionAnswering"),
-        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
-    ]
-)
-
-FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Token Classification mapping
-        ("albert", "FlaxAlbertForTokenClassification"),
-        ("bert", "FlaxBertForTokenClassification"),
-        ("big_bird", "FlaxBigBirdForTokenClassification"),
-        ("distilbert", "FlaxDistilBertForTokenClassification"),
-        ("electra", "FlaxElectraForTokenClassification"),
-        ("roberta", "FlaxRobertaForTokenClassification"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
-        ("roformer", "FlaxRoFormerForTokenClassification"),
-        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
-    ]
-)
-
-FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict(
-    [
-        # Model for Multiple Choice mapping
-        ("albert", "FlaxAlbertForMultipleChoice"),
-        ("bert", "FlaxBertForMultipleChoice"),
-        ("big_bird", "FlaxBigBirdForMultipleChoice"),
-        ("distilbert", "FlaxDistilBertForMultipleChoice"),
-        ("electra", "FlaxElectraForMultipleChoice"),
-        ("roberta", "FlaxRobertaForMultipleChoice"),
-        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
-        ("roformer", "FlaxRoFormerForMultipleChoice"),
-        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
-    ]
-)
-
-FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict(
-    [
-        ("bert", "FlaxBertForNextSentencePrediction"),
-    ]
-)
-
-FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict(
-    [
-        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
-        ("whisper", "FlaxWhisperForConditionalGeneration"),
-    ]
-)
-
-
-FLAX_MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
-FLAX_MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
-FLAX_MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
-FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
-)
-FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
-)
-FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
-FLAX_MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
-FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
-)
-FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
-)
-FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
-)
-FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
-)
-FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
-)
-FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
-)
-
-
-class FlaxAutoModel(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_MAPPING
-
-
-FlaxAutoModel = auto_class_update(FlaxAutoModel)
-
-
-class FlaxAutoModelForPreTraining(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING
-
-
-FlaxAutoModelForPreTraining = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
-
-
-class FlaxAutoModelForCausalLM(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
-
-
-FlaxAutoModelForCausalLM = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
-
-
-class FlaxAutoModelForMaskedLM(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING
-
-
-FlaxAutoModelForMaskedLM = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
-
-
-class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
-
-
-FlaxAutoModelForSeq2SeqLM = auto_class_update(
-    FlaxAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
-)
-
-
-class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
-
-
-FlaxAutoModelForSequenceClassification = auto_class_update(
-    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
-)
-
-
-class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
-
-
-FlaxAutoModelForQuestionAnswering = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
-
-
-class FlaxAutoModelForTokenClassification(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
-
-
-FlaxAutoModelForTokenClassification = auto_class_update(
-    FlaxAutoModelForTokenClassification, head_doc="token classification"
-)
-
-
-class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
-
-
-FlaxAutoModelForMultipleChoice = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
-
-
-class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
-
-
-FlaxAutoModelForNextSentencePrediction = auto_class_update(
-    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
-)
-
-
-class FlaxAutoModelForImageClassification(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
-
-
-FlaxAutoModelForImageClassification = auto_class_update(
-    FlaxAutoModelForImageClassification, head_doc="image classification"
-)
-
-
-class FlaxAutoModelForVision2Seq(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
-
-
-FlaxAutoModelForVision2Seq = auto_class_update(FlaxAutoModelForVision2Seq, head_doc="vision-to-text modeling")
-
-
-class FlaxAutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
-    _model_mapping = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
-
-
-FlaxAutoModelForSpeechSeq2Seq = auto_class_update(
-    FlaxAutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling"
-)
diff --git a/spaces/chewing/liandan/build_db.py b/spaces/chewing/liandan/build_db.py
deleted file mode 100644
index 40a25323ad0d36da28f602ac79bf51003ecfa907..0000000000000000000000000000000000000000
--- a/spaces/chewing/liandan/build_db.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from src.utils import load_json
-from tinydb import TinyDB
-import os
-
-db_path = './db.json'
-if os.path.exists(db_path):
-    print("del db")
-    os.remove(db_path)
-db = TinyDB(db_path)
-
-material = db.table('material')
-for id,data in load_json("./data/药材.json").items():
-    name = data["name"]
-    level = data["level"]
-    material.insert({'name': f"{name}({level[:2]})", 'level': level,
-               'main_temp': data["主药"]["h_a_c"]["type"]*data["主药"]["h_a_c"]["power"],
-               'main_func_t': data["主药"]["type"],
-               'main_func_p': data["主药"]["power"],
-               'phar_temp': data["药引"]["h_a_c"]["type"]*data["药引"]["h_a_c"]["power"],
-               'auxi_func_t': data["辅药"]["type"],
-               'auxi_func_p': data["主药"]["power"],
-               })
-
-
-medicine = db.table('medicine')
-for id,data in load_json("./data/炼丹丹药.json").items():
-    name = data["name"]
-    desc = data["desc"]
-
-    if "点修为" in desc:
-        type = "增加修为"
-    elif "概率提升" in desc:
-        type = "突破概率"
-    elif "点攻击力" in desc:
-        type = "加攻击力"
-    else:
-        print(desc)
-        type = "???"
-
-    if type == "突破概率":
-        state_f = data["境界"][0:3]
-        t = desc.split("，")[1]
-        state_t = t[2:5]
-        name = name + f"({state_f}->{state_t})"
-    elif type == "增加修为":
-        num = desc[7:-4]
-        name = name + f"(修{num})"
-    elif type == "加攻击力":
-        num = desc[9:-5]
-        name = name + f"(攻{num})"
-
-    elixir_config = data["elixir_config"]
-    l0 = []
-    for key,i in elixir_config.items():
-        l0.append((int(key),i))
-    medicine.insert(
-        {
-            "name":name,
-            "type":type,
-            "func1_type":l0[0][0],
-            "func1_power":l0[0][1],
-            "func2_type": l0[1][0],
-            "func2_power": l0[1][1]
-        }
-    )
\ No newline at end of file
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/coloredlogs/syslog.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/coloredlogs/syslog.py
deleted file mode 100644
index 5fd4629ab56288bbfe0269ae6ba619db5d3756bc..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/coloredlogs/syslog.py
+++ /dev/null
@@ -1,292 +0,0 @@
-# Easy to use system logging for Python's logging module.
-#
-# Author: Peter Odding <peter@peterodding.com>
-# Last Change: December 10, 2020
-# URL: https://coloredlogs.readthedocs.io
-
-"""
-Easy to use UNIX system logging for Python's :mod:`logging` module.
-
-Admittedly system logging has little to do with colored terminal output, however:
-
-- The `coloredlogs` package is my attempt to do Python logging right and system
-  logging is an important part of that equation.
-
-- I've seen a surprising number of quirks and mistakes in system logging done
-  in Python, for example including ``%(asctime)s`` in a format string (the
-  system logging daemon is responsible for adding timestamps and thus you end
-  up with duplicate timestamps that make the logs awful to read :-).
-
-- The ``%(programname)s`` filter originated in my system logging code and I
-  wanted it in `coloredlogs` so the step to include this module wasn't that big.
-
-- As a bonus this Python module now has a test suite and proper documentation.
-
-So there :-P. Go take a look at :func:`enable_system_logging()`.
-"""
-
-# Standard library modules.
-import logging
-import logging.handlers
-import os
-import socket
-import sys
-
-# External dependencies.
-from humanfriendly import coerce_boolean
-from humanfriendly.compat import on_macos, on_windows
-
-# Modules included in our package.
-from coloredlogs import (
-    DEFAULT_LOG_LEVEL,
-    ProgramNameFilter,
-    adjust_level,
-    find_program_name,
-    level_to_number,
-    replace_handler,
-)
-
-LOG_DEVICE_MACOSX = '/var/run/syslog'
-"""The pathname of the log device on Mac OS X (a string)."""
-
-LOG_DEVICE_UNIX = '/dev/log'
-"""The pathname of the log device on Linux and most other UNIX systems (a string)."""
-
-DEFAULT_LOG_FORMAT = '%(programname)s[%(process)d]: %(levelname)s %(message)s'
-"""
-The default format for log messages sent to the system log (a string).
-
-The ``%(programname)s`` format requires :class:`~coloredlogs.ProgramNameFilter`
-but :func:`enable_system_logging()` takes care of this for you.
-
-The ``name[pid]:`` construct (specifically the colon) in the format allows
-rsyslogd_ to extract the ``$programname`` from each log message, which in turn
-allows configuration files in ``/etc/rsyslog.d/*.conf`` to filter these log
-messages to a separate log file (if the need arises).
-
-.. _rsyslogd: https://en.wikipedia.org/wiki/Rsyslog
-"""
-
-# Initialize a logger for this module.
-logger = logging.getLogger(__name__)
-
-
-class SystemLogging(object):
-
-    """Context manager to enable system logging."""
-
-    def __init__(self, *args, **kw):
-        """
-        Initialize a :class:`SystemLogging` object.
-
-        :param args: Positional arguments to :func:`enable_system_logging()`.
-        :param kw: Keyword arguments to :func:`enable_system_logging()`.
-        """
-        self.args = args
-        self.kw = kw
-        self.handler = None
-
-    def __enter__(self):
-        """Enable system logging when entering the context."""
-        if self.handler is None:
-            self.handler = enable_system_logging(*self.args, **self.kw)
-        return self.handler
-
-    def __exit__(self, exc_type=None, exc_value=None, traceback=None):
-        """
-        Disable system logging when leaving the context.
-
-        .. note:: If an exception is being handled when we leave the context a
-                  warning message including traceback is logged *before* system
-                  logging is disabled.
-        """
-        if self.handler is not None:
-            if exc_type is not None:
-                logger.warning("Disabling system logging due to unhandled exception!", exc_info=True)
-            (self.kw.get('logger') or logging.getLogger()).removeHandler(self.handler)
-            self.handler = None
-
-
-def enable_system_logging(programname=None, fmt=None, logger=None, reconfigure=True, **kw):
-    """
-    Redirect :mod:`logging` messages to the system log (e.g. ``/var/log/syslog``).
-
-    :param programname: The program name to embed in log messages (a string, defaults
-                         to the result of :func:`~coloredlogs.find_program_name()`).
-    :param fmt: The log format for system log messages (a string, defaults to
-                :data:`DEFAULT_LOG_FORMAT`).
-    :param logger: The logger to which the :class:`~logging.handlers.SysLogHandler`
-                   should be connected (defaults to the root logger).
-    :param level: The logging level for the :class:`~logging.handlers.SysLogHandler`
-                  (defaults to :data:`.DEFAULT_LOG_LEVEL`). This value is coerced
-                  using :func:`~coloredlogs.level_to_number()`.
-    :param reconfigure: If :data:`True` (the default) multiple calls to
-                        :func:`enable_system_logging()` will each override
-                        the previous configuration.
-    :param kw: Refer to :func:`connect_to_syslog()`.
-    :returns: A :class:`~logging.handlers.SysLogHandler` object or
-              :data:`None`. If an existing handler is found and `reconfigure`
-              is :data:`False` the existing handler object is returned. If the
-              connection to the system logging daemon fails :data:`None` is
-              returned.
-
-    As of release 15.0 this function uses :func:`is_syslog_supported()` to
-    check whether system logging is supported and appropriate before it's
-    enabled.
-
-    .. note:: When the logger's effective level is too restrictive it is
-              relaxed (refer to `notes about log levels`_ for details).
-    """
-    # Check whether system logging is supported / appropriate.
-    if not is_syslog_supported():
-        return None
-    # Provide defaults for omitted arguments.
-    programname = programname or find_program_name()
-    logger = logger or logging.getLogger()
-    fmt = fmt or DEFAULT_LOG_FORMAT
-    level = level_to_number(kw.get('level', DEFAULT_LOG_LEVEL))
-    # Check whether system logging is already enabled.
-    handler, logger = replace_handler(logger, match_syslog_handler, reconfigure)
-    # Make sure reconfiguration is allowed or not relevant.
-    if not (handler and not reconfigure):
-        # Create a system logging handler.
-        handler = connect_to_syslog(**kw)
-        # Make sure the handler was successfully created.
-        if handler:
-            # Enable the use of %(programname)s.
-            ProgramNameFilter.install(handler=handler, fmt=fmt, programname=programname)
-            # Connect the formatter, handler and logger.
-            handler.setFormatter(logging.Formatter(fmt))
-            logger.addHandler(handler)
-            # Adjust the level of the selected logger.
-            adjust_level(logger, level)
-    return handler
-
-
-def connect_to_syslog(address=None, facility=None, level=None):
-    """
-    Create a :class:`~logging.handlers.SysLogHandler`.
-
-    :param address: The device file or network address of the system logging
-                    daemon (a string or tuple, defaults to the result of
-                    :func:`find_syslog_address()`).
-    :param facility: Refer to :class:`~logging.handlers.SysLogHandler`.
-                     Defaults to ``LOG_USER``.
-    :param level: The logging level for the :class:`~logging.handlers.SysLogHandler`
-                  (defaults to :data:`.DEFAULT_LOG_LEVEL`). This value is coerced
-                  using :func:`~coloredlogs.level_to_number()`.
-    :returns: A :class:`~logging.handlers.SysLogHandler` object or :data:`None` (if the
-              system logging daemon is unavailable).
-
-    The process of connecting to the system logging daemon goes as follows:
-
-    - The following two socket types are tried (in decreasing preference):
-
-       1. :data:`~socket.SOCK_RAW` avoids truncation of log messages but may
-          not be supported.
-       2. :data:`~socket.SOCK_STREAM` (TCP) supports longer messages than the
-          default (which is UDP).
-    """
-    if not address:
-        address = find_syslog_address()
-    if facility is None:
-        facility = logging.handlers.SysLogHandler.LOG_USER
-    if level is None:
-        level = DEFAULT_LOG_LEVEL
-    for socktype in socket.SOCK_RAW, socket.SOCK_STREAM, None:
-        kw = dict(facility=facility, address=address)
-        if socktype is not None:
-            kw['socktype'] = socktype
-        try:
-            handler = logging.handlers.SysLogHandler(**kw)
-        except IOError:
-            # IOError is a superclass of socket.error which can be raised if the system
-            # logging daemon is unavailable.
-            pass
-        else:
-            handler.setLevel(level_to_number(level))
-            return handler
-
-
-def find_syslog_address():
-    """
-    Find the most suitable destination for system log messages.
-
-    :returns: The pathname of a log device (a string) or an address/port tuple as
-              supported by :class:`~logging.handlers.SysLogHandler`.
-
-    On Mac OS X this prefers :data:`LOG_DEVICE_MACOSX`, after that :data:`LOG_DEVICE_UNIX`
-    is checked for existence. If both of these device files don't exist the default used
-    by :class:`~logging.handlers.SysLogHandler` is returned.
-    """
-    if sys.platform == 'darwin' and os.path.exists(LOG_DEVICE_MACOSX):
-        return LOG_DEVICE_MACOSX
-    elif os.path.exists(LOG_DEVICE_UNIX):
-        return LOG_DEVICE_UNIX
-    else:
-        return 'localhost', logging.handlers.SYSLOG_UDP_PORT
-
-
-def is_syslog_supported():
-    """
-    Determine whether system logging is supported.
-
-    :returns:
-
-        :data:`True` if system logging is supported and can be enabled,
-        :data:`False` if system logging is not supported or there are good
-        reasons for not enabling it.
-
-    The decision making process here is as follows:
-
-    Override
-     If the environment variable ``$COLOREDLOGS_SYSLOG`` is set it is evaluated
-     using :func:`~humanfriendly.coerce_boolean()` and the resulting value
-     overrides the platform detection discussed below, this allows users to
-     override the decision making process if they disagree / know better.
-
-    Linux / UNIX
-     On systems that are not Windows or MacOS (see below) we assume UNIX which
-     means either syslog is available or sending a bunch of UDP packets to
-     nowhere won't hurt anyone...
-
-    Microsoft Windows
-     Over the years I've had multiple reports of :pypi:`coloredlogs` spewing
-     extremely verbose errno 10057 warning messages to the console (once for
-     each log message I suppose) so I now assume it a default that
-     "syslog-style system logging" is not generally available on Windows.
-
-    Apple MacOS
-     There's cPython issue `#38780`_ which seems to result in a fatal exception
-     when the Python interpreter shuts down. This is (way) worse than not
-     having system logging enabled. The error message mentioned in `#38780`_
-     has actually been following me around for years now, see for example:
-
-     - https://github.com/xolox/python-rotate-backups/issues/9 mentions Docker
-       images implying Linux, so not strictly the same as `#38780`_.
-
-     - https://github.com/xolox/python-npm-accel/issues/4 is definitely related
-       to `#38780`_ and is what eventually prompted me to add the
-       :func:`is_syslog_supported()` logic.
-
-    .. _#38780: https://bugs.python.org/issue38780
-    """
-    override = os.environ.get("COLOREDLOGS_SYSLOG")
-    if override is not None:
-        return coerce_boolean(override)
-    else:
-        return not (on_windows() or on_macos())
-
-
-def match_syslog_handler(handler):
-    """
-    Identify system logging handlers.
-
-    :param handler: The :class:`~logging.Handler` class to check.
-    :returns: :data:`True` if the handler is a
-              :class:`~logging.handlers.SysLogHandler`,
-              :data:`False` otherwise.
-
-    This function can be used as a callback for :func:`.find_handler()`.
-    """
-    return isinstance(handler, logging.handlers.SysLogHandler)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/asymmetric/x448.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/asymmetric/x448.py
deleted file mode 100644
index abf7848550b8b42fea24155e614d4228fcf2b1e1..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/asymmetric/x448.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from __future__ import annotations
-
-import abc
-
-from cryptography.exceptions import UnsupportedAlgorithm, _Reasons
-from cryptography.hazmat.bindings._rust import openssl as rust_openssl
-from cryptography.hazmat.primitives import _serialization
-
-
-class X448PublicKey(metaclass=abc.ABCMeta):
-    @classmethod
-    def from_public_bytes(cls, data: bytes) -> X448PublicKey:
-        from cryptography.hazmat.backends.openssl.backend import backend
-
-        if not backend.x448_supported():
-            raise UnsupportedAlgorithm(
-                "X448 is not supported by this version of OpenSSL.",
-                _Reasons.UNSUPPORTED_EXCHANGE_ALGORITHM,
-            )
-
-        return backend.x448_load_public_bytes(data)
-
-    @abc.abstractmethod
-    def public_bytes(
-        self,
-        encoding: _serialization.Encoding,
-        format: _serialization.PublicFormat,
-    ) -> bytes:
-        """
-        The serialized bytes of the public key.
-        """
-
-    @abc.abstractmethod
-    def public_bytes_raw(self) -> bytes:
-        """
-        The raw bytes of the public key.
-        Equivalent to public_bytes(Raw, Raw).
-        """
-
-    @abc.abstractmethod
-    def __eq__(self, other: object) -> bool:
-        """
-        Checks equality.
-        """
-
-
-if hasattr(rust_openssl, "x448"):
-    X448PublicKey.register(rust_openssl.x448.X448PublicKey)
-
-
-class X448PrivateKey(metaclass=abc.ABCMeta):
-    @classmethod
-    def generate(cls) -> X448PrivateKey:
-        from cryptography.hazmat.backends.openssl.backend import backend
-
-        if not backend.x448_supported():
-            raise UnsupportedAlgorithm(
-                "X448 is not supported by this version of OpenSSL.",
-                _Reasons.UNSUPPORTED_EXCHANGE_ALGORITHM,
-            )
-        return backend.x448_generate_key()
-
-    @classmethod
-    def from_private_bytes(cls, data: bytes) -> X448PrivateKey:
-        from cryptography.hazmat.backends.openssl.backend import backend
-
-        if not backend.x448_supported():
-            raise UnsupportedAlgorithm(
-                "X448 is not supported by this version of OpenSSL.",
-                _Reasons.UNSUPPORTED_EXCHANGE_ALGORITHM,
-            )
-
-        return backend.x448_load_private_bytes(data)
-
-    @abc.abstractmethod
-    def public_key(self) -> X448PublicKey:
-        """
-        Returns the public key associated with this private key
-        """
-
-    @abc.abstractmethod
-    def private_bytes(
-        self,
-        encoding: _serialization.Encoding,
-        format: _serialization.PrivateFormat,
-        encryption_algorithm: _serialization.KeySerializationEncryption,
-    ) -> bytes:
-        """
-        The serialized bytes of the private key.
-        """
-
-    @abc.abstractmethod
-    def private_bytes_raw(self) -> bytes:
-        """
-        The raw bytes of the private key.
-        Equivalent to private_bytes(Raw, Raw, NoEncryption()).
-        """
-
-    @abc.abstractmethod
-    def exchange(self, peer_public_key: X448PublicKey) -> bytes:
-        """
-        Performs a key exchange operation using the provided peer's public key.
-        """
-
-
-if hasattr(rust_openssl, "x448"):
-    X448PrivateKey.register(rust_openssl.x448.X448PrivateKey)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/cffLib/specializer.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/cffLib/specializer.py
deleted file mode 100644
index 3d28c82dc77b8b8b764bcf76d401265903db1a64..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/cffLib/specializer.py
+++ /dev/null
@@ -1,850 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""T2CharString operator specializer and generalizer.
-
-PostScript glyph drawing operations can be expressed in multiple different
-ways. For example, as well as the ``lineto`` operator, there is also a
-``hlineto`` operator which draws a horizontal line, removing the need to
-specify a ``dx`` coordinate, and a ``vlineto`` operator which draws a
-vertical line, removing the need to specify a ``dy`` coordinate. As well
-as decompiling :class:`fontTools.misc.psCharStrings.T2CharString` objects
-into lists of operations, this module allows for conversion between general
-and specific forms of the operation.
-
-"""
-
-from fontTools.cffLib import maxStackLimit
-
-
-def stringToProgram(string):
-    if isinstance(string, str):
-        string = string.split()
-    program = []
-    for token in string:
-        try:
-            token = int(token)
-        except ValueError:
-            try:
-                token = float(token)
-            except ValueError:
-                pass
-        program.append(token)
-    return program
-
-
-def programToString(program):
-    return " ".join(str(x) for x in program)
-
-
-def programToCommands(program, getNumRegions=None):
-    """Takes a T2CharString program list and returns list of commands.
-    Each command is a two-tuple of commandname,arg-list.  The commandname might
-    be empty string if no commandname shall be emitted (used for glyph width,
-    hintmask/cntrmask argument, as well as stray arguments at the end of the
-    program (¯\_(ツ)_/¯).
-    'getNumRegions' may be None, or a callable object. It must return the
-    number of regions. 'getNumRegions' takes a single argument, vsindex. If
-    the vsindex argument is None, getNumRegions returns the default number
-    of regions for the charstring, else it returns the numRegions for
-    the vsindex.
-    The Charstring may or may not start with a width value. If the first
-    non-blend operator has an odd number of arguments, then the first argument is
-    a width, and is popped off. This is complicated with blend operators, as
-    there may be more than one before the first hint or moveto operator, and each
-    one reduces several arguments to just one list argument. We have to sum the
-    number of arguments that are not part of the blend arguments, and all the
-    'numBlends' values. We could instead have said that by definition, if there
-    is a blend operator, there is no width value, since CFF2 Charstrings don't
-    have width values. I discussed this with Behdad, and we are allowing for an
-    initial width value in this case because developers may assemble a CFF2
-    charstring from CFF Charstrings, which could have width values.
-    """
-
-    seenWidthOp = False
-    vsIndex = None
-    lenBlendStack = 0
-    lastBlendIndex = 0
-    commands = []
-    stack = []
-    it = iter(program)
-
-    for token in it:
-        if not isinstance(token, str):
-            stack.append(token)
-            continue
-
-        if token == "blend":
-            assert getNumRegions is not None
-            numSourceFonts = 1 + getNumRegions(vsIndex)
-            # replace the blend op args on the stack with a single list
-            # containing all the blend op args.
-            numBlends = stack[-1]
-            numBlendArgs = numBlends * numSourceFonts + 1
-            # replace first blend op by a list of the blend ops.
-            stack[-numBlendArgs:] = [stack[-numBlendArgs:]]
-            lenBlendStack += numBlends + len(stack) - 1
-            lastBlendIndex = len(stack)
-            # if a blend op exists, this is or will be a CFF2 charstring.
-            continue
-
-        elif token == "vsindex":
-            vsIndex = stack[-1]
-            assert type(vsIndex) is int
-
-        elif (not seenWidthOp) and token in {
-            "hstem",
-            "hstemhm",
-            "vstem",
-            "vstemhm",
-            "cntrmask",
-            "hintmask",
-            "hmoveto",
-            "vmoveto",
-            "rmoveto",
-            "endchar",
-        }:
-            seenWidthOp = True
-            parity = token in {"hmoveto", "vmoveto"}
-            if lenBlendStack:
-                # lenBlendStack has the number of args represented by the last blend
-                # arg and all the preceding args. We need to now add the number of
-                # args following the last blend arg.
-                numArgs = lenBlendStack + len(stack[lastBlendIndex:])
-            else:
-                numArgs = len(stack)
-            if numArgs and (numArgs % 2) ^ parity:
-                width = stack.pop(0)
-                commands.append(("", [width]))
-
-        if token in {"hintmask", "cntrmask"}:
-            if stack:
-                commands.append(("", stack))
-            commands.append((token, []))
-            commands.append(("", [next(it)]))
-        else:
-            commands.append((token, stack))
-        stack = []
-    if stack:
-        commands.append(("", stack))
-    return commands
-
-
-def _flattenBlendArgs(args):
-    token_list = []
-    for arg in args:
-        if isinstance(arg, list):
-            token_list.extend(arg)
-            token_list.append("blend")
-        else:
-            token_list.append(arg)
-    return token_list
-
-
-def commandsToProgram(commands):
-    """Takes a commands list as returned by programToCommands() and converts
-    it back to a T2CharString program list."""
-    program = []
-    for op, args in commands:
-        if any(isinstance(arg, list) for arg in args):
-            args = _flattenBlendArgs(args)
-        program.extend(args)
-        if op:
-            program.append(op)
-    return program
-
-
-def _everyN(el, n):
-    """Group the list el into groups of size n"""
-    if len(el) % n != 0:
-        raise ValueError(el)
-    for i in range(0, len(el), n):
-        yield el[i : i + n]
-
-
-class _GeneralizerDecombinerCommandsMap(object):
-    @staticmethod
-    def rmoveto(args):
-        if len(args) != 2:
-            raise ValueError(args)
-        yield ("rmoveto", args)
-
-    @staticmethod
-    def hmoveto(args):
-        if len(args) != 1:
-            raise ValueError(args)
-        yield ("rmoveto", [args[0], 0])
-
-    @staticmethod
-    def vmoveto(args):
-        if len(args) != 1:
-            raise ValueError(args)
-        yield ("rmoveto", [0, args[0]])
-
-    @staticmethod
-    def rlineto(args):
-        if not args:
-            raise ValueError(args)
-        for args in _everyN(args, 2):
-            yield ("rlineto", args)
-
-    @staticmethod
-    def hlineto(args):
-        if not args:
-            raise ValueError(args)
-        it = iter(args)
-        try:
-            while True:
-                yield ("rlineto", [next(it), 0])
-                yield ("rlineto", [0, next(it)])
-        except StopIteration:
-            pass
-
-    @staticmethod
-    def vlineto(args):
-        if not args:
-            raise ValueError(args)
-        it = iter(args)
-        try:
-            while True:
-                yield ("rlineto", [0, next(it)])
-                yield ("rlineto", [next(it), 0])
-        except StopIteration:
-            pass
-
-    @staticmethod
-    def rrcurveto(args):
-        if not args:
-            raise ValueError(args)
-        for args in _everyN(args, 6):
-            yield ("rrcurveto", args)
-
-    @staticmethod
-    def hhcurveto(args):
-        if len(args) < 4 or len(args) % 4 > 1:
-            raise ValueError(args)
-        if len(args) % 2 == 1:
-            yield ("rrcurveto", [args[1], args[0], args[2], args[3], args[4], 0])
-            args = args[5:]
-        for args in _everyN(args, 4):
-            yield ("rrcurveto", [args[0], 0, args[1], args[2], args[3], 0])
-
-    @staticmethod
-    def vvcurveto(args):
-        if len(args) < 4 or len(args) % 4 > 1:
-            raise ValueError(args)
-        if len(args) % 2 == 1:
-            yield ("rrcurveto", [args[0], args[1], args[2], args[3], 0, args[4]])
-            args = args[5:]
-        for args in _everyN(args, 4):
-            yield ("rrcurveto", [0, args[0], args[1], args[2], 0, args[3]])
-
-    @staticmethod
-    def hvcurveto(args):
-        if len(args) < 4 or len(args) % 8 not in {0, 1, 4, 5}:
-            raise ValueError(args)
-        last_args = None
-        if len(args) % 2 == 1:
-            lastStraight = len(args) % 8 == 5
-            args, last_args = args[:-5], args[-5:]
-        it = _everyN(args, 4)
-        try:
-            while True:
-                args = next(it)
-                yield ("rrcurveto", [args[0], 0, args[1], args[2], 0, args[3]])
-                args = next(it)
-                yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], 0])
-        except StopIteration:
-            pass
-        if last_args:
-            args = last_args
-            if lastStraight:
-                yield ("rrcurveto", [args[0], 0, args[1], args[2], args[4], args[3]])
-            else:
-                yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], args[4]])
-
-    @staticmethod
-    def vhcurveto(args):
-        if len(args) < 4 or len(args) % 8 not in {0, 1, 4, 5}:
-            raise ValueError(args)
-        last_args = None
-        if len(args) % 2 == 1:
-            lastStraight = len(args) % 8 == 5
-            args, last_args = args[:-5], args[-5:]
-        it = _everyN(args, 4)
-        try:
-            while True:
-                args = next(it)
-                yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], 0])
-                args = next(it)
-                yield ("rrcurveto", [args[0], 0, args[1], args[2], 0, args[3]])
-        except StopIteration:
-            pass
-        if last_args:
-            args = last_args
-            if lastStraight:
-                yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], args[4]])
-            else:
-                yield ("rrcurveto", [args[0], 0, args[1], args[2], args[4], args[3]])
-
-    @staticmethod
-    def rcurveline(args):
-        if len(args) < 8 or len(args) % 6 != 2:
-            raise ValueError(args)
-        args, last_args = args[:-2], args[-2:]
-        for args in _everyN(args, 6):
-            yield ("rrcurveto", args)
-        yield ("rlineto", last_args)
-
-    @staticmethod
-    def rlinecurve(args):
-        if len(args) < 8 or len(args) % 2 != 0:
-            raise ValueError(args)
-        args, last_args = args[:-6], args[-6:]
-        for args in _everyN(args, 2):
-            yield ("rlineto", args)
-        yield ("rrcurveto", last_args)
-
-
-def _convertBlendOpToArgs(blendList):
-    # args is list of blend op args. Since we are supporting
-    # recursive blend op calls, some of these args may also
-    # be a list of blend op args, and need to be converted before
-    # we convert the current list.
-    if any([isinstance(arg, list) for arg in blendList]):
-        args = [
-            i
-            for e in blendList
-            for i in (_convertBlendOpToArgs(e) if isinstance(e, list) else [e])
-        ]
-    else:
-        args = blendList
-
-    # We now know that blendList contains a blend op argument list, even if
-    # some of the args are lists that each contain a blend op argument list.
-    # 	Convert from:
-    # 		[default font arg sequence x0,...,xn] + [delta tuple for x0] + ... + [delta tuple for xn]
-    # 	to:
-    # 		[ [x0] + [delta tuple for x0],
-    #                 ...,
-    #          [xn] + [delta tuple for xn] ]
-    numBlends = args[-1]
-    # Can't use args.pop() when the args are being used in a nested list
-    # comprehension. See calling context
-    args = args[:-1]
-
-    numRegions = len(args) // numBlends - 1
-    if not (numBlends * (numRegions + 1) == len(args)):
-        raise ValueError(blendList)
-
-    defaultArgs = [[arg] for arg in args[:numBlends]]
-    deltaArgs = args[numBlends:]
-    numDeltaValues = len(deltaArgs)
-    deltaList = [
-        deltaArgs[i : i + numRegions] for i in range(0, numDeltaValues, numRegions)
-    ]
-    blend_args = [a + b + [1] for a, b in zip(defaultArgs, deltaList)]
-    return blend_args
-
-
-def generalizeCommands(commands, ignoreErrors=False):
-    result = []
-    mapping = _GeneralizerDecombinerCommandsMap
-    for op, args in commands:
-        # First, generalize any blend args in the arg list.
-        if any([isinstance(arg, list) for arg in args]):
-            try:
-                args = [
-                    n
-                    for arg in args
-                    for n in (
-                        _convertBlendOpToArgs(arg) if isinstance(arg, list) else [arg]
-                    )
-                ]
-            except ValueError:
-                if ignoreErrors:
-                    # Store op as data, such that consumers of commands do not have to
-                    # deal with incorrect number of arguments.
-                    result.append(("", args))
-                    result.append(("", [op]))
-                else:
-                    raise
-
-        func = getattr(mapping, op, None)
-        if not func:
-            result.append((op, args))
-            continue
-        try:
-            for command in func(args):
-                result.append(command)
-        except ValueError:
-            if ignoreErrors:
-                # Store op as data, such that consumers of commands do not have to
-                # deal with incorrect number of arguments.
-                result.append(("", args))
-                result.append(("", [op]))
-            else:
-                raise
-    return result
-
-
-def generalizeProgram(program, getNumRegions=None, **kwargs):
-    return commandsToProgram(
-        generalizeCommands(programToCommands(program, getNumRegions), **kwargs)
-    )
-
-
-def _categorizeVector(v):
-    """
-    Takes X,Y vector v and returns one of r, h, v, or 0 depending on which
-    of X and/or Y are zero, plus tuple of nonzero ones.  If both are zero,
-    it returns a single zero still.
-
-    >>> _categorizeVector((0,0))
-    ('0', (0,))
-    >>> _categorizeVector((1,0))
-    ('h', (1,))
-    >>> _categorizeVector((0,2))
-    ('v', (2,))
-    >>> _categorizeVector((1,2))
-    ('r', (1, 2))
-    """
-    if not v[0]:
-        if not v[1]:
-            return "0", v[:1]
-        else:
-            return "v", v[1:]
-    else:
-        if not v[1]:
-            return "h", v[:1]
-        else:
-            return "r", v
-
-
-def _mergeCategories(a, b):
-    if a == "0":
-        return b
-    if b == "0":
-        return a
-    if a == b:
-        return a
-    return None
-
-
-def _negateCategory(a):
-    if a == "h":
-        return "v"
-    if a == "v":
-        return "h"
-    assert a in "0r"
-    return a
-
-
-def _convertToBlendCmds(args):
-    # return a list of blend commands, and
-    # the remaining non-blended args, if any.
-    num_args = len(args)
-    stack_use = 0
-    new_args = []
-    i = 0
-    while i < num_args:
-        arg = args[i]
-        if not isinstance(arg, list):
-            new_args.append(arg)
-            i += 1
-            stack_use += 1
-        else:
-            prev_stack_use = stack_use
-            # The arg is a tuple of blend values.
-            # These are each (master 0,delta 1..delta n, 1)
-            # Combine as many successive tuples as we can,
-            # up to the max stack limit.
-            num_sources = len(arg) - 1
-            blendlist = [arg]
-            i += 1
-            stack_use += 1 + num_sources  # 1 for the num_blends arg
-            while (i < num_args) and isinstance(args[i], list):
-                blendlist.append(args[i])
-                i += 1
-                stack_use += num_sources
-                if stack_use + num_sources > maxStackLimit:
-                    # if we are here, max stack is the CFF2 max stack.
-                    # I use the CFF2 max stack limit here rather than
-                    # the 'maxstack' chosen by the client, as the default
-                    #  maxstack may have been used unintentionally. For all
-                    # the other operators, this just produces a little less
-                    # optimization, but here it puts a hard (and low) limit
-                    # on the number of source fonts that can be used.
-                    break
-            # blendList now contains as many single blend tuples as can be
-            # combined without exceeding the CFF2 stack limit.
-            num_blends = len(blendlist)
-            # append the 'num_blends' default font values
-            blend_args = []
-            for arg in blendlist:
-                blend_args.append(arg[0])
-            for arg in blendlist:
-                assert arg[-1] == 1
-                blend_args.extend(arg[1:-1])
-            blend_args.append(num_blends)
-            new_args.append(blend_args)
-            stack_use = prev_stack_use + num_blends
-
-    return new_args
-
-
-def _addArgs(a, b):
-    if isinstance(b, list):
-        if isinstance(a, list):
-            if len(a) != len(b) or a[-1] != b[-1]:
-                raise ValueError()
-            return [_addArgs(va, vb) for va, vb in zip(a[:-1], b[:-1])] + [a[-1]]
-        else:
-            a, b = b, a
-    if isinstance(a, list):
-        assert a[-1] == 1
-        return [_addArgs(a[0], b)] + a[1:]
-    return a + b
-
-
-def specializeCommands(
-    commands,
-    ignoreErrors=False,
-    generalizeFirst=True,
-    preserveTopology=False,
-    maxstack=48,
-):
-
-    # We perform several rounds of optimizations.  They are carefully ordered and are:
-    #
-    # 0. Generalize commands.
-    #    This ensures that they are in our expected simple form, with each line/curve only
-    #    having arguments for one segment, and using the generic form (rlineto/rrcurveto).
-    #    If caller is sure the input is in this form, they can turn off generalization to
-    #    save time.
-    #
-    # 1. Combine successive rmoveto operations.
-    #
-    # 2. Specialize rmoveto/rlineto/rrcurveto operators into horizontal/vertical variants.
-    #    We specialize into some, made-up, variants as well, which simplifies following
-    #    passes.
-    #
-    # 3. Merge or delete redundant operations, to the extent requested.
-    #    OpenType spec declares point numbers in CFF undefined.  As such, we happily
-    #    change topology.  If client relies on point numbers (in GPOS anchors, or for
-    #    hinting purposes(what?)) they can turn this off.
-    #
-    # 4. Peephole optimization to revert back some of the h/v variants back into their
-    #    original "relative" operator (rline/rrcurveto) if that saves a byte.
-    #
-    # 5. Combine adjacent operators when possible, minding not to go over max stack size.
-    #
-    # 6. Resolve any remaining made-up operators into real operators.
-    #
-    # I have convinced myself that this produces optimal bytecode (except for, possibly
-    # one byte each time maxstack size prohibits combining.)  YMMV, but you'd be wrong. :-)
-    # A dynamic-programming approach can do the same but would be significantly slower.
-    #
-    # 7. For any args which are blend lists, convert them to a blend command.
-
-    # 0. Generalize commands.
-    if generalizeFirst:
-        commands = generalizeCommands(commands, ignoreErrors=ignoreErrors)
-    else:
-        commands = list(commands)  # Make copy since we modify in-place later.
-
-    # 1. Combine successive rmoveto operations.
-    for i in range(len(commands) - 1, 0, -1):
-        if "rmoveto" == commands[i][0] == commands[i - 1][0]:
-            v1, v2 = commands[i - 1][1], commands[i][1]
-            commands[i - 1] = ("rmoveto", [v1[0] + v2[0], v1[1] + v2[1]])
-            del commands[i]
-
-    # 2. Specialize rmoveto/rlineto/rrcurveto operators into horizontal/vertical variants.
-    #
-    # We, in fact, specialize into more, made-up, variants that special-case when both
-    # X and Y components are zero.  This simplifies the following optimization passes.
-    # This case is rare, but OCD does not let me skip it.
-    #
-    # After this round, we will have four variants that use the following mnemonics:
-    #
-    #  - 'r' for relative,   ie. non-zero X and non-zero Y,
-    #  - 'h' for horizontal, ie. zero X and non-zero Y,
-    #  - 'v' for vertical,   ie. non-zero X and zero Y,
-    #  - '0' for zeros,      ie. zero X and zero Y.
-    #
-    # The '0' pseudo-operators are not part of the spec, but help simplify the following
-    # optimization rounds.  We resolve them at the end.  So, after this, we will have four
-    # moveto and four lineto variants:
-    #
-    #  - 0moveto, 0lineto
-    #  - hmoveto, hlineto
-    #  - vmoveto, vlineto
-    #  - rmoveto, rlineto
-    #
-    # and sixteen curveto variants.  For example, a '0hcurveto' operator means a curve
-    # dx0,dy0,dx1,dy1,dx2,dy2,dx3,dy3 where dx0, dx1, and dy3 are zero but not dx3.
-    # An 'rvcurveto' means dx3 is zero but not dx0,dy0,dy3.
-    #
-    # There are nine different variants of curves without the '0'.  Those nine map exactly
-    # to the existing curve variants in the spec: rrcurveto, and the four variants hhcurveto,
-    # vvcurveto, hvcurveto, and vhcurveto each cover two cases, one with an odd number of
-    # arguments and one without.  Eg. an hhcurveto with an extra argument (odd number of
-    # arguments) is in fact an rhcurveto.  The operators in the spec are designed such that
-    # all four of rhcurveto, rvcurveto, hrcurveto, and vrcurveto are encodable for one curve.
-    #
-    # Of the curve types with '0', the 00curveto is equivalent to a lineto variant.  The rest
-    # of the curve types with a 0 need to be encoded as a h or v variant.  Ie. a '0' can be
-    # thought of a "don't care" and can be used as either an 'h' or a 'v'.  As such, we always
-    # encode a number 0 as argument when we use a '0' variant.  Later on, we can just substitute
-    # the '0' with either 'h' or 'v' and it works.
-    #
-    # When we get to curve splines however, things become more complicated...  XXX finish this.
-    # There's one more complexity with splines.  If one side of the spline is not horizontal or
-    # vertical (or zero), ie. if it's 'r', then it limits which spline types we can encode.
-    # Only hhcurveto and vvcurveto operators can encode a spline starting with 'r', and
-    # only hvcurveto and vhcurveto operators can encode a spline ending with 'r'.
-    # This limits our merge opportunities later.
-    #
-    for i in range(len(commands)):
-        op, args = commands[i]
-
-        if op in {"rmoveto", "rlineto"}:
-            c, args = _categorizeVector(args)
-            commands[i] = c + op[1:], args
-            continue
-
-        if op == "rrcurveto":
-            c1, args1 = _categorizeVector(args[:2])
-            c2, args2 = _categorizeVector(args[-2:])
-            commands[i] = c1 + c2 + "curveto", args1 + args[2:4] + args2
-            continue
-
-    # 3. Merge or delete redundant operations, to the extent requested.
-    #
-    # TODO
-    # A 0moveto that comes before all other path operations can be removed.
-    # though I find conflicting evidence for this.
-    #
-    # TODO
-    # "If hstem and vstem hints are both declared at the beginning of a
-    # CharString, and this sequence is followed directly by the hintmask or
-    # cntrmask operators, then the vstem hint operator (or, if applicable,
-    # the vstemhm operator) need not be included."
-    #
-    # "The sequence and form of a CFF2 CharString program may be represented as:
-    # {hs* vs* cm* hm* mt subpath}? {mt subpath}*"
-    #
-    # https://www.microsoft.com/typography/otspec/cff2charstr.htm#section3.1
-    #
-    # For Type2 CharStrings the sequence is:
-    # w? {hs* vs* cm* hm* mt subpath}? {mt subpath}* endchar"
-
-    # Some other redundancies change topology (point numbers).
-    if not preserveTopology:
-        for i in range(len(commands) - 1, -1, -1):
-            op, args = commands[i]
-
-            # A 00curveto is demoted to a (specialized) lineto.
-            if op == "00curveto":
-                assert len(args) == 4
-                c, args = _categorizeVector(args[1:3])
-                op = c + "lineto"
-                commands[i] = op, args
-                # and then...
-
-            # A 0lineto can be deleted.
-            if op == "0lineto":
-                del commands[i]
-                continue
-
-            # Merge adjacent hlineto's and vlineto's.
-            # In CFF2 charstrings from variable fonts, each
-            # arg item may be a list of blendable values, one from
-            # each source font.
-            if i and op in {"hlineto", "vlineto"} and (op == commands[i - 1][0]):
-                _, other_args = commands[i - 1]
-                assert len(args) == 1 and len(other_args) == 1
-                try:
-                    new_args = [_addArgs(args[0], other_args[0])]
-                except ValueError:
-                    continue
-                commands[i - 1] = (op, new_args)
-                del commands[i]
-                continue
-
-    # 4. Peephole optimization to revert back some of the h/v variants back into their
-    #    original "relative" operator (rline/rrcurveto) if that saves a byte.
-    for i in range(1, len(commands) - 1):
-        op, args = commands[i]
-        prv, nxt = commands[i - 1][0], commands[i + 1][0]
-
-        if op in {"0lineto", "hlineto", "vlineto"} and prv == nxt == "rlineto":
-            assert len(args) == 1
-            args = [0, args[0]] if op[0] == "v" else [args[0], 0]
-            commands[i] = ("rlineto", args)
-            continue
-
-        if op[2:] == "curveto" and len(args) == 5 and prv == nxt == "rrcurveto":
-            assert (op[0] == "r") ^ (op[1] == "r")
-            if op[0] == "v":
-                pos = 0
-            elif op[0] != "r":
-                pos = 1
-            elif op[1] == "v":
-                pos = 4
-            else:
-                pos = 5
-            # Insert, while maintaining the type of args (can be tuple or list).
-            args = args[:pos] + type(args)((0,)) + args[pos:]
-            commands[i] = ("rrcurveto", args)
-            continue
-
-    # 5. Combine adjacent operators when possible, minding not to go over max stack size.
-    for i in range(len(commands) - 1, 0, -1):
-        op1, args1 = commands[i - 1]
-        op2, args2 = commands[i]
-        new_op = None
-
-        # Merge logic...
-        if {op1, op2} <= {"rlineto", "rrcurveto"}:
-            if op1 == op2:
-                new_op = op1
-            else:
-                if op2 == "rrcurveto" and len(args2) == 6:
-                    new_op = "rlinecurve"
-                elif len(args2) == 2:
-                    new_op = "rcurveline"
-
-        elif (op1, op2) in {("rlineto", "rlinecurve"), ("rrcurveto", "rcurveline")}:
-            new_op = op2
-
-        elif {op1, op2} == {"vlineto", "hlineto"}:
-            new_op = op1
-
-        elif "curveto" == op1[2:] == op2[2:]:
-            d0, d1 = op1[:2]
-            d2, d3 = op2[:2]
-
-            if d1 == "r" or d2 == "r" or d0 == d3 == "r":
-                continue
-
-            d = _mergeCategories(d1, d2)
-            if d is None:
-                continue
-            if d0 == "r":
-                d = _mergeCategories(d, d3)
-                if d is None:
-                    continue
-                new_op = "r" + d + "curveto"
-            elif d3 == "r":
-                d0 = _mergeCategories(d0, _negateCategory(d))
-                if d0 is None:
-                    continue
-                new_op = d0 + "r" + "curveto"
-            else:
-                d0 = _mergeCategories(d0, d3)
-                if d0 is None:
-                    continue
-                new_op = d0 + d + "curveto"
-
-        # Make sure the stack depth does not exceed (maxstack - 1), so
-        # that subroutinizer can insert subroutine calls at any point.
-        if new_op and len(args1) + len(args2) < maxstack:
-            commands[i - 1] = (new_op, args1 + args2)
-            del commands[i]
-
-    # 6. Resolve any remaining made-up operators into real operators.
-    for i in range(len(commands)):
-        op, args = commands[i]
-
-        if op in {"0moveto", "0lineto"}:
-            commands[i] = "h" + op[1:], args
-            continue
-
-        if op[2:] == "curveto" and op[:2] not in {"rr", "hh", "vv", "vh", "hv"}:
-            op0, op1 = op[:2]
-            if (op0 == "r") ^ (op1 == "r"):
-                assert len(args) % 2 == 1
-            if op0 == "0":
-                op0 = "h"
-            if op1 == "0":
-                op1 = "h"
-            if op0 == "r":
-                op0 = op1
-            if op1 == "r":
-                op1 = _negateCategory(op0)
-            assert {op0, op1} <= {"h", "v"}, (op0, op1)
-
-            if len(args) % 2:
-                if op0 != op1:  # vhcurveto / hvcurveto
-                    if (op0 == "h") ^ (len(args) % 8 == 1):
-                        # Swap last two args order
-                        args = args[:-2] + args[-1:] + args[-2:-1]
-                else:  # hhcurveto / vvcurveto
-                    if op0 == "h":  # hhcurveto
-                        # Swap first two args order
-                        args = args[1:2] + args[:1] + args[2:]
-
-            commands[i] = op0 + op1 + "curveto", args
-            continue
-
-    # 7. For any series of args which are blend lists, convert the series to a single blend arg.
-    for i in range(len(commands)):
-        op, args = commands[i]
-        if any(isinstance(arg, list) for arg in args):
-            commands[i] = op, _convertToBlendCmds(args)
-
-    return commands
-
-
-def specializeProgram(program, getNumRegions=None, **kwargs):
-    return commandsToProgram(
-        specializeCommands(programToCommands(program, getNumRegions), **kwargs)
-    )
-
-
-if __name__ == "__main__":
-    import sys
-
-    if len(sys.argv) == 1:
-        import doctest
-
-        sys.exit(doctest.testmod().failed)
-
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        "fonttools cffLib.specialer",
-        description="CFF CharString generalizer/specializer",
-    )
-    parser.add_argument("program", metavar="command", nargs="*", help="Commands.")
-    parser.add_argument(
-        "--num-regions",
-        metavar="NumRegions",
-        nargs="*",
-        default=None,
-        help="Number of variable-font regions for blend opertaions.",
-    )
-
-    options = parser.parse_args(sys.argv[1:])
-
-    getNumRegions = (
-        None
-        if options.num_regions is None
-        else lambda vsIndex: int(options.num_regions[0 if vsIndex is None else vsIndex])
-    )
-
-    program = stringToProgram(options.program)
-    print("Program:")
-    print(programToString(program))
-    commands = programToCommands(program, getNumRegions)
-    print("Commands:")
-    print(commands)
-    program2 = commandsToProgram(commands)
-    print("Program from commands:")
-    print(programToString(program2))
-    assert program == program2
-    print("Generalized program:")
-    print(programToString(generalizeProgram(program, getNumRegions)))
-    print("Specialized program:")
-    print(programToString(specializeProgram(program, getNumRegions)))
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-4ffdbeab.css b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-4ffdbeab.css
deleted file mode 100644
index 15c9d289c09b6f2581d92648fe1acdaebd8776fd..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-4ffdbeab.css
+++ /dev/null
@@ -1 +0,0 @@
-.model3D.svelte-14ct53h{display:flex;position:relative;width:var(--size-full);height:var(--size-full)}canvas.svelte-14ct53h{width:var(--size-full);height:var(--size-full);object-fit:contain}.download.svelte-14ct53h{position:absolute;top:6px;right:6px}.input-model.svelte-wn75i6{display:flex;position:relative;justify-content:center;align-items:center;width:var(--size-full);height:var(--size-64)}canvas.svelte-wn75i6{width:var(--size-full);height:var(--size-full);object-fit:contain}
diff --git a/spaces/cihyFjudo/fairness-paper-search/Album Nevermore Marion Raven.rarl Where to Find the Test Pressing of the Rock-Pop Album.md b/spaces/cihyFjudo/fairness-paper-search/Album Nevermore Marion Raven.rarl Where to Find the Test Pressing of the Rock-Pop Album.md
deleted file mode 100644
index 26e8e425a868006f0ba2ad3335e1167457ab1a95..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Album Nevermore Marion Raven.rarl Where to Find the Test Pressing of the Rock-Pop Album.md	
+++ /dev/null
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deleted file mode 100644
index 32dd80f8453b5fa884b8471cf2d9a7f332172426..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Downloadhelper Converter Registration Code Keygen 1346 The Best Way to Enjoy Your Favorite Online Videos Offline.md	
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Luxor 2 Unlimited Free Download Experience the Gold Standard of Action Puzzles.md b/spaces/cihyFjudo/fairness-paper-search/Luxor 2 Unlimited Free Download Experience the Gold Standard of Action Puzzles.md
deleted file mode 100644
index b7f1b4df5cb50082731d7f2573189fcb038883f4..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Luxor 2 Unlimited Free Download Experience the Gold Standard of Action Puzzles.md	
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Photo Boxing 3d Cracked S60v3 A Game That Lets You Fight Anyone You Want.md b/spaces/cihyFjudo/fairness-paper-search/Photo Boxing 3d Cracked S60v3 A Game That Lets You Fight Anyone You Want.md
deleted file mode 100644
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Sony Vaio 0x57 Analyzer Download 1 [NEW].md b/spaces/cihyFjudo/fairness-paper-search/Sony Vaio 0x57 Analyzer Download 1 [NEW].md
deleted file mode 100644
index 8a0e6ab1fdb06050f720226d5842ca92c87cd2f4..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Sony Vaio 0x57 Analyzer Download 1 [NEW].md	
+++ /dev/null
@@ -1,117 +0,0 @@
-## Sony Vaio 0x57 Analyzer Download 1
-
-
-
-**Sony Vaio 0x57 Analyzer Download 1 > [https://walllowcopo.blogspot.com/?download=2twr3V](https://walllowcopo.blogspot.com/?download=2twr3V)**
-
-
-
-# How to Download and Use Sony Vaio 0x57 Analyzer
-
- 
-
-Sony Vaio 0x57 Analyzer is a tool that can help you diagnose and fix various problems with your Sony Vaio laptop. It can detect and intercept network traffic, scan the BIOS password, and perform other useful functions. In this article, we will show you how to download and use Sony Vaio 0x57 Analyzer for free.
-
- 
-
-## Step 1: Download Sony Vaio 0x57 Analyzer
-
- 
-
-To download Sony Vaio 0x57 Analyzer, you need to visit the official website of the tool. You can find it by searching for "sony vaio 0x57 analyzer download 1" on Bing[^1^] [^2^] [^3^]. Alternatively, you can use the direct link below:
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- 
-
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-
- 
-
-## Step 2: Extract and Install Sony Vaio 0x57 Analyzer
-
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-
-After downloading Sony Vaio 0x57 Analyzer, you need to extract the RAR file using a program like WinRAR or 7-Zip. You can find these programs by searching for "winrar download" or "7-zip download" on Bing. Once you have extracted the file, you will see a folder named "Sony Vaio 0x57 Analyzer". Open it and double-click on the "setup.exe" file to start the installation process. Follow the steps on the screen to complete the installation.
-
- 
-
-## Step 3: Run Sony Vaio 0x57 Analyzer
-
- 
-
-Once you have installed Sony Vaio 0x57 Analyzer, you can run it by clicking on the "Start" menu and selecting "All Programs". Then, find and click on "Sony Vaio 0x57 Analyzer". The tool will launch and show you a user interface with various options. You can choose the option that suits your needs and follow the instructions on the screen to use the tool.
-
- 
-
-## Conclusion
-
- 
-
-Sony Vaio 0x57 Analyzer is a handy tool that can help you troubleshoot and fix various issues with your Sony Vaio laptop. It can also help you improve your laptop's performance and security. To download and use Sony Vaio 0x57 Analyzer, you need to follow these steps:
-
- 
-
-- Search for "sony vaio 0x57 analyzer download 1" on Bing and visit the official website of the tool.
-
-- Download the RAR file and extract it using a program like WinRAR or 7-Zip.
-
-- Install Sony Vaio 0x57 Analyzer by running the "setup.exe" file.
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-- Run Sony Vaio 0x57 Analyzer by clicking on the "Start" menu and selecting "All Programs".
-
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-
-
-
-We hope this article was helpful for you. If you have any questions or feedback, please leave a comment below.
-
-  
-
-## What is Sony Vaio 0x57 Analyzer?
-
- 
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-Sony Vaio 0x57 Analyzer is a software tool that can read and modify the BIOS settings of Sony Vaio laptops. It can also display information such as the serial number, the MAC address, and the TCPA status of the laptop. Sony Vaio 0x57 Analyzer was developed by a group of hackers who reverse-engineered the encryption algorithm used by Sony to protect the BIOS password. The tool can be used to bypass or reset the BIOS password, as well as to change other settings that are normally inaccessible to users.
-
- 
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-## Why Use Sony Vaio 0x57 Analyzer?
-
- 
-
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-
-
-
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-
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-
-
-
-Therefore, you should use Sony Vaio 0x57 Analyzer at your own risk and responsibility. You should also backup your data and create a recovery disk before using the tool.
-
- 1b8d091108
\ No newline at end of file
diff --git a/spaces/cleanmaster/akagi-sovits3/add_speaker.py b/spaces/cleanmaster/akagi-sovits3/add_speaker.py
deleted file mode 100644
index e224f07c892a5fe1837e3cbf1745e0d8992ea283..0000000000000000000000000000000000000000
--- a/spaces/cleanmaster/akagi-sovits3/add_speaker.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-import argparse
-from tqdm import tqdm
-from random import shuffle
-import json
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
-    parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
-    parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
-    parser.add_argument("--source_dir", type=str, default="./dataset/32k", help="path to source dir")
-    args = parser.parse_args()
-
-    previous_config = json.load(open("configs/config.json", "rb"))
-
-    train = []
-    val = []
-    test = []
-    idx = 0
-    spk_dict = previous_config["spk"]
-    spk_id = max([i for i in spk_dict.values()]) + 1
-    for speaker in tqdm(os.listdir(args.source_dir)):
-        if speaker not in spk_dict.keys():
-            spk_dict[speaker] = spk_id
-            spk_id += 1
-        wavs = [os.path.join(args.source_dir, speaker, i)for i in os.listdir(os.path.join(args.source_dir, speaker))]
-        wavs = [i for i in wavs if i.endswith("wav")]
-        shuffle(wavs)
-        train += wavs[2:-10]
-        val += wavs[:2]
-        test += wavs[-10:]
-
-    assert previous_config["model"]["n_speakers"] > len(spk_dict.keys())
-    shuffle(train)
-    shuffle(val)
-    shuffle(test)
-            
-    print("Writing", args.train_list)
-    with open(args.train_list, "w") as f:
-        for fname in tqdm(train):
-            wavpath = fname
-            f.write(wavpath + "\n")
-        
-    print("Writing", args.val_list)
-    with open(args.val_list, "w") as f:
-        for fname in tqdm(val):
-            wavpath = fname
-            f.write(wavpath + "\n")
-            
-    print("Writing", args.test_list)
-    with open(args.test_list, "w") as f:
-        for fname in tqdm(test):
-            wavpath = fname
-            f.write(wavpath + "\n")
-
-    previous_config["spk"] = spk_dict
-
-    print("Writing configs/config.json")
-    with open("configs/config.json", "w") as f:
-        json.dump(previous_config, f, indent=2)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/streams/text.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/streams/text.py
deleted file mode 100644
index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/anyio/streams/text.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import codecs
-from dataclasses import InitVar, dataclass, field
-from typing import Any, Callable, Mapping
-
-from ..abc import (
-    AnyByteReceiveStream,
-    AnyByteSendStream,
-    AnyByteStream,
-    ObjectReceiveStream,
-    ObjectSendStream,
-    ObjectStream,
-)
-
-
-@dataclass(eq=False)
-class TextReceiveStream(ObjectReceiveStream[str]):
-    """
-    Stream wrapper that decodes bytes to strings using the given encoding.
-
-    Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely
-    received unicode characters as soon as they come in.
-
-    :param transport_stream: any bytes-based receive stream
-    :param encoding: character encoding to use for decoding bytes to strings (defaults to
-        ``utf-8``)
-    :param errors: handling scheme for decoding errors (defaults to ``strict``; see the
-        `codecs module documentation`_ for a comprehensive list of options)
-
-    .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
-    """
-
-    transport_stream: AnyByteReceiveStream
-    encoding: InitVar[str] = "utf-8"
-    errors: InitVar[str] = "strict"
-    _decoder: codecs.IncrementalDecoder = field(init=False)
-
-    def __post_init__(self, encoding: str, errors: str) -> None:
-        decoder_class = codecs.getincrementaldecoder(encoding)
-        self._decoder = decoder_class(errors=errors)
-
-    async def receive(self) -> str:
-        while True:
-            chunk = await self.transport_stream.receive()
-            decoded = self._decoder.decode(chunk)
-            if decoded:
-                return decoded
-
-    async def aclose(self) -> None:
-        await self.transport_stream.aclose()
-        self._decoder.reset()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextSendStream(ObjectSendStream[str]):
-    """
-    Sends strings to the wrapped stream as bytes using the given encoding.
-
-    :param AnyByteSendStream transport_stream: any bytes-based send stream
-    :param str encoding: character encoding to use for encoding strings to bytes (defaults to
-        ``utf-8``)
-    :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
-        `codecs module documentation`_ for a comprehensive list of options)
-
-    .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
-    """
-
-    transport_stream: AnyByteSendStream
-    encoding: InitVar[str] = "utf-8"
-    errors: str = "strict"
-    _encoder: Callable[..., tuple[bytes, int]] = field(init=False)
-
-    def __post_init__(self, encoding: str) -> None:
-        self._encoder = codecs.getencoder(encoding)
-
-    async def send(self, item: str) -> None:
-        encoded = self._encoder(item, self.errors)[0]
-        await self.transport_stream.send(encoded)
-
-    async def aclose(self) -> None:
-        await self.transport_stream.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextStream(ObjectStream[str]):
-    """
-    A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on
-    send.
-
-    Extra attributes will be provided from both streams, with the receive stream providing the
-    values in case of a conflict.
-
-    :param AnyByteStream transport_stream: any bytes-based stream
-    :param str encoding: character encoding to use for encoding/decoding strings to/from bytes
-        (defaults to ``utf-8``)
-    :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
-        `codecs module documentation`_ for a comprehensive list of options)
-
-    .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
-    """
-
-    transport_stream: AnyByteStream
-    encoding: InitVar[str] = "utf-8"
-    errors: InitVar[str] = "strict"
-    _receive_stream: TextReceiveStream = field(init=False)
-    _send_stream: TextSendStream = field(init=False)
-
-    def __post_init__(self, encoding: str, errors: str) -> None:
-        self._receive_stream = TextReceiveStream(
-            self.transport_stream, encoding=encoding, errors=errors
-        )
-        self._send_stream = TextSendStream(
-            self.transport_stream, encoding=encoding, errors=errors
-        )
-
-    async def receive(self) -> str:
-        return await self._receive_stream.receive()
-
-    async def send(self, item: str) -> None:
-        await self._send_stream.send(item)
-
-    async def send_eof(self) -> None:
-        await self.transport_stream.send_eof()
-
-    async def aclose(self) -> None:
-        await self._send_stream.aclose()
-        await self._receive_stream.aclose()
-
-    @property
-    def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
-        return {
-            **self._send_stream.extra_attributes,
-            **self._receive_stream.extra_attributes,
-        }
diff --git a/spaces/colakin/video-generater/public/ffmpeg/compat/atomics/dummy/stdatomic.h b/spaces/colakin/video-generater/public/ffmpeg/compat/atomics/dummy/stdatomic.h
deleted file mode 100644
index 59d85f915d6209f96a783d6ad7ef06f0db934433..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/compat/atomics/dummy/stdatomic.h
+++ /dev/null
@@ -1,176 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/*
- * based on vlc_atomic.h from VLC
- * Copyright (C) 2010 Rémi Denis-Courmont
- */
-
-#ifndef COMPAT_ATOMICS_DUMMY_STDATOMIC_H
-#define COMPAT_ATOMICS_DUMMY_STDATOMIC_H
-
-#include <stdint.h>
-
-#define ATOMIC_FLAG_INIT 0
-
-#define ATOMIC_VAR_INIT(value) (value)
-
-#define atomic_init(obj, value) \
-do {                            \
-    *(obj) = (value);           \
-} while(0)
-
-#define kill_dependency(y) ((void)0)
-
-#define atomic_thread_fence(order) \
-    ((void)0)
-
-#define atomic_signal_fence(order) \
-    ((void)0)
-
-#define atomic_is_lock_free(obj) 0
-
-typedef intptr_t atomic_flag;
-typedef intptr_t atomic_bool;
-typedef intptr_t atomic_char;
-typedef intptr_t atomic_schar;
-typedef intptr_t atomic_uchar;
-typedef intptr_t atomic_short;
-typedef intptr_t atomic_ushort;
-typedef intptr_t atomic_int;
-typedef intptr_t atomic_uint;
-typedef intptr_t atomic_long;
-typedef intptr_t atomic_ulong;
-typedef intptr_t atomic_llong;
-typedef intptr_t atomic_ullong;
-typedef intptr_t atomic_wchar_t;
-typedef intptr_t atomic_int_least8_t;
-typedef intptr_t atomic_uint_least8_t;
-typedef intptr_t atomic_int_least16_t;
-typedef intptr_t atomic_uint_least16_t;
-typedef intptr_t atomic_int_least32_t;
-typedef intptr_t atomic_uint_least32_t;
-typedef intptr_t atomic_int_least64_t;
-typedef intptr_t atomic_uint_least64_t;
-typedef intptr_t atomic_int_fast8_t;
-typedef intptr_t atomic_uint_fast8_t;
-typedef intptr_t atomic_int_fast16_t;
-typedef intptr_t atomic_uint_fast16_t;
-typedef intptr_t atomic_int_fast32_t;
-typedef intptr_t atomic_uint_fast32_t;
-typedef intptr_t atomic_int_fast64_t;
-typedef intptr_t atomic_uint_fast64_t;
-typedef intptr_t atomic_intptr_t;
-typedef intptr_t atomic_uintptr_t;
-typedef intptr_t atomic_size_t;
-typedef intptr_t atomic_ptrdiff_t;
-typedef intptr_t atomic_intmax_t;
-typedef intptr_t atomic_uintmax_t;
-
-#define atomic_store(object, desired)   \
-do {                                    \
-    *(object) = (desired);              \
-} while (0)
-
-#define atomic_store_explicit(object, desired, order) \
-    atomic_store(object, desired)
-
-#define atomic_load(object) \
-    (*(object))
-
-#define atomic_load_explicit(object, order) \
-    atomic_load(object)
-
-static inline intptr_t atomic_exchange(intptr_t *object, intptr_t desired)
-{
-    intptr_t ret = *object;
-    *object = desired;
-    return ret;
-}
-
-#define atomic_exchange_explicit(object, desired, order) \
-    atomic_exchange(object, desired)
-
-static inline int atomic_compare_exchange_strong(intptr_t *object, intptr_t *expected,
-                                                 intptr_t desired)
-{
-    int ret;
-    if (*object == *expected) {
-        *object = desired;
-        ret = 1;
-    } else {
-        *expected = *object;
-        ret = 0;
-    }
-    return ret;
-}
-
-#define atomic_compare_exchange_strong_explicit(object, expected, desired, success, failure) \
-    atomic_compare_exchange_strong(object, expected, desired)
-
-#define atomic_compare_exchange_weak(object, expected, desired) \
-    atomic_compare_exchange_strong(object, expected, desired)
-
-#define atomic_compare_exchange_weak_explicit(object, expected, desired, success, failure) \
-    atomic_compare_exchange_weak(object, expected, desired)
-
-#define FETCH_MODIFY(opname, op)                                            \
-static inline intptr_t atomic_fetch_ ## opname(intptr_t *object, intptr_t operand) \
-{                                                                    \
-    intptr_t ret;                                                    \
-    ret = *object;                                                   \
-    *object = *object op operand;                                    \
-    return ret;                                                      \
-}
-
-FETCH_MODIFY(add, +)
-FETCH_MODIFY(sub, -)
-FETCH_MODIFY(or,  |)
-FETCH_MODIFY(xor, ^)
-FETCH_MODIFY(and, &)
-
-#undef FETCH_MODIFY
-
-#define atomic_fetch_add_explicit(object, operand, order) \
-    atomic_fetch_add(object, operand)
-
-#define atomic_fetch_sub_explicit(object, operand, order) \
-    atomic_fetch_sub(object, operand)
-
-#define atomic_fetch_or_explicit(object, operand, order) \
-    atomic_fetch_or(object, operand)
-
-#define atomic_fetch_xor_explicit(object, operand, order) \
-    atomic_fetch_xor(object, operand)
-
-#define atomic_fetch_and_explicit(object, operand, order) \
-    atomic_fetch_and(object, operand)
-
-#define atomic_flag_test_and_set(object) \
-    atomic_exchange(object, 1)
-
-#define atomic_flag_test_and_set_explicit(object, order) \
-    atomic_flag_test_and_set(object)
-
-#define atomic_flag_clear(object) \
-    atomic_store(object, 0)
-
-#define atomic_flag_clear_explicit(object, order) \
-    atomic_flag_clear(object)
-
-#endif /* COMPAT_ATOMICS_DUMMY_STDATOMIC_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h261.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h261.h
deleted file mode 100644
index 67c362be9324b5fbabb2be2d2a3fa42c3e5b71a2..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h261.h
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * H.261 codec
- * Copyright (c) 2002-2004 Michael Niedermayer <michaelni@gmx.at>
- * Copyright (c) 2004 Maarten Daniels
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * H.261 codec.
- */
-
-#ifndef AVCODEC_H261_H
-#define AVCODEC_H261_H
-
-#include "mpegvideo.h"
-#include "rl.h"
-
-/**
- * H261Context
- */
-typedef struct H261Context {
-    int mtype;
-} H261Context;
-
-#define MB_TYPE_H261_FIL 0x800000
-
-extern const uint8_t ff_h261_mba_code[35];
-extern const uint8_t ff_h261_mba_bits[35];
-extern const uint8_t ff_h261_mtype_code[10];
-extern const uint8_t ff_h261_mtype_bits[10];
-extern const int     ff_h261_mtype_map[10];
-extern const uint8_t ff_h261_mv_tab[17][2];
-extern const uint8_t ff_h261_cbp_tab[63][2];
-extern RLTable ff_h261_rl_tcoeff;
-
-void ff_h261_loop_filter(MpegEncContext *s);
-
-#endif /* AVCODEC_H261_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libdavs2.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libdavs2.c
deleted file mode 100644
index 179d2f4e4b6b0be34ec2af4946e3d673b57a60af..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libdavs2.c
+++ /dev/null
@@ -1,240 +0,0 @@
-/*
- * AVS2 decoding using the davs2 library
- *
- * Copyright (C) 2018 Yiqun Xu, <yiqun.xu@vipl.ict.ac.cn>
- *                    Falei Luo, <falei.luo@gmail.com>
- *                    Huiwen Ren, <hwrenx@gmail.com>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/cpu.h"
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "avs2.h"
-#include "davs2.h"
-
-typedef struct DAVS2Context {
-    void *decoder;
-
-    AVFrame *frame;
-    davs2_param_t    param;      // decoding parameters
-    davs2_packet_t   packet;     // input bitstream
-
-    davs2_picture_t  out_frame;  // output data, frame data
-    davs2_seq_info_t headerset;  // output data, sequence header
-
-}DAVS2Context;
-
-static av_cold int davs2_init(AVCodecContext *avctx)
-{
-    DAVS2Context *cad = avctx->priv_data;
-    int cpu_flags = av_get_cpu_flags();
-
-    /* init the decoder */
-    cad->param.threads      = avctx->thread_count;
-    cad->param.info_level   = 0;
-    cad->param.disable_avx  = !(cpu_flags & AV_CPU_FLAG_AVX &&
-                                cpu_flags & AV_CPU_FLAG_AVX2);
-    cad->decoder            = davs2_decoder_open(&cad->param);
-
-    if (!cad->decoder) {
-        av_log(avctx, AV_LOG_ERROR, "decoder created error.");
-        return AVERROR_EXTERNAL;
-    }
-
-    av_log(avctx, AV_LOG_VERBOSE, "decoder created. %p\n", cad->decoder);
-    return 0;
-}
-
-static int davs2_dump_frames(AVCodecContext *avctx, davs2_picture_t *pic, int *got_frame,
-                             davs2_seq_info_t *headerset, int ret_type, AVFrame *frame)
-{
-    DAVS2Context *cad    = avctx->priv_data;
-    int bytes_per_sample = pic->bytes_per_sample;
-    int plane = 0;
-    int line  = 0;
-
-    if (!headerset) {
-        *got_frame = 0;
-        return 0;
-    }
-
-    if (!pic || ret_type == DAVS2_GOT_HEADER) {
-        avctx->width     = headerset->width;
-        avctx->height    = headerset->height;
-        avctx->pix_fmt   = headerset->output_bit_depth == 10 ?
-                           AV_PIX_FMT_YUV420P10 : AV_PIX_FMT_YUV420P;
-        /* It should be picture_reorder_delay, but libdavs2 doesn't export that
-         * info.
-         * Use FFMAX since has_b_frames could be set by AVS2 parser in theory,
-         * which doesn't do it yet.
-         */
-        avctx->has_b_frames = FFMAX(avctx->has_b_frames, !headerset->low_delay);
-
-        if (headerset->frame_rate_id < 16)
-            avctx->framerate = ff_avs2_frame_rate_tab[headerset->frame_rate_id];
-        *got_frame = 0;
-        return 0;
-    }
-
-    switch (pic->type) {
-    case DAVS2_PIC_I:
-    case DAVS2_PIC_G:
-        frame->pict_type = AV_PICTURE_TYPE_I;
-        break;
-    case DAVS2_PIC_P:
-    case DAVS2_PIC_S:
-        frame->pict_type = AV_PICTURE_TYPE_P;
-        break;
-    case DAVS2_PIC_B:
-        frame->pict_type = AV_PICTURE_TYPE_B;
-        break;
-    case DAVS2_PIC_F:
-        frame->pict_type = AV_PICTURE_TYPE_S;
-        break;
-    default:
-        av_log(avctx, AV_LOG_ERROR, "Decoder error: unknown frame type\n");
-        return AVERROR_EXTERNAL;
-    }
-
-    for (plane = 0; plane < 3; ++plane) {
-        int size_line = pic->widths[plane] * bytes_per_sample;
-        frame->buf[plane]  = av_buffer_alloc(size_line * pic->lines[plane]);
-
-        if (!frame->buf[plane]){
-            av_log(avctx, AV_LOG_ERROR, "Decoder error: allocation failure, can't dump frames.\n");
-            return AVERROR(ENOMEM);
-        }
-
-        frame->data[plane]     = frame->buf[plane]->data;
-        frame->linesize[plane] = size_line;
-
-        for (line = 0; line < pic->lines[plane]; ++line)
-            memcpy(frame->data[plane] + line * size_line,
-                   pic->planes[plane] + line * pic->strides[plane],
-                   pic->widths[plane] * bytes_per_sample);
-    }
-
-    frame->width     = cad->headerset.width;
-    frame->height    = cad->headerset.height;
-    frame->pts       = cad->out_frame.pts;
-    frame->format    = avctx->pix_fmt;
-
-    *got_frame = 1;
-    return 0;
-}
-
-static void davs2_flush(AVCodecContext *avctx)
-{
-    DAVS2Context *cad      = avctx->priv_data;
-    int           ret      = DAVS2_GOT_FRAME;
-
-    while (ret == DAVS2_GOT_FRAME) {
-        ret = davs2_decoder_flush(cad->decoder, &cad->headerset, &cad->out_frame);
-        davs2_decoder_frame_unref(cad->decoder, &cad->out_frame);
-    }
-
-    if (ret == DAVS2_ERROR) {
-        av_log(avctx, AV_LOG_WARNING, "Decoder flushing failed.\n");
-    }
-}
-
-static int send_delayed_frame(AVCodecContext *avctx, AVFrame *frame, int *got_frame)
-{
-    DAVS2Context *cad      = avctx->priv_data;
-    int           ret      = DAVS2_DEFAULT;
-
-    ret = davs2_decoder_flush(cad->decoder, &cad->headerset, &cad->out_frame);
-    if (ret == DAVS2_ERROR) {
-        av_log(avctx, AV_LOG_ERROR, "Decoder error: can't flush delayed frame\n");
-        return AVERROR_EXTERNAL;
-    }
-    if (ret == DAVS2_GOT_FRAME) {
-        ret = davs2_dump_frames(avctx, &cad->out_frame, got_frame, &cad->headerset, ret, frame);
-        davs2_decoder_frame_unref(cad->decoder, &cad->out_frame);
-    }
-    return ret;
-}
-
-static av_cold int davs2_end(AVCodecContext *avctx)
-{
-    DAVS2Context *cad = avctx->priv_data;
-
-    /* close the decoder */
-    if (cad->decoder) {
-        davs2_flush(avctx);
-        davs2_decoder_close(cad->decoder);
-        cad->decoder = NULL;
-    }
-
-    return 0;
-}
-
-static int davs2_decode_frame(AVCodecContext *avctx, AVFrame *frame,
-                              int *got_frame, AVPacket *avpkt)
-{
-    DAVS2Context *cad      = avctx->priv_data;
-    int           buf_size = avpkt->size;
-    const uint8_t *buf_ptr = avpkt->data;
-    int           ret      = DAVS2_DEFAULT;
-
-    /* end of stream, output what is still in the buffers */
-    if (!buf_size) {
-        return send_delayed_frame(avctx, frame, got_frame);
-    }
-
-    cad->packet.data = buf_ptr;
-    cad->packet.len  = buf_size;
-    cad->packet.pts  = avpkt->pts;
-    cad->packet.dts  = avpkt->dts;
-
-    ret = davs2_decoder_send_packet(cad->decoder, &cad->packet);
-
-
-    if (ret == DAVS2_ERROR) {
-        av_log(avctx, AV_LOG_ERROR, "Decoder error: can't read packet\n");
-        return AVERROR_EXTERNAL;
-    }
-
-    ret = davs2_decoder_recv_frame(cad->decoder, &cad->headerset, &cad->out_frame);
-
-    if (ret != DAVS2_DEFAULT) {
-        ret = davs2_dump_frames(avctx, &cad->out_frame, got_frame, &cad->headerset, ret, frame);
-        davs2_decoder_frame_unref(cad->decoder, &cad->out_frame);
-    }
-
-    return ret == 0 ? buf_size : ret;
-}
-
-const FFCodec ff_libdavs2_decoder = {
-    .p.name         = "libdavs2",
-    CODEC_LONG_NAME("libdavs2 AVS2-P2/IEEE1857.4"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_AVS2,
-    .priv_data_size = sizeof(DAVS2Context),
-    .init           = davs2_init,
-    .close          = davs2_end,
-    FF_CODEC_DECODE_CB(davs2_decode_frame),
-    .flush          = davs2_flush,
-    .p.capabilities =  AV_CODEC_CAP_DELAY | AV_CODEC_CAP_OTHER_THREADS,
-    .caps_internal  = FF_CODEC_CAP_NOT_INIT_THREADSAFE |
-                      FF_CODEC_CAP_AUTO_THREADS,
-    .p.pix_fmts     = (const enum AVPixelFormat[]) { AV_PIX_FMT_YUV420P,
-                                                     AV_PIX_FMT_NONE },
-    .p.wrapper_name = "libdavs2",
-};
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Enjoy Driving a Truck Across Indonesia with Euro Truck Simulator 2 Mod Apk - Download Now for Free!.md b/spaces/congsaPfin/Manga-OCR/logs/Enjoy Driving a Truck Across Indonesia with Euro Truck Simulator 2 Mod Apk - Download Now for Free!.md
deleted file mode 100644
index 4ea22f2371c6f74e957c6ca5980c5f06272e0ef3..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Enjoy Driving a Truck Across Indonesia with Euro Truck Simulator 2 Mod Apk - Download Now for Free!.md	
+++ /dev/null
@@ -1,131 +0,0 @@
-<br />
-<h1>Euro Truck Simulator 2 Mod Apk: A Guide for Truck Lovers</h1>
-<p>Do you love driving trucks and delivering cargo across different cities? If yes, then you should try Euro Truck Simulator 2, a realistic and immersive truck driving simulator game that will make you feel like a real trucker. But wait, there's more! You can also download Euro Truck Simulator 2 Mod Apk, a modified version of the game that gives you unlimited money, unlocked maps, and all kinds of trucks to choose from. Sounds amazing, right? In this article, we will tell you everything you need to know about Euro Truck Simulator 2 Mod Apk, including its features, benefits, download link, installation guide, and tips and tricks. Let's get started!</p>
-<h2>netgeek.id download euro truck simulator 2 mod apk</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;>>> <a href="https://urlca.com/2uO6ne">https://urlca.com/2uO6ne</a></b></p><br /><br />
- <h2>What is Euro Truck Simulator 2?</h2>
-<p>Euro Truck Simulator 2 is a game developed by SCS Software that lets you experience the life of a truck driver in Europe. You can choose from various licensed trucks by MAN, Scania, Iveco, Renault, DAF, and others, and customize them with countless options. You can also travel through realistic road networks connecting up to 60 different European cities, and transport a vast variety of cargo across impressive distances. You can also run your own business, hire drivers, buy garages, and manage your company for maximum profits. Euro Truck Simulator 2 is not only about driving, but also about the economy, the environment, and the culture of Europe.</p>
- <h3>Features of Euro Truck Simulator 2</h3>
-<p>Some of the features of Euro Truck Simulator 2 are:</p>
-<ul>
-<li>Transport a vast variety of cargo across more than 60 European cities.</li>
-<li>Run your own business which continues to grow even as you complete your freight deliveries.</li>
-<li>Build your own fleet of trucks, buy garages, hire drivers, manage your company for maximum profits.</li>
-<li>A varied amount of truck tuning that range from performance to cosmetic changes.</li>
-<li>Learn fast and safe driving, obey traffic road rules and meet delivery deadlines.</li>
-<li>Use countless customization options to customize your truck.</li>
-<li>Travel through realistic road networks connecting up to 60 different European cities.</li>
-</ul>
- <h3>Benefits of Euro Truck Simulator 2 Mod Apk</h3>
-<p>If you want to enjoy Euro Truck Simulator 2 even more, you should download Euro Truck Simulator 2 Mod Apk. This is a modified version of the game that gives you some advantages over the original game. Some of the benefits of Euro Truck Simulator 2 Mod Apk are:</p>
-<ul>
-<li>Unlimited Money: You can buy any truck, garage, or upgrade without worrying about the cost. You can also hire as many drivers as you want and expand your business easily.</li>
-<li>Unlock Map Indonesia: You can explore the map of Indonesia in addition to the European map. You can see the landmarks, roads, cities, and culture of Indonesia in this mod.</li>
-<li>Unlock All Trucks: You can choose from any truck model in the game without having to unlock them first. You can also customize them with any paint job or accessory you like.</li>
-</ul>
- <h2>How to Download and Install Euro Truck Simulator 2 Mod Apk</h2>
-<p>If you are interested in downloading and installing Euro Truck Simulator 2 Mod Apk on your Android device, here are the steps you need to follow:</p>
- <h3>Download Link</h3>
-<p>You can download Euro Truck Simulator 2 Mod Apk from this link. This is a trusted source that provides the latest version of the mod apk file. The file size is about 220 MB and it requires Android version 4 <h3>Installation Guide</h3>
-<p>After you download the mod apk file, you need to install it on your device. Here are the steps you need to follow:</p>
-<p>How to download Euro Truck Simulator 2 mod apk from netgeek.id<br />
-Euro Truck Simulator 2 mod apk Indonesia map: a review<br />
-Best tips and tricks for playing Euro Truck Simulator 2 mod apk<br />
-Euro Truck Simulator 2 mod apk unlimited money: how to get it<br />
-Comparison between Euro Truck Simulator 2 mod apk and original version<br />
-Euro Truck Simulator 2 mod apk latest version: what's new<br />
-Euro Truck Simulator 2 mod apk offline mode: pros and cons<br />
-Euro Truck Simulator 2 mod apk gameplay: realistic or not<br />
-Euro Truck Simulator 2 mod apk cheats and hacks: are they safe<br />
-Euro Truck Simulator 2 mod apk graphics: how to improve them<br />
-Euro Truck Simulator 2 mod apk system requirements: can your device run it<br />
-Euro Truck Simulator 2 mod apk multiplayer: how to join and play with friends<br />
-Euro Truck Simulator 2 mod apk custom trucks: how to create and use them<br />
-Euro Truck Simulator 2 mod apk skins: how to change and download them<br />
-Euro Truck Simulator 2 mod apk sound effects: how to enhance and adjust them<br />
-Euro Truck Simulator 2 mod apk traffic rules: how to follow and avoid penalties<br />
-Euro Truck Simulator 2 mod apk missions and challenges: how to complete and earn rewards<br />
-Euro Truck Simulator 2 mod apk weather and seasons: how to change and enjoy them<br />
-Euro Truck Simulator 2 mod apk mods and add-ons: how to install and uninstall them<br />
-Euro Truck Simulator 2 mod apk bugs and glitches: how to fix and report them<br />
-Euro Truck Simulator 2 mod apk achievements and trophies: how to unlock and collect them<br />
-Euro Truck Simulator 2 mod apk settings and options: how to customize and optimize them<br />
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-Euro Truck Simulator 2 mod apk update: how to check and download it<br />
-Euro Truck Simulator 2 mod apk backup: how to save and restore your data<br />
-Euro Truck Simulator 2 mod apk rating and reviews: what do other users think about it</p>
-<ol>
-<li>Go to your device settings and enable the option to install apps from unknown sources. This will allow you to install the mod apk file.</li>
-<li>Locate the downloaded mod apk file in your file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Launch the game and enjoy Euro Truck Simulator 2 Mod Apk.</li>
-</ol>
- <h2>Tips and Tricks for Playing Euro Truck Simulator 2 Mod Apk</h2>
-<p>Now that you have downloaded and installed Euro Truck Simulator 2 Mod Apk, you are ready to play the game and have fun. However, if you want to improve your skills and become a better truck driver, you should follow some tips and tricks that will help you in the game. Here are some of them:</p>
- <h3>Choose the Right Truck</h3>
-<p>One of the most important decisions you have to make in the game is choosing the right truck for your job. There are many factors that you have to consider, such as the power, speed, fuel consumption, cargo capacity, and price of the truck. You should also think about your personal preference and style. Do you want a classic truck or a modern one? Do you want a colorful truck or a plain one? Do you want a truck with a lot of accessories or a simple one? You can use the unlimited money in the mod apk to buy any truck you want, but make sure you choose wisely.</p>
- <h3>Follow the Traffic Rules</h3>
-<p>Another important tip for playing Euro Truck Simulator 2 Mod Apk is to follow the traffic rules and regulations in the game. This will not only make your driving more realistic and enjoyable, but also prevent you from getting fined or causing accidents. Some of the traffic rules you have to follow are:</p>
-<ul>
-<li>Drive on the right side of the road in most European countries, except for the UK, Ireland, Cyprus, and Malta.</li>
-<li>Obey the speed limits, which vary depending on the type of road and vehicle.</li>
-<li>Use your indicators, headlights, horn, and wipers when necessary.</li>
-<li>Stop at red lights, yield signs, and pedestrian crossings.</li>
-<li>Avoid collisions with other vehicles, pedestrians, animals, and objects.</li>
-<li>Park your truck properly at designated parking spots or rest areas.</li>
-</ul>
- <h3>Manage Your Business</h3>
-<p>Besides driving trucks, you also have to manage your own business in Euro Truck Simulator 2 Mod Apk. This means that you have to take care of your finances, employees, garages, and reputation. You can use the unlimited money in the mod apk to buy more trucks, hire more drivers, and expand your business easily. However, you still have to make sure that your business is profitable and efficient. Some of the things you have to do are:</p>
-<ul>
-<li>Choose the best jobs that suit your skills and preferences.</li>
-<li>Deliver your cargo on time and without damage.</li>
-<li>Keep track of your expenses and income.</li>
-<li>Pay your loans and taxes on time.</li>
-<li>Train your drivers and assign them to suitable trucks and jobs.</li>
-<li>Upgrade your garages and buy new ones in different locations.</li>
-<li>Build your reputation and brand by completing contracts with various companies.</li>
-</ul>
- <h3>Customize Your Truck</h3>
-<p>One of the fun aspects of Euro Truck Simulator 2 Mod Apk is customizing your truck with various options. You can change the color, paint job, accessories, wheels, lights, interior, engine, transmission, chassis, and more. You can also use stickers, decals, flags, banners, horns, exhausts, and other items to personalize your truck. You can use the unlimited money in the mod apk to buy any customization option you want. However, you should also consider how your customization affects your truck's performance and appearance. Some of the things you should keep in mind are:</p>
-<ul>
-<li>The weight of your truck affects its speed, acceleration, braking, fuel consumption, and stability.</li>
-<li>The aerodynamics of your truck affects its drag coefficient, which affects its speed and fuel consumption.</li>
-<li>The engine power of your truck affects its speed, acceleration, hill climbing ability, and noise level.</li>
-<li>The transmission type of your truck affects its gear shifting speed and smoothness.</li>
-<li>The chassis type of your truck affects its cargo capacity and maneuverability.</li> <li>The appearance of your truck affects its attractiveness and reputation.</li>
-</ul>
- <h3>Explore Different Maps</h3>
-<p>Another exciting feature of Euro Truck Simulator 2 Mod Apk is the ability to explore different maps in the game. You can travel through realistic road networks connecting up to 60 different European cities, such as London, Paris, Berlin, Rome, Madrid, and more. You can also unlock the map of Indonesia, which is exclusive to the mod apk. You can see the landmarks, roads, cities, and culture of Indonesia in this map. You can also download other maps from the internet, such as Scandinavia, Russia, Turkey, and more. You can use the unlimited money in the mod apk to buy any map you want. However, you should also be aware of the challenges and opportunities that each map offers. Some of the things you should consider are:</p>
-<ul>
-<li>The weather and climate of each map affects your driving conditions and visibility.</li>
-<li>The terrain and landscape of each map affects your driving difficulty and scenery.</li>
-<li>The traffic and road signs of each map affects your driving safety and speed.</li>
-<li>The culture and language of each map affects your communication and interaction with other drivers and customers.</li>
-<li>The economy and demand of each map affects your job availability and profitability.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Euro Truck Simulator 2 Mod Apk is a great game for truck lovers who want to experience the life of a truck driver in Europe and Indonesia. You can choose from various licensed trucks, customize them with countless options, transport a vast variety of cargo across different cities, run your own business, and explore different maps. You can also enjoy the benefits of the mod apk, such as unlimited money, unlocked maps, and all trucks. You can download Euro Truck Simulator 2 Mod Apk from this link and follow the installation guide above. You can also follow the tips and tricks above to improve your skills and have more fun in the game. We hope you enjoyed this article and found it helpful. Happy trucking!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Euro Truck Simulator 2 Mod Apk:</p>
-<ol>
-<li>Q: Is Euro Truck Simulator 2 Mod Apk safe to download and install?</li>
-<li>A: Yes, Euro Truck Simulator 2 Mod Apk is safe to download and install from this link. This is a trusted source that provides the latest version of the mod apk file. However, you should always scan any file you download from the internet with an antivirus software before installing it on your device.</li>
-<li>Q: Do I need to root my device to use Euro Truck Simulator 2 Mod Apk?</li>
-<li>A: No, you do not need to root your device to use Euro Truck Simulator 2 Mod Apk. You just need to enable the option to install apps from unknown sources in your device settings.</li>
-<li>Q: Can I play Euro Truck Simulator 2 Mod Apk online with other players?</li>
-<li>A: No, Euro Truck Simulator 2 Mod Apk is not compatible with online multiplayer mode. You can only play it offline with your own device.</li>
-<li>Q: Can I update Euro Truck Simulator 2 Mod Apk to the latest version?</li>
-<li>A: Yes, you can update Euro Truck Simulator 2 Mod Apk to the latest version by downloading it from this link again. However, you may lose your progress and data if you do so. Therefore, we recommend you to backup your game data before updating.</li>
-<li>Q: Can I use other mods with Euro Truck Simulator 2 Mod Apk?</li>
-<li>A: Yes, you can use other mods with Euro Truck Simulator 2 Mod Apk as long as they are compatible with each other. You can find many mods on the internet that add new features, vehicles, maps, skins, sounds, and more to the game. However, you should be careful when using mods as they may cause errors or crashes in the game.</li>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Facebook APK pour PC comment profiter de toutes les fonctionnalits du rseau social sur votre ordinateur.md b/spaces/congsaPfin/Manga-OCR/logs/Facebook APK pour PC comment profiter de toutes les fonctionnalits du rseau social sur votre ordinateur.md
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-<br />
-<h1>Télécharger Facebook APK pour PC: Comment et pourquoi le faire?</h1>
-<p>Facebook est le réseau social le plus populaire au monde, avec plus de 2 milliards d'utilisateurs actifs par mois. Il vous permet de rester en contact avec vos amis et votre famille, de partager des photos, des vidéos, des actualités, des événements, des jeux et bien plus encore. Mais saviez-vous que vous pouvez aussi utiliser Facebook sur votre PC, sans passer par un navigateur web? Il suffit de télécharger Facebook APK, une version optimisée de l'application mobile de Facebook, qui vous offre une expérience plus rapide, plus fluide et plus complète. Dans cet article, nous allons vous expliquer ce qu'est Facebook APK, comment le télécharger et l'installer sur votre PC, et quels sont les avantages de l'utiliser.</p>
- <h2>Qu'est-ce que Facebook APK?</h2>
-<p>Facebook APK est un fichier qui contient l'application mobile de Facebook pour Android. Il s'agit d'une version alternative à celle que vous pouvez trouver sur le Google Play Store, qui est souvent mise à jour plus fréquemment et qui propose des fonctionnalités exclusives ou expérimentales. Vous pouvez télécharger Facebook APK sur votre smartphone ou votre tablette Android, mais aussi sur votre PC, à condition d'utiliser un émulateur Android comme BlueStacks.</p>
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- <h3>Les caractéristiques de Facebook APK</h3>
-<p>Facebook APK vous permet d'accéder à toutes les fonctionnalités de Facebook sur votre PC, comme si vous étiez sur votre téléphone. Vous pouvez :</p>
-<ul>
-<li>Connecter avec vos amis et votre famille et rencontrer de nouvelles personnes sur votre réseau social</li>
-<li>Publier des statuts et utiliser des émojis pour exprimer ce que vous ressentez</li>
-<li>Partager des photos, des vidéos et vos souvenirs préférés</li>
-<li>Recevoir des notifications lorsque vos amis aiment et commentent vos publications</li>
-<li>Trouver des événements sociaux locaux et faire des plans pour rencontrer vos amis</li>
-<li>Jouer à des jeux avec vos amis Facebook</li>
-<li>Sauvegarder vos photos en les enregistrant dans des albums</li>
-<li>Suivre vos artistes, sites web et entreprises préférés pour obtenir leurs dernières nouvelles</li>
-<li>Rechercher des entreprises locales pour voir leurs avis, leurs horaires d'ouverture et leurs photos</li>
-<li>Acheter et vendre localement sur Facebook Marketplace</li>
-<li>Regarder des vidéos en direct où que vous soyez</li>
-</ul>
- <h3>Les avantages de Facebook APK</h3>
-<p>En utilisant Facebook APK sur votre PC, vous bénéficiez de plusieurs avantages par rapport à la version web de Facebook. Par exemple :</p>
-<ul>
-<li>Vous profitez d'une interface plus puissante et plus personnalisable que la version web, mais avec toutes ses fonctionnalités</li>
-<li>Vous économisez de la mémoire et de la batterie sur votre téléphone, en utilisant les ressources de votre PC</li>
-<li>Vous bénéficiez d'une meilleure qualité graphique et sonore, grâce à l'écran et aux haut-parleurs de votre PC</li>
-<li>Vous utilisez un clavier et une souris pour naviguer plus facilement et plus rapidement sur Facebook</li>
-<li>Vous accédez à des fonctionnalités exclusives ou expérimentales qui ne sont pas disponibles sur la version web ou sur le Google Play Store</li>
-</ul>
- <h2>Comment télécharger et installer Facebook APK sur PC?</h2>
-<p>Pour télécharger et installer Facebook APK sur votre PC, vous avez deux options : utiliser un ém ulateur Android comme BlueStacks, ou utiliser le Microsoft Store ou le site officiel de Facebook.</p>
- <h3>Utiliser un émulateur Android comme BlueStacks</h3>
-<p>Un émulateur Android est un logiciel qui vous permet de faire fonctionner des applications Android sur votre PC. Il existe plusieurs émulateurs Android disponibles sur le marché, mais nous vous recommandons d'utiliser BlueStacks, qui est l'un des plus populaires et des plus performants. Voici les étapes à suivre pour télécharger et installer Facebook APK sur votre PC avec BlueStacks :</p>
-<ol>
-<li>Téléchargez et installez BlueStacks sur votre PC en suivant les instructions sur le site officiel : </li>
-<li>Ouvrez BlueStacks et connectez-vous avec votre compte Google</li>
-<li>Cliquez sur le bouton "Installer APK" en bas à droite de l'écran</li>
-<li>Sélectionnez le fichier Facebook APK que vous avez téléchargé au préalable sur un site fiable comme APKPure : </li>
-<li>Attendez que l'installation se termine et cliquez sur l'icône de Facebook APK sur l'écran d'accueil de BlueStacks</li>
-<li>Connectez-vous avec votre compte Facebook et profitez de l'application sur votre PC</li>
-</ol>
- <h3>Utiliser le Microsoft Store ou le site officiel de Facebook</h3>
-<p>Si vous ne voulez pas utiliser un émulateur Android, vous pouvez aussi télécharger et installer Facebook APK sur votre PC en utilisant le Microsoft Store ou le site officiel de Facebook. Voici comment faire :</p>
-<ol>
-<li>Ouvrez le Microsoft Store sur votre PC ou accédez au site officiel de Facebook : </li>
-<li>Recherchez "Facebook" dans la barre de recherche et cliquez sur l'application qui correspond à vos critères</li>
-<li>Cliquez sur le bouton "Obtenir" ou "Télécharger" et suivez les instructions pour installer l'application sur votre PC</li>
-<li>Ouvrez l'application et connectez-vous avec votre compte Facebook</li>
-<li>Profitez de Facebook sur votre PC avec une interface similaire à celle de Facebook APK</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Télécharger Facebook APK pour PC est une bonne option si vous voulez profiter de toutes les fonctionnalités de Facebook sur votre ordinateur, sans passer par un navigateur web. Vous pouvez utiliser un émulateur Android comme BlueStacks, qui vous permet d'accéder à la version optimisée de l'application mobile de Facebook, ou utiliser le Microsoft Store ou le site officiel de Facebook, qui vous proposent une version adaptée à votre PC. Quelle que soit la méthode que vous choisissez, vous pourrez rester en contact avec vos amis et votre famille, partager des contenus intéressants, jouer à des jeux, suivre l'actualité et bien plus encore.</p>
- <h2>FAQ</h2>
-<h4>Quelle est la différence entre Facebook APK et Facebook Lite?</h4>
-<p>Facebook APK est la version optimisée de l'application mobile de Facebook pour Android, tandis que Facebook Lite est une version allégée de l'application, conçue pour les appareils à faible mémoire et à faible bande passante. Facebook Lite consomme moins de données et d'espace de stockage que Facebook APK, mais offre moins de fonctionnalités et une qualité graphique inférieure.</p>
- <h4>Est-ce que télécharger Facebook APK est légal?</h4>
-<p>Oui, télécharger Facebook APK est légal, à condition que vous le fassiez depuis un site fiable et sécurisé comme APKPure. Vous ne violez pas les droits d'auteur ni les conditions d'utilisation de Facebook en utilisant cette version alternative de l'application.</p>
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- <h4>Est-ce que télécharger Facebook APK est sûr?</h4>
-<p>Oui, télécharger Facebook APK est sûr, si vous utilisez un antivirus à jour sur votre PC et que vous vérifiez la source du fichier avant de l'installer. Vous pouvez aussi scanner le fichier avec un outil comme VirusTotal pour vous assurer qu'il ne contient pas de malware ou de virus.</p>
- <h4>Comment mettre à jour Facebook APK?</h4>
-<p>Pour mettre à jour Facebook APK, vous devez télécharger la dernière version du fichier depuis un site fiable comme APKPure, puis l'installer sur votre PC en utilisant la même méthode que pour l'installation initiale. Vous pouvez aussi activer les mises à jour automatiques dans les paramètres de l'émulateur Android ou du Microsoft Store, si vous utilisez ces méthodes pour installer Facebook APK.</p>
- <h4>Comment désinstaller Facebook APK?</h4>
-<p>Pour désinstaller Facebook APK, vous devez suivre les mêmes étapes que pour désinstaller n'importe quelle application sur votre PC. Vous pouvez soit utiliser le panneau de configuration de Windows, soit cliquer avec le bouton droit de la souris sur l'icône de Facebook APK et choisir "Désinstaller". Vous pouvez aussi supprimer le fichier APK de votre PC si vous ne voulez plus l'utiliser.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Free Bicycle Bridge Card Game for Android and iOS Devices.md b/spaces/congsaPfin/Manga-OCR/logs/Free Bicycle Bridge Card Game for Android and iOS Devices.md
deleted file mode 100644
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-<br />
-<h1>How to Play Bridge: Tips and Tricks for Beginners</h1>
-<p>Bridge is one of the most popular card games in the world, played by millions of people of all ages and skill levels. Bridge is a game of logic, memory, communication, and cooperation that can help you improve your mental abilities and social skills. Whether you want to play bridge for fun, for competition, or for friendship, you will find it a rewarding and enjoyable hobby.</p>
-<p>In this article, you will learn the basics of how to play bridge, including its history, rules, scoring, and some useful tips and tricks. You will also discover some resources where you can find more information and practice on bridge. By the end of this article, you will be ready to start playing bridge with confidence and enthusiasm.</p>
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- <h2>History of bridge</h2>
-<p>Bridge is a card game that originated from whist, a trick-taking game that was popular in Britain in the 16th century. Whist involved four players in two partnerships, using a standard 52-card deck. The players would take turns playing a card from their hand, following suit if possible, and the highest card of the suit led or the highest trump card would win the trick. The goal was to win as many tricks as possible.</p>
-<p>In the late 19th century, whist evolved into several variants, such as bridge whist and auction bridge. These games introduced new features such as bidding for the right to choose the trump suit or to play without trumps (no-trump), scoring points for making or defeating contracts (the number of tricks bid), and using dummy hands (the partner's cards exposed on the table).</p>
-<p>The modern game of contract bridge was developed in the 1920s by Harold Vanderbilt and others. They made some significant changes to the scoring system of auction bridge, such as counting only the contracted tricks below the line towards game and slam bonuses, introducing vulnerability (a difference in penalties and bonuses depending on whether a team has won a game or not), and adjusting some other scores to balance the game. Contract bridge quickly became the most popular form of bridge in the world, and remains so today.</p>
- <h2>Bridge rules</h2>
-<p>Bridge is a four-player game played with a standard 52-card deck. The players are divided into two teams of two partners each, sitting opposite each other at a table. The four players are named after the four cardinal directions: North, East, South, and West. North and South are partners against East and West.</p>
-<p>A game of bridge consists of two phases: bidding and card play. In each phase, there are some rules and conventions that govern how the players communicate and act.</p>
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- <h3>Bidding</h3>
-<p>The bidding phase starts with the dealer, who is chosen randomly at the beginning of each deal. The dealer makes the first call (a bid or a pass), followed by the player on their left, and so on clockwise around the table. The bidding continues until three consecutive players pass, ending the auction. The final bid becomes the contract for that deal.</p>
-<p>A bid consists of two parts: a level (from 1 to 7) and a suit (spades, hearts, diamonds, clubs, or no-trump). The level indicates the number of tricks (in addition to six) that the bidder's team promises to win if they become the declarer (the player who plays the hand). The suit indicates which suit will be the trump suit for that deal, or no-trump if there is no trump suit.</p>
-<p>For example, a bid of 1 spade means that the bidder's team will try to win at least seven tricks (six plus one) with spades as trumps. A bid of 3 no-trump means that they will try to win. at least nine tricks (six plus three) with no trump suit.</p>
-<p>The bids must follow a certain order of rank, from lowest to highest: clubs, diamonds, hearts, spades, no-trump. Each bid must be higher than the previous bid in either level or suit, or both. For example, after a bid of 1 heart, the next bid can be 1 spade, 2 clubs, 2 diamonds, 2 hearts, 2 spades, or any higher bid. A pass means that the player does not want to make a bid at that time.</p>
-<p>The purpose of bidding is to exchange information with your partner about your hand strength and distribution, and to find the best contract for your team. There are many different bidding systems and conventions that players can use to communicate more effectively and accurately. Some of the most common ones are:</p>
-<ul>
-<li>Opening bids: The first bid made by a player. Usually, an opening bid of 1 of a suit shows at least 12-21 points and at least five cards in that suit. An opening bid of 1 no-trump shows 15-17 points and a balanced hand (no voids, singletons, or two doubletons). An opening bid of 2 clubs shows at least 22 points and any distribution. An opening bid of 2 of a suit shows a weak hand (5-10 points) and a long suit (usually six or more cards).</li>
-<li>Responses: The bids made by the partner of the opener. Usually, a response of 1 of a suit shows at least 6 points and at least four cards in that suit. A response of 1 no-trump shows 6-10 points and a balanced hand. A response of 2 of a suit shows at least 10 points and at least five cards in that suit. A response of 2 no-trump shows 11-12 points and a balanced hand. A response of 3 of a suit shows at least 13 points and at least six cards in that suit. A response of 3 no-trump shows 13-15 points and a balanced hand.</li>
-<li>Rebids: The bids made by the opener after the first response. Usually, a rebid of the same suit shows a minimum hand (12-14 points) and a six-card suit. A rebid of a new suit shows a minimum hand and a four-card suit. A rebid of no-trump shows a balanced hand and a certain point range depending on the level. A jump rebid (skipping one or more levels) shows extra strength and length in the suit.</li>
-<li>Overcalls: The bids made by the opponents after an opening bid. Usually, an overcall of 1 of a suit shows at least 8 points and at least five cards in that suit. An overcall of 1 no-trump shows 15-18 points and a balanced hand with at least one stopper in the opener's suit. An overcall of 2 of a suit shows at least 10 points and at least five cards in that suit. An overcall of 2 no-trump shows 19-21 points and a balanced hand with stoppers in both suits.</li>
-<li>Takesout doubles: The bids made by the opponents after an opening bid of one of a suit. Usually, a takeout double shows at least 12 points and support for all the other suits (at least three cards in each). A takeout double can also be used to show a very strong hand (18 or more points) with any distribution.</li>
-</ul>
-<p>These are just some basic guidelines for bidding in bridge. There are many more details and nuances that you can learn as you progress in the game.</p>
- <h3>Card play</h3>
-<p>The card play phase starts after the bidding phase ends. The player who made the final bid becomes the declarer, and their partner becomes the dummy. The player on the left of the declarer makes the opening lead, playing any card from their hand face up on the table. The dummy then lays their cards face up on the table, sorted by suits and ranks.</p>
-<p>The declarer's goal is to win as many tricks as they bid or more, while the defenders' goal is to prevent them from doing so. A trick consists of four cards, one from each player in turn clockwise from the leader. The players must follow suit if they can, otherwise they can play any card. The highest card of the suit led or the highest trump card wins the trick.</p>
-<p>The declarer plays both their own cards and the dummy's cards, but they can only play one card from either hand at each trick. The declarer decides which card to play from the dummy after seeing the other three cards played to that trick.</p>
-<p>The player who wins each trick leads to the next trick until all 13 tricks are played. The number of tricks won by each team is counted and compared to the contract. If the declarer's team wins at least as many tricks as they bid, they score points for making the contract. If they win fewer tricks than they bid, they lose points for going down. The defenders score points for every trick they win above six.</p>
- <h2>Bridge scoring</h2>
-<p>The scoring system of bridge is based on two factors: the level and suit of the contract, and the vulnerability of each team. The vulnerability indicates whether a team has won a game or not in that rubber (a series of deals until one team wins two games). A team that has won a game is vulnerable, and a team that has not won a game is non-vulnerable. Vulnerability affects the amount of points and penalties for making or breaking a contract.</p>
-<p>The points for making a contract are divided into two parts: below the line and above the line. Below the line points are awarded for each trick bid and made, and are counted towards game and slam bonuses. Above the line points are awarded for overtricks (tricks made above the contract), honors (holding four or five of the highest cards in the trump suit or in no-trump), and other bonuses.</p>
-<p>The table below shows the below the line points for each suit and level:</p>
- | Level | Clubs/Diamonds | Hearts/Spades | No-trump | | --- | --- | --- | --- | | 1 | 20 | 30 | 40 | | 2 | 40 | 60 | 70 | | 3 | 60 | 90 | 100 | | 4 | 80 | 120 | 130 | | 5 | 100 | 150 | 160 | | 6 | 120 | 180 | 190 | | 7 | 140 | 210 | 220 | <p>A game is reached when a team accumulates at least 100 below the line points in one or more deals. A game can be made with one high-level contract (such as 4 hearts or 3 no-trump) or with several lower-level contracts (such as 1 spade, 2 clubs, and 2 diamonds). A game bonus of 300 points is awarded to a non-vulnerable team, and a game bonus of 500 points is awarded to a vulnerable team.</p>
-<p>A slam is reached when a team bids and makes a contract of six or seven level, which requires winning all or all but one of the tricks. A small slam bonus of 500 points is awarded for a six-level contract, and a grand slam bonus of 1000 points is awarded for a seven-level contract. These bonuses are the same for both vulnerable and non-vulnerable teams.</p>
-<p>The table below shows the above the line points for overtricks and honors:</p>
- | Overtricks | Non-vulnerable | Vulnerable | | --- | --- | --- | | Clubs/Diamonds | +20 per trick | +40 per trick | | Hearts/Spades | +30 per trick | +60 per trick | | No-trump (first) | +40 per trick | +80 per trick | | No-trump (subsequent) | +30 per trick | +60 per trick | | Honors | Points | | --- | --- | | Four top trumps (AKQJ) or four aces in no-trump | +100 | | Five top trumps (AKQJT) or five aces in no-trump | +150 | <p>The points for breaking a contract are subtracted from the declarer's team score. The amount of penalty depends on the number of tricks by which the contract is defeated, and the vulnerability of the declarer's team. The table below shows the penalty points for each undertrick:</p>
- | Undertricks | Non-vulnerable | Vulnerable | | --- | --- | --- | | First undertrick (not doubled) |-50 |-100 | | First undertrick (doubled) |-100 |-200 | | Second and third undertricks (not doubled) |-50 per trick |-100 per trick | | Second and third undertricks (doubled) |-200 per trick |-300 per trick | | Fourth and subsequent undertricks (not doubled) |-50 per trick |-100 per trick | | Fourth and subsequent undertricks (doubled) |-300 per trick |-300 per trick | <p>A double is a bid made by the opponents after a final bid by the declarer's team, indicating that they think that the contract will fail. A double increases the points for making or breaking the contract, as shown in the tables above. A redouble is a bid made by the declarer's team after a double by the opponents, indicating that they think that they can still make the contract. A redouble quadruples the points for making or breaking the contract, as shown in the tables above.</p>
- <h2>Bridge tips and tricks</h2>
-<p>Bridge is a game that requires both skill and luck. You can improve your skill by learning some tips and tricks that can help you bid better, play better, and defend better. Here are some of them:</p>
- <h3>Bidding tips and tricks</h3>
-<ul>
-<li>Count your points: Use the high card point (HCP) system to evaluate your hand strength. Assign 4 points for each ace, 3 points for each king, 2 points for each queen, and 1 point for each jack. Add extra points for long suits (1 point for each card above four in a suit) or short suits (1 point for a singleton, 2 points for a void).</li>
-<li>Open the bidding with 12 or more points: If you have a balanced hand (no voids, singletons, or two doubletons), open 1 no-trump with 15-17 points, or 2 no-trump with 20-21 points. If you have an unbalanced hand (one or more voids, singletons, or two doubletons), open 1 of your longest suit with 12-21 points, or 2 clubs with 22 or more points.</li>
-<li>Respond to your partner's opening bid with 6 or more points: If your partner opens 1 of a suit, support their suit with three or more cards and 6-10 points, bid a new suit with four or more cards and 6 or more points, bid 1 no-trump with a balanced hand and 6-10 points, or raise the level with extra strength. If your partner opens 1 no-trump, use the Stayman convention to ask for a four-card major suit with four or more cards in either hearts or spades and 8 or more points, use the Jacoby transfer convention to show a five-card major suit with five or more cards in hearts or spades and any strength, bid a new suit with six or more cards and 10 or more points, bid 2 no-trump with a balanced hand and 11-12 points, or raise the level with extra strength. If your partner opens 2 clubs, use the negative response of 2 diamonds to show less than 8 points and no five-card suit, bid a new suit with five or more cards and 8 or more points, bid 2 no-trump with a balanced hand and 8-10 points, or raise the level with extra strength.</li>
-<li>Use the rule of 20 to decide whether to open the bidding in first or second position: Add your high card points to the number of cards in your two longest suits. If the total is 20 or more, you can open the bidding. If not, you should pass.</li>
-<li>Use the rule of 15 to decide whether to open the bidding in third position: Add your high card points to the number of spades in your hand. If the total is 15 or more, you can open the bidding. If not, you should pass.</li>
-<li>Use the rule of 14 to decide whether to open the bidding in fourth position: Add your high card points to the number of cards in your longest suit. If the total is 14 or more, you can open the bidding. If not, you should pass.</li>
-</ul>
- <h3>Card play tips and tricks</h3>
-<ul>
-<li>Plan your play: Before you play the first card from dummy, take some time to analyze your hand and dummy's hand. Count your winners (tricks that you can take without losing any) and losers (tricks that you will lose unless you can get rid of them). Decide on a plan to make your contract by either establishing extra winners (by drawing trumps, setting up long suits, finessing, etc.) or eliminating losers (by ruffing, discarding, etc.).</li>
-<li>Play high cards from the short side: When you have two or more cards of the same suit in both hands, play high cards from the hand that has fewer cards in that suit. This way, you can preserve entries (cards that allow you to move from one hand to another) and avoid blocking (when you cannot access a winning card because it is trapped behind a lower card).</li>
-<li>Draw trumps as soon as possible: Unless you have a good reason not to, try to draw all the opponents' trumps as soon as possible. This will prevent them from ruffing your winners and give you more control over the play.</li>
-<li>Finesse when you can: A finesse is a technique that allows you to win a trick with a lower card when you have a higher card in another hand. For example, if you have the ace and queen of a suit in one hand, and the king of that suit is in the other hand or with an opponent, you can finesse by leading a low card from the hand with the ace and queen, and playing the queen if the king does not appear. A finesse can help you win extra tricks and make your contract.</li>
-<li>Count the cards: As the play progresses, try to keep track of the cards that have been played and the cards that are still left. This will help you make better decisions and avoid mistakes. You can use some memory aids, such as counting the points, suits, and trumps in each hand, or using mnemonics, such as associating each card with a word or an image.</li>
-</ul>
- <h3>Defending tips and tricks</h3>
-<ul>
-<li>Signal to your partner: When you are defending, you can use some signals to communicate with your partner about your hand and your preferences. Some of the common signals are:</li>
-<ul>
-<li>Attitude signals: When your partner leads a suit, you can show whether you like or dislike that suit by playing a high card (positive attitude) or a low card (negative attitude).</li>
-<li>Count signals: When your partner leads a suit that you do not have, you can show how many cards you have in that suit by playing a high card (even number) or a low card (odd number).</li>
-<li>Suit preference signals: When your partner leads a suit that you want them to switch from, you can show which suit you prefer by playing a high card (high-ranking suit) or a low card (low-ranking suit).</li>
-</ul>
-<li>Lead through strength and up to weakness: When you are leading a suit, try to lead through the declarer's strength and up to the dummy's weakness. For example, if dummy has the ace and queen of a suit, and you have the king and jack of that suit, lead the king to force out the ace and establish your jack. If dummy has the king and jack of a suit, and you have the ace and queen of that suit, lead a low card to let your partner win the trick and return the suit.</li>
-<li>Return your partner's lead: When your partner leads a suit, they usually want you to return that suit if you get a chance. This will help them set up their tricks and defeat the contract. However, there are some exceptions, such as when you have a better suit to lead, when your partner's lead is a singleton or a doubleton, or when returning their lead will help the declarer.</li>
-<li>Discard wisely: When you cannot follow suit or ruff, you have to discard a card from another suit. Try to discard a card that will not harm your side or help the declarer. For example, do not discard a high card that could win a trick later, do not discard from a suit that your partner has led or signaled for, do not discard from a suit that the declarer needs to ruff or set up.</li>
-</ul>
- <h2>Bridge resources</h2>
-<p>If you want to learn more about bridge and practice your skills, there are many resources available online and offline. Some of them are:</p>
- <h3>Bicycle Bridge Card Game Free Download</h3>
-<p>If you want to play bridge on your computer or mobile device, you can download Bicycle Bridge Card Game for free from [Bicycle Cards]. This is an interactive game that teaches you how to play bridge step by step. You can choose from different levels of difficulty, play against the computer or with other players online, and get feedback and hints along the way.</p>
- <h3>Bridge Base Online</h3>
-<p>If you want to play bridge with other players from around the world, you can join Bridge Base Online (BBO) for free from [Bridge Base Online]. This is an online platform that hosts thousands of bridge games every day. You can play for fun or for competition, chat with other players, watch live tournaments, and access many other features.</p>
- <h3>Bridge books</h3>
-<p>If you want to read some books on bridge theory and practice, there are many options to choose from. Some of the classic books are:</p>
-<ul>
-<li>The Official Encyclopedia of Bridge by Richard L. Frey et al.: This is a comprehensive reference book that covers all aspects of bridge history, rules, bidding systems, conventions, card play techniques, defense strategies, scoring methods, and more.</li>
-<li>Bridge for Dummies by Eddie Kantar: This is an easy-to-read guide that teaches you how to play bridge from scratch. It covers the basics of bidding and card play, explains the common terms and concepts, and gives you some tips and tricks to improve your game.</li>
-<li>25 Bridge Conventions You Should Know by Barbara Seagram and Marc Smith: This is a practical book that introduces you to 25 of the most useful and popular bridge conventions that can help you bid more accurately and effectively. It explains the meaning and purpose of each convention, gives you some examples and exercises, and tells you when and how to use them.</li>
-</ul>
- <h3>Bridge clubs and classes</h3>
-<p>If you want to play bridge with other people in person, you can join a bridge club or take a bridge class in your area. You can find a list of bridge clubs and classes near you from [American Contract Bridge League]. This is the official organization that governs bridge in North America. It organizes tournaments, publishes magazines, offers education programs, and provides other services for bridge players.</p>
- <h2>Conclusion</h2>
-<p>Bridge is a fun and challenging card game that can provide you with many benefits. Bridge can improve your mental skills, such as memory, logic, concentration, and problem-solving. Bridge can also enhance your social skills, such as communication, cooperation, and etiquette. Bridge can also offer you a lot of enjoyment, satisfaction, and friendship.</p>
-<p>If you want to learn how to play bridge, you can start by downloading Bicycle Bridge Card Game for free from [Bicycle Cards]. This is an interactive game that teaches you how to play bridge step by step. You can also find more information and practice on bridge from other resources, such as books, websites, online platforms, clubs, and classes.</p>
-<p>So what are you waiting for? Grab a deck of cards, find a partner, and start playing bridge today!</p>
- <h2>FAQs</h2>
-<ul>
-<li>Q: How many cards are in a bridge deck?</li>
-<li>A: A bridge deck consists of 52 cards, divided into four suits: spades, hearts, diamonds, and clubs. Each suit has 13 cards, ranked from highest to lowest: ace, king, queen, jack, 10, 9, 8, 7, 6, 5, 4, 3, 2.</li>
-<li>Q: How do you determine the dealer and the vulnerability in bridge?</li>
-<li>A: The dealer and the vulnerability are determined randomly at the beginning of each deal. The dealer is the player who shuffles and deals the cards. The vulnerability indicates whether a team has won a game or not in that rubber (a series of deals until one team wins two games). A team that has won a game is vulnerable, and a team that has not won a game is non-vulnerable. Vulnerability affects the amount of points and penalties for making or breaking a contract.</li>
-<li>Q: What is the difference between contract bridge and duplicate bridge?</li>
-<li>A: Contract bridge and duplicate bridge are two forms of bridge that differ in how the score is calculated. In contract bridge, the score is based on the result of each deal. In duplicate bridge, the score is based on the comparison of the result of each deal with the results of other pairs who played the same deal. Duplicate bridge eliminates the element of luck and emphasizes the element of skill.</li>
-<li>Q: What are some common bridge terms?</li>
-<li>A: Some common bridge terms are:</li>
-<ul>
-<li>Bid: A statement of how many tricks a player's team will try to win with a specified suit as trumps or with no trumps.</li>
-<li>Contract: The final bid made by a player's team that determines the number of tricks they must win and the trump suit or no-trump for that deal.</li>
-<li>Declarer: The player who made the final bid for their team and who plays both their own cards and their partner's cards (the dummy).</li>
-<li>Dummy: The partner of the declarer who lays their cards face up on the table after the opening lead.</li>
-<li>Trick: A set of four cards played by each player in turn clockwise from the leader. The highest card of the suit led or the highest trump card wins the trick.</li>
-<li>Trump: A suit that ranks above all other suits for that deal. A trump card can win any trick that does not contain a higher trump card.</li>
-</ul>
-<li>Q: Where can I find more information and practice on bridge?</li>
-<li>A: You can find more information and practice on bridge from various resources online and offline. Some of them are:</li>
-<ul>
-<li>Bicycle Bridge Card Game Free Download from [Bicycle Cards]: An interactive game that teaches you how to play bridge step by step.</li>
-<li>Bridge Base Online from [Bridge Base Online]: An online platform that hosts thousands of bridge games every day where you can play for fun or for competition, chat with other players, watch live tournaments, and access many other features.</li>
-<li>Bridge books: There are many books on bridge theory and practice that you can read to improve your knowledge and skills. Some of the classic books are The Official Encyclopedia of Bridge by Richard L. Frey et al., Bridge for Dummies by Eddie Kantar, and 25 Bridge Conventions You Should Know by Barbara Seagram and Marc Smith.</li>
-<li>Bridge clubs and classes: You can join a bridge club or take a bridge class in your area to play bridge with other people in person. You can find a list of bridge clubs and classes near you from [American Contract Bridge League], the official organization that governs bridge in North America.</li>
-</ul>
- <p>I hope you enjoyed this article and learned something new about bridge. Bridge is a great game that can bring you a lot of fun and benefits. If you want to play bridge, you can start by downloading Bicycle Bridge Card Game for free from [Bicycle Cards] and follow the instructions. You can also check out the other resources mentioned in this article for more information and practice. Happy bridging!</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Real Football 2012 APK Join the Community of Fans and Replay the Games You Watched on TV.md b/spaces/congsaPfin/Manga-OCR/logs/Real Football 2012 APK Join the Community of Fans and Replay the Games You Watched on TV.md
deleted file mode 100644
index 1b7e9721b57061ee14943e9c635503f3606689dc..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Real Football 2012 APK Join the Community of Fans and Replay the Games You Watched on TV.md	
+++ /dev/null
@@ -1,113 +0,0 @@
-
-<h1>Download APK Real Football 2012: How to Enjoy the Ultimate Football Game on Your Android Device</h1>
-<p>If you are a fan of football, you might have heard of Real Football 2012, one of the most popular and realistic football games on mobile. But did you know that you can download APK Real Football 2012 and play it on your Android device without any restrictions? In this article, we will show you how to do that and what are the benefits of downloading APK Real Football 2012. We will also review some of the amazing features of this game that make it stand out from other football games. So, if you are ready to experience the ultimate football game on your smartphone, read on!</p>
-<h2>Introduction</h2>
-<h3>What is Real Football 2012?</h3>
-<p>Real Football 2012 is a football simulation game developed by Gameloft, one of the leading mobile game developers in the world. It was released in 2021 for various platforms, including Android, iOS, Windows Phone, and Java ME. It is the latest installment in the Real Football series, which has been running since 2004.</p>
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-<p>APK stands for Android Package Kit, which is a file format that contains all the necessary components to install and run an app on an Android device. By downloading APK Real Football 2012, you can enjoy some advantages over the regular version of the game, such as:</p>
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-</ul>
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-<p>Downloading APK Real Football 2012 is very easy and simple. All you need to do is follow these steps:</p>
-<ol>
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-<h2>Conclusion</h2>
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-<p>In conclusion, Real Football 2012 is an amazing football game that you can download APK Real Football 2012 and play on your Android device without any restrictions. It has many features that make it unique and enjoyable, such as:</p>
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-<li>Hypergame technology</li>
-<li>Custom kit editor</li>
-<li>Football community</li>
-<li>Realistic graphics and animations</li>
-<li>Official FIFPRO licence</li>
-<li>Many game modes</li>
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-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Real Football 2012:</p>
-<ol>
-<li><b>Is Real Football 2012 compatible with my device?</b><br/>Real Football 2012 is compatible with most Android devices that have Android 4.0 or higher. However, some devices may have performance issues or compatibility problems due to different specifications or models.</li>
-<li><b>Is Real Football 2012 safe to download?</b><br/>Yes, Real Football 2012 is safe to download as long as you use a reliable and trusted website that offers APK files for free download. We recommend using [APKCombo] or [FileHippo] as they are verified and secure sources.</li>
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diff --git a/spaces/contluForse/HuggingGPT/assets/Digital Signal Processing Ifeachor Jervis Pdf WORK Download.md b/spaces/contluForse/HuggingGPT/assets/Digital Signal Processing Ifeachor Jervis Pdf WORK Download.md
deleted file mode 100644
index e369eee38ffd54bbc8ee6e17c00291736807695e..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Digital Signal Processing Ifeachor Jervis Pdf WORK Download.md	
+++ /dev/null
@@ -1,5 +0,0 @@
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/distance_weighting.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/distance_weighting.py
deleted file mode 100644
index 90ce05bee5f633662057b3347d8791e1b4d115a0..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/distance_weighting.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from annotator.lama.saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN
-
-
-def dummy_distance_weighter(real_img, pred_img, mask):
-    return mask
-
-
-def get_gauss_kernel(kernel_size, width_factor=1):
-    coords = torch.stack(torch.meshgrid(torch.arange(kernel_size),
-                                        torch.arange(kernel_size)),
-                         dim=0).float()
-    diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor)
-    diff /= diff.sum()
-    return diff
-
-
-class BlurMask(nn.Module):
-    def __init__(self, kernel_size=5, width_factor=1):
-        super().__init__()
-        self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False)
-        self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor))
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            result = self.filter(mask) * mask
-            return result
-
-
-class EmulatedEDTMask(nn.Module):
-    def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1):
-        super().__init__()
-        self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate',
-                                       bias=False)
-        self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float))
-        self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False)
-        self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor))
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            known_mask = 1 - mask
-            dilated_known_mask = (self.dilate_filter(known_mask) > 1).float()
-            result = self.blur_filter(1 - dilated_known_mask) * mask
-            return result
-
-
-class PropagatePerceptualSim(nn.Module):
-    def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3):
-        super().__init__()
-        vgg = torchvision.models.vgg19(pretrained=True).features
-        vgg_avg_pooling = []
-
-        for weights in vgg.parameters():
-            weights.requires_grad = False
-
-        cur_level_i = 0
-        for module in vgg.modules():
-            if module.__class__.__name__ == 'Sequential':
-                continue
-            elif module.__class__.__name__ == 'MaxPool2d':
-                vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
-            else:
-                vgg_avg_pooling.append(module)
-                if module.__class__.__name__ == 'ReLU':
-                    cur_level_i += 1
-                if cur_level_i == level:
-                    break
-
-        self.features = nn.Sequential(*vgg_avg_pooling)
-
-        self.max_iters = max_iters
-        self.temperature = temperature
-        self.do_erode = erode_mask_size > 0
-        if self.do_erode:
-            self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False)
-            self.erode_mask.weight.data.fill_(1)
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img)
-            real_feats = self.features(real_img)
-
-            vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True)
-                                     / self.temperature)
-            horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True)
-                                       / self.temperature)
-
-            mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False)
-            if self.do_erode:
-                mask_scaled = (self.erode_mask(mask_scaled) > 1).float()
-
-            cur_knowness = 1 - mask_scaled
-
-            for iter_i in range(self.max_iters):
-                new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate')
-                new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate')
-
-                new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate')
-                new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate')
-
-                new_knowness = torch.stack([new_top_knowness, new_bottom_knowness,
-                                            new_left_knowness, new_right_knowness],
-                                           dim=0).max(0).values
-
-                cur_knowness = torch.max(cur_knowness, new_knowness)
-
-            cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear')
-            result = torch.min(mask, 1 - cur_knowness)
-
-            return result
-
-
-def make_mask_distance_weighter(kind='none', **kwargs):
-    if kind == 'none':
-        return dummy_distance_weighter
-    if kind == 'blur':
-        return BlurMask(**kwargs)
-    if kind == 'edt':
-        return EmulatedEDTMask(**kwargs)
-    if kind == 'pps':
-        return PropagatePerceptualSim(**kwargs)
-    raise ValueError(f'Unknown mask distance weighter kind {kind}')
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/transforms/transform.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/transforms/transform.py
deleted file mode 100644
index de44b991d7ab0d920ffb769e1402f08e358d37f7..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/data/transforms/transform.py
+++ /dev/null
@@ -1,351 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-See "Data Augmentation" tutorial for an overview of the system:
-https://detectron2.readthedocs.io/tutorials/augmentation.html
-"""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fvcore.transforms.transform import (
-    CropTransform,
-    HFlipTransform,
-    NoOpTransform,
-    Transform,
-    TransformList,
-)
-from PIL import Image
-
-try:
-    import cv2  # noqa
-except ImportError:
-    # OpenCV is an optional dependency at the moment
-    pass
-
-__all__ = [
-    "ExtentTransform",
-    "ResizeTransform",
-    "RotationTransform",
-    "ColorTransform",
-    "PILColorTransform",
-]
-
-
-class ExtentTransform(Transform):
-    """
-    Extracts a subregion from the source image and scales it to the output size.
-
-    The fill color is used to map pixels from the source rect that fall outside
-    the source image.
-
-    See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform
-    """
-
-    def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0):
-        """
-        Args:
-            src_rect (x0, y0, x1, y1): src coordinates
-            output_size (h, w): dst image size
-            interp: PIL interpolation methods
-            fill: Fill color used when src_rect extends outside image
-        """
-        super().__init__()
-        self._set_attributes(locals())
-
-    def apply_image(self, img, interp=None):
-        h, w = self.output_size
-        if len(img.shape) > 2 and img.shape[2] == 1:
-            pil_image = Image.fromarray(img[:, :, 0], mode="L")
-        else:
-            pil_image = Image.fromarray(img)
-        pil_image = pil_image.transform(
-            size=(w, h),
-            method=Image.EXTENT,
-            data=self.src_rect,
-            resample=interp if interp else self.interp,
-            fill=self.fill,
-        )
-        ret = np.asarray(pil_image)
-        if len(img.shape) > 2 and img.shape[2] == 1:
-            ret = np.expand_dims(ret, -1)
-        return ret
-
-    def apply_coords(self, coords):
-        # Transform image center from source coordinates into output coordinates
-        # and then map the new origin to the corner of the output image.
-        h, w = self.output_size
-        x0, y0, x1, y1 = self.src_rect
-        new_coords = coords.astype(np.float32)
-        new_coords[:, 0] -= 0.5 * (x0 + x1)
-        new_coords[:, 1] -= 0.5 * (y0 + y1)
-        new_coords[:, 0] *= w / (x1 - x0)
-        new_coords[:, 1] *= h / (y1 - y0)
-        new_coords[:, 0] += 0.5 * w
-        new_coords[:, 1] += 0.5 * h
-        return new_coords
-
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
-        return segmentation
-
-
-class ResizeTransform(Transform):
-    """
-    Resize the image to a target size.
-    """
-
-    def __init__(self, h, w, new_h, new_w, interp=None):
-        """
-        Args:
-            h, w (int): original image size
-            new_h, new_w (int): new image size
-            interp: PIL interpolation methods, defaults to bilinear.
-        """
-        # TODO decide on PIL vs opencv
-        super().__init__()
-        if interp is None:
-            interp = Image.BILINEAR
-        self._set_attributes(locals())
-
-    def apply_image(self, img, interp=None):
-        assert img.shape[:2] == (self.h, self.w)
-        assert len(img.shape) <= 4
-        interp_method = interp if interp is not None else self.interp
-
-        if img.dtype == np.uint8:
-            if len(img.shape) > 2 and img.shape[2] == 1:
-                pil_image = Image.fromarray(img[:, :, 0], mode="L")
-            else:
-                pil_image = Image.fromarray(img)
-            pil_image = pil_image.resize((self.new_w, self.new_h), interp_method)
-            ret = np.asarray(pil_image)
-            if len(img.shape) > 2 and img.shape[2] == 1:
-                ret = np.expand_dims(ret, -1)
-        else:
-            # PIL only supports uint8
-            if any(x < 0 for x in img.strides):
-                img = np.ascontiguousarray(img)
-            img = torch.from_numpy(img)
-            shape = list(img.shape)
-            shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:]
-            img = img.view(shape_4d).permute(2, 3, 0, 1)  # hw(c) -> nchw
-            _PIL_RESIZE_TO_INTERPOLATE_MODE = {
-                Image.NEAREST: "nearest",
-                Image.BILINEAR: "bilinear",
-                Image.BICUBIC: "bicubic",
-            }
-            mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method]
-            align_corners = None if mode == "nearest" else False
-            img = F.interpolate(
-                img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners
-            )
-            shape[:2] = (self.new_h, self.new_w)
-            ret = img.permute(2, 3, 0, 1).view(shape).numpy()  # nchw -> hw(c)
-
-        return ret
-
-    def apply_coords(self, coords):
-        coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w)
-        coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h)
-        return coords
-
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
-        return segmentation
-
-    def inverse(self):
-        return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp)
-
-
-class RotationTransform(Transform):
-    """
-    This method returns a copy of this image, rotated the given
-    number of degrees counter clockwise around its center.
-    """
-
-    def __init__(self, h, w, angle, expand=True, center=None, interp=None):
-        """
-        Args:
-            h, w (int): original image size
-            angle (float): degrees for rotation
-            expand (bool): choose if the image should be resized to fit the whole
-                rotated image (default), or simply cropped
-            center (tuple (width, height)): coordinates of the rotation center
-                if left to None, the center will be fit to the center of each image
-                center has no effect if expand=True because it only affects shifting
-            interp: cv2 interpolation method, default cv2.INTER_LINEAR
-        """
-        super().__init__()
-        image_center = np.array((w / 2, h / 2))
-        if center is None:
-            center = image_center
-        if interp is None:
-            interp = cv2.INTER_LINEAR
-        abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle))))
-        if expand:
-            # find the new width and height bounds
-            bound_w, bound_h = np.rint(
-                [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin]
-            ).astype(int)
-        else:
-            bound_w, bound_h = w, h
-
-        self._set_attributes(locals())
-        self.rm_coords = self.create_rotation_matrix()
-        # Needed because of this problem https://github.com/opencv/opencv/issues/11784
-        self.rm_image = self.create_rotation_matrix(offset=-0.5)
-
-    def apply_image(self, img, interp=None):
-        """
-        img should be a numpy array, formatted as Height * Width * Nchannels
-        """
-        if len(img) == 0 or self.angle % 360 == 0:
-            return img
-        assert img.shape[:2] == (self.h, self.w)
-        interp = interp if interp is not None else self.interp
-        return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp)
-
-    def apply_coords(self, coords):
-        """
-        coords should be a N * 2 array-like, containing N couples of (x, y) points
-        """
-        coords = np.asarray(coords, dtype=float)
-        if len(coords) == 0 or self.angle % 360 == 0:
-            return coords
-        return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :]
-
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST)
-        return segmentation
-
-    def create_rotation_matrix(self, offset=0):
-        center = (self.center[0] + offset, self.center[1] + offset)
-        rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1)
-        if self.expand:
-            # Find the coordinates of the center of rotation in the new image
-            # The only point for which we know the future coordinates is the center of the image
-            rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :]
-            new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center
-            # shift the rotation center to the new coordinates
-            rm[:, 2] += new_center
-        return rm
-
-    def inverse(self):
-        """
-        The inverse is to rotate it back with expand, and crop to get the original shape.
-        """
-        if not self.expand:  # Not possible to inverse if a part of the image is lost
-            raise NotImplementedError()
-        rotation = RotationTransform(
-            self.bound_h, self.bound_w, -self.angle, True, None, self.interp
-        )
-        crop = CropTransform(
-            (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h
-        )
-        return TransformList([rotation, crop])
-
-
-class ColorTransform(Transform):
-    """
-    Generic wrapper for any photometric transforms.
-    These transformations should only affect the color space and
-        not the coordinate space of the image (e.g. annotation
-        coordinates such as bounding boxes should not be changed)
-    """
-
-    def __init__(self, op):
-        """
-        Args:
-            op (Callable): operation to be applied to the image,
-                which takes in an ndarray and returns an ndarray.
-        """
-        if not callable(op):
-            raise ValueError("op parameter should be callable")
-        super().__init__()
-        self._set_attributes(locals())
-
-    def apply_image(self, img):
-        return self.op(img)
-
-    def apply_coords(self, coords):
-        return coords
-
-    def inverse(self):
-        return NoOpTransform()
-
-    def apply_segmentation(self, segmentation):
-        return segmentation
-
-
-class PILColorTransform(ColorTransform):
-    """
-    Generic wrapper for PIL Photometric image transforms,
-        which affect the color space and not the coordinate
-        space of the image
-    """
-
-    def __init__(self, op):
-        """
-        Args:
-            op (Callable): operation to be applied to the image,
-                which takes in a PIL Image and returns a transformed
-                PIL Image.
-                For reference on possible operations see:
-                - https://pillow.readthedocs.io/en/stable/
-        """
-        if not callable(op):
-            raise ValueError("op parameter should be callable")
-        super().__init__(op)
-
-    def apply_image(self, img):
-        img = Image.fromarray(img)
-        return np.asarray(super().apply_image(img))
-
-
-def HFlip_rotated_box(transform, rotated_boxes):
-    """
-    Apply the horizontal flip transform on rotated boxes.
-
-    Args:
-        rotated_boxes (ndarray): Nx5 floating point array of
-            (x_center, y_center, width, height, angle_degrees) format
-            in absolute coordinates.
-    """
-    # Transform x_center
-    rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0]
-    # Transform angle
-    rotated_boxes[:, 4] = -rotated_boxes[:, 4]
-    return rotated_boxes
-
-
-def Resize_rotated_box(transform, rotated_boxes):
-    """
-    Apply the resizing transform on rotated boxes. For details of how these (approximation)
-    formulas are derived, please refer to :meth:`RotatedBoxes.scale`.
-
-    Args:
-        rotated_boxes (ndarray): Nx5 floating point array of
-            (x_center, y_center, width, height, angle_degrees) format
-            in absolute coordinates.
-    """
-    scale_factor_x = transform.new_w * 1.0 / transform.w
-    scale_factor_y = transform.new_h * 1.0 / transform.h
-    rotated_boxes[:, 0] *= scale_factor_x
-    rotated_boxes[:, 1] *= scale_factor_y
-    theta = rotated_boxes[:, 4] * np.pi / 180.0
-    c = np.cos(theta)
-    s = np.sin(theta)
-    rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s))
-    rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c))
-    rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi
-
-    return rotated_boxes
-
-
-HFlipTransform.register_type("rotated_box", HFlip_rotated_box)
-ResizeTransform.register_type("rotated_box", Resize_rotated_box)
-
-# not necessary any more with latest fvcore
-NoOpTransform.register_type("rotated_box", lambda t, x: x)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/layers/roi_align.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/layers/roi_align.py
deleted file mode 100644
index 163462e1f194e1e4100da92d76d9516f7cc22e35..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/layers/roi_align.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from torch import nn
-from torchvision.ops import roi_align
-
-
-# NOTE: torchvision's RoIAlign has a different default aligned=False
-class ROIAlign(nn.Module):
-    def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
-        """
-        Args:
-            output_size (tuple): h, w
-            spatial_scale (float): scale the input boxes by this number
-            sampling_ratio (int): number of inputs samples to take for each output
-                sample. 0 to take samples densely.
-            aligned (bool): if False, use the legacy implementation in
-                Detectron. If True, align the results more perfectly.
-
-        Note:
-            The meaning of aligned=True:
-
-            Given a continuous coordinate c, its two neighboring pixel indices (in our
-            pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
-            c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
-            from the underlying signal at continuous coordinates 0.5 and 1.5). But the original
-            roi_align (aligned=False) does not subtract the 0.5 when computing neighboring
-            pixel indices and therefore it uses pixels with a slightly incorrect alignment
-            (relative to our pixel model) when performing bilinear interpolation.
-
-            With `aligned=True`,
-            we first appropriately scale the ROI and then shift it by -0.5
-            prior to calling roi_align. This produces the correct neighbors; see
-            detectron2/tests/test_roi_align.py for verification.
-
-            The difference does not make a difference to the model's performance if
-            ROIAlign is used together with conv layers.
-        """
-        super().__init__()
-        self.output_size = output_size
-        self.spatial_scale = spatial_scale
-        self.sampling_ratio = sampling_ratio
-        self.aligned = aligned
-
-        from torchvision import __version__
-
-        version = tuple(int(x) for x in __version__.split(".")[:2])
-        # https://github.com/pytorch/vision/pull/2438
-        assert version >= (0, 7), "Require torchvision >= 0.7"
-
-    def forward(self, input, rois):
-        """
-        Args:
-            input: NCHW images
-            rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
-        """
-        assert rois.dim() == 2 and rois.size(1) == 5
-        if input.is_quantized:
-            input = input.dequantize()
-        return roi_align(
-            input,
-            rois.to(dtype=input.dtype),
-            self.output_size,
-            self.spatial_scale,
-            self.sampling_ratio,
-            self.aligned,
-        )
-
-    def __repr__(self):
-        tmpstr = self.__class__.__name__ + "("
-        tmpstr += "output_size=" + str(self.output_size)
-        tmpstr += ", spatial_scale=" + str(self.spatial_scale)
-        tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
-        tmpstr += ", aligned=" + str(self.aligned)
-        tmpstr += ")"
-        return tmpstr
diff --git a/spaces/crashedice/signify/SOURCE/yolo_files/utils/flask_rest_api/restapi.py b/spaces/crashedice/signify/SOURCE/yolo_files/utils/flask_rest_api/restapi.py
deleted file mode 100644
index a54e2309715ce5d3d41e9e2e76a347db3cdb7ccb..0000000000000000000000000000000000000000
--- a/spaces/crashedice/signify/SOURCE/yolo_files/utils/flask_rest_api/restapi.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""
-Run a rest API exposing the yolov5s object detection model
-"""
-import argparse
-import io
-
-import torch
-from PIL import Image
-from flask import Flask, request
-
-app = Flask(__name__)
-
-DETECTION_URL = "/v1/object-detection/yolov5s"
-
-
-@app.route(DETECTION_URL, methods=["POST"])
-def predict():
-    if not request.method == "POST":
-        return
-
-    if request.files.get("image"):
-        image_file = request.files["image"]
-        image_bytes = image_file.read()
-
-        img = Image.open(io.BytesIO(image_bytes))
-
-        results = model(img, size=640)  # reduce size=320 for faster inference
-        return results.pandas().xyxy[0].to_json(orient="records")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
-    parser.add_argument("--port", default=5000, type=int, help="port number")
-    args = parser.parse_args()
-
-    model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True)  # force_reload to recache
-    app.run(host="0.0.0.0", port=args.port)  # debug=True causes Restarting with stat
diff --git a/spaces/cscan/CodeFormer/CodeFormer/basicsr/data/data_util.py b/spaces/cscan/CodeFormer/CodeFormer/basicsr/data/data_util.py
deleted file mode 100644
index 63b1bce8e089485182c962e830a163d6d0059da8..0000000000000000000000000000000000000000
--- a/spaces/cscan/CodeFormer/CodeFormer/basicsr/data/data_util.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import cv2
-import numpy as np
-import torch
-from os import path as osp
-from torch.nn import functional as F
-
-from basicsr.data.transforms import mod_crop
-from basicsr.utils import img2tensor, scandir
-
-
-def read_img_seq(path, require_mod_crop=False, scale=1):
-    """Read a sequence of images from a given folder path.
-
-    Args:
-        path (list[str] | str): List of image paths or image folder path.
-        require_mod_crop (bool): Require mod crop for each image.
-            Default: False.
-        scale (int): Scale factor for mod_crop. Default: 1.
-
-    Returns:
-        Tensor: size (t, c, h, w), RGB, [0, 1].
-    """
-    if isinstance(path, list):
-        img_paths = path
-    else:
-        img_paths = sorted(list(scandir(path, full_path=True)))
-    imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths]
-    if require_mod_crop:
-        imgs = [mod_crop(img, scale) for img in imgs]
-    imgs = img2tensor(imgs, bgr2rgb=True, float32=True)
-    imgs = torch.stack(imgs, dim=0)
-    return imgs
-
-
-def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'):
-    """Generate an index list for reading `num_frames` frames from a sequence
-    of images.
-
-    Args:
-        crt_idx (int): Current center index.
-        max_frame_num (int): Max number of the sequence of images (from 1).
-        num_frames (int): Reading num_frames frames.
-        padding (str): Padding mode, one of
-            'replicate' | 'reflection' | 'reflection_circle' | 'circle'
-            Examples: current_idx = 0, num_frames = 5
-            The generated frame indices under different padding mode:
-            replicate: [0, 0, 0, 1, 2]
-            reflection: [2, 1, 0, 1, 2]
-            reflection_circle: [4, 3, 0, 1, 2]
-            circle: [3, 4, 0, 1, 2]
-
-    Returns:
-        list[int]: A list of indices.
-    """
-    assert num_frames % 2 == 1, 'num_frames should be an odd number.'
-    assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.'
-
-    max_frame_num = max_frame_num - 1  # start from 0
-    num_pad = num_frames // 2
-
-    indices = []
-    for i in range(crt_idx - num_pad, crt_idx + num_pad + 1):
-        if i < 0:
-            if padding == 'replicate':
-                pad_idx = 0
-            elif padding == 'reflection':
-                pad_idx = -i
-            elif padding == 'reflection_circle':
-                pad_idx = crt_idx + num_pad - i
-            else:
-                pad_idx = num_frames + i
-        elif i > max_frame_num:
-            if padding == 'replicate':
-                pad_idx = max_frame_num
-            elif padding == 'reflection':
-                pad_idx = max_frame_num * 2 - i
-            elif padding == 'reflection_circle':
-                pad_idx = (crt_idx - num_pad) - (i - max_frame_num)
-            else:
-                pad_idx = i - num_frames
-        else:
-            pad_idx = i
-        indices.append(pad_idx)
-    return indices
-
-
-def paired_paths_from_lmdb(folders, keys):
-    """Generate paired paths from lmdb files.
-
-    Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is:
-
-    lq.lmdb
-    ├── data.mdb
-    ├── lock.mdb
-    ├── meta_info.txt
-
-    The data.mdb and lock.mdb are standard lmdb files and you can refer to
-    https://lmdb.readthedocs.io/en/release/ for more details.
-
-    The meta_info.txt is a specified txt file to record the meta information
-    of our datasets. It will be automatically created when preparing
-    datasets by our provided dataset tools.
-    Each line in the txt file records
-    1)image name (with extension),
-    2)image shape,
-    3)compression level, separated by a white space.
-    Example: `baboon.png (120,125,3) 1`
-
-    We use the image name without extension as the lmdb key.
-    Note that we use the same key for the corresponding lq and gt images.
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-            Note that this key is different from lmdb keys.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')):
-        raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb '
-                         f'formats. But received {input_key}: {input_folder}; '
-                         f'{gt_key}: {gt_folder}')
-    # ensure that the two meta_info files are the same
-    with open(osp.join(input_folder, 'meta_info.txt')) as fin:
-        input_lmdb_keys = [line.split('.')[0] for line in fin]
-    with open(osp.join(gt_folder, 'meta_info.txt')) as fin:
-        gt_lmdb_keys = [line.split('.')[0] for line in fin]
-    if set(input_lmdb_keys) != set(gt_lmdb_keys):
-        raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.')
-    else:
-        paths = []
-        for lmdb_key in sorted(input_lmdb_keys):
-            paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)]))
-        return paths
-
-
-def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl):
-    """Generate paired paths from an meta information file.
-
-    Each line in the meta information file contains the image names and
-    image shape (usually for gt), separated by a white space.
-
-    Example of an meta information file:
-    ```
-    0001_s001.png (480,480,3)
-    0001_s002.png (480,480,3)
-    ```
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-        meta_info_file (str): Path to the meta information file.
-        filename_tmpl (str): Template for each filename. Note that the
-            template excludes the file extension. Usually the filename_tmpl is
-            for files in the input folder.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    with open(meta_info_file, 'r') as fin:
-        gt_names = [line.split(' ')[0] for line in fin]
-
-    paths = []
-    for gt_name in gt_names:
-        basename, ext = osp.splitext(osp.basename(gt_name))
-        input_name = f'{filename_tmpl.format(basename)}{ext}'
-        input_path = osp.join(input_folder, input_name)
-        gt_path = osp.join(gt_folder, gt_name)
-        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
-    return paths
-
-
-def paired_paths_from_folder(folders, keys, filename_tmpl):
-    """Generate paired paths from folders.
-
-    Args:
-        folders (list[str]): A list of folder path. The order of list should
-            be [input_folder, gt_folder].
-        keys (list[str]): A list of keys identifying folders. The order should
-            be in consistent with folders, e.g., ['lq', 'gt'].
-        filename_tmpl (str): Template for each filename. Note that the
-            template excludes the file extension. Usually the filename_tmpl is
-            for files in the input folder.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. '
-                               f'But got {len(folders)}')
-    assert len(keys) == 2, ('The len of keys should be 2 with [input_key, gt_key]. ' f'But got {len(keys)}')
-    input_folder, gt_folder = folders
-    input_key, gt_key = keys
-
-    input_paths = list(scandir(input_folder))
-    gt_paths = list(scandir(gt_folder))
-    assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: '
-                                               f'{len(input_paths)}, {len(gt_paths)}.')
-    paths = []
-    for gt_path in gt_paths:
-        basename, ext = osp.splitext(osp.basename(gt_path))
-        input_name = f'{filename_tmpl.format(basename)}{ext}'
-        input_path = osp.join(input_folder, input_name)
-        assert input_name in input_paths, (f'{input_name} is not in ' f'{input_key}_paths.')
-        gt_path = osp.join(gt_folder, gt_path)
-        paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)]))
-    return paths
-
-
-def paths_from_folder(folder):
-    """Generate paths from folder.
-
-    Args:
-        folder (str): Folder path.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-
-    paths = list(scandir(folder))
-    paths = [osp.join(folder, path) for path in paths]
-    return paths
-
-
-def paths_from_lmdb(folder):
-    """Generate paths from lmdb.
-
-    Args:
-        folder (str): Folder path.
-
-    Returns:
-        list[str]: Returned path list.
-    """
-    if not folder.endswith('.lmdb'):
-        raise ValueError(f'Folder {folder}folder should in lmdb format.')
-    with open(osp.join(folder, 'meta_info.txt')) as fin:
-        paths = [line.split('.')[0] for line in fin]
-    return paths
-
-
-def generate_gaussian_kernel(kernel_size=13, sigma=1.6):
-    """Generate Gaussian kernel used in `duf_downsample`.
-
-    Args:
-        kernel_size (int): Kernel size. Default: 13.
-        sigma (float): Sigma of the Gaussian kernel. Default: 1.6.
-
-    Returns:
-        np.array: The Gaussian kernel.
-    """
-    from scipy.ndimage import filters as filters
-    kernel = np.zeros((kernel_size, kernel_size))
-    # set element at the middle to one, a dirac delta
-    kernel[kernel_size // 2, kernel_size // 2] = 1
-    # gaussian-smooth the dirac, resulting in a gaussian filter
-    return filters.gaussian_filter(kernel, sigma)
-
-
-def duf_downsample(x, kernel_size=13, scale=4):
-    """Downsamping with Gaussian kernel used in the DUF official code.
-
-    Args:
-        x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w).
-        kernel_size (int): Kernel size. Default: 13.
-        scale (int): Downsampling factor. Supported scale: (2, 3, 4).
-            Default: 4.
-
-    Returns:
-        Tensor: DUF downsampled frames.
-    """
-    assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.'
-
-    squeeze_flag = False
-    if x.ndim == 4:
-        squeeze_flag = True
-        x = x.unsqueeze(0)
-    b, t, c, h, w = x.size()
-    x = x.view(-1, 1, h, w)
-    pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2
-    x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect')
-
-    gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale)
-    gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0)
-    x = F.conv2d(x, gaussian_filter, stride=scale)
-    x = x[:, :, 2:-2, 2:-2]
-    x = x.view(b, t, c, x.size(2), x.size(3))
-    if squeeze_flag:
-        x = x.squeeze(0)
-    return x
diff --git a/spaces/cscan/demucs/app.py b/spaces/cscan/demucs/app.py
deleted file mode 100644
index 67b0ad0943e927f88e28ebb1cef9bc0794d68250..0000000000000000000000000000000000000000
--- a/spaces/cscan/demucs/app.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import gradio as gr
-from scipy.io.wavfile import write
-
-
-def inference(audio):
-  os.makedirs("out", exist_ok=True)
-  write('test.wav', audio[0], audio[1])
-  os.system("python3 -m demucs.separate -n mdx_extra_q -d cpu test.wav -o out")
-  return "./out/mdx_extra_q/test/vocals.wav","./out/mdx_extra_q/test/bass.wav",\
-"./out/mdx_extra_q/test/drums.wav","./out/mdx_extra_q/test/other.wav"
-  
-title = "Demucs"
-description = "Gradio demo for Demucs: Music Source Separation in the Waveform Domain. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1911.13254' target='_blank'>Music Source Separation in the Waveform Domain</a> | <a href='https://github.com/facebookresearch/demucs' target='_blank'>Github Repo</a></p>"
-
-examples=[['test.mp3']]
-gr.Interface(
-    inference, 
-    gr.inputs.Audio(type="numpy", label="Input"), 
-    [gr.outputs.Audio(type="filepath", label="Vocals"),gr.outputs.Audio(type="filepath", label="Bass"),gr.outputs.Audio(type="filepath", label="Drums"),gr.outputs.Audio(type="filepath", label="Other")],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples
-    ).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_o_s_t.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_o_s_t.py
deleted file mode 100644
index dba637117a0ac148af65c75853dd3bffbbbd1154..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_o_s_t.py
+++ /dev/null
@@ -1,308 +0,0 @@
-from fontTools import ttLib
-from fontTools.ttLib.standardGlyphOrder import standardGlyphOrder
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytechr, byteord, tobytes, tostr, safeEval, readHex
-from . import DefaultTable
-import sys
-import struct
-import array
-import logging
-
-log = logging.getLogger(__name__)
-
-postFormat = """
-	>
-	formatType:			16.16F
-	italicAngle:		16.16F		# italic angle in degrees
-	underlinePosition:	h
-	underlineThickness:	h
-	isFixedPitch:		L
-	minMemType42:		L			# minimum memory if TrueType font is downloaded
-	maxMemType42:		L			# maximum memory if TrueType font is downloaded
-	minMemType1:		L			# minimum memory if Type1 font is downloaded
-	maxMemType1:		L			# maximum memory if Type1 font is downloaded
-"""
-
-postFormatSize = sstruct.calcsize(postFormat)
-
-
-class table__p_o_s_t(DefaultTable.DefaultTable):
-    def decompile(self, data, ttFont):
-        sstruct.unpack(postFormat, data[:postFormatSize], self)
-        data = data[postFormatSize:]
-        if self.formatType == 1.0:
-            self.decode_format_1_0(data, ttFont)
-        elif self.formatType == 2.0:
-            self.decode_format_2_0(data, ttFont)
-        elif self.formatType == 3.0:
-            self.decode_format_3_0(data, ttFont)
-        elif self.formatType == 4.0:
-            self.decode_format_4_0(data, ttFont)
-        else:
-            # supported format
-            raise ttLib.TTLibError(
-                "'post' table format %f not supported" % self.formatType
-            )
-
-    def compile(self, ttFont):
-        data = sstruct.pack(postFormat, self)
-        if self.formatType == 1.0:
-            pass  # we're done
-        elif self.formatType == 2.0:
-            data = data + self.encode_format_2_0(ttFont)
-        elif self.formatType == 3.0:
-            pass  # we're done
-        elif self.formatType == 4.0:
-            data = data + self.encode_format_4_0(ttFont)
-        else:
-            # supported format
-            raise ttLib.TTLibError(
-                "'post' table format %f not supported" % self.formatType
-            )
-        return data
-
-    def getGlyphOrder(self):
-        """This function will get called by a ttLib.TTFont instance.
-        Do not call this function yourself, use TTFont().getGlyphOrder()
-        or its relatives instead!
-        """
-        if not hasattr(self, "glyphOrder"):
-            raise ttLib.TTLibError("illegal use of getGlyphOrder()")
-        glyphOrder = self.glyphOrder
-        del self.glyphOrder
-        return glyphOrder
-
-    def decode_format_1_0(self, data, ttFont):
-        self.glyphOrder = standardGlyphOrder[: ttFont["maxp"].numGlyphs]
-
-    def decode_format_2_0(self, data, ttFont):
-        (numGlyphs,) = struct.unpack(">H", data[:2])
-        numGlyphs = int(numGlyphs)
-        if numGlyphs > ttFont["maxp"].numGlyphs:
-            # Assume the numGlyphs field is bogus, so sync with maxp.
-            # I've seen this in one font, and if the assumption is
-            # wrong elsewhere, well, so be it: it's hard enough to
-            # work around _one_ non-conforming post format...
-            numGlyphs = ttFont["maxp"].numGlyphs
-        data = data[2:]
-        indices = array.array("H")
-        indices.frombytes(data[: 2 * numGlyphs])
-        if sys.byteorder != "big":
-            indices.byteswap()
-        data = data[2 * numGlyphs :]
-        maxIndex = max(indices)
-        self.extraNames = extraNames = unpackPStrings(data, maxIndex - 257)
-        self.glyphOrder = glyphOrder = [""] * int(ttFont["maxp"].numGlyphs)
-        for glyphID in range(numGlyphs):
-            index = indices[glyphID]
-            if index > 257:
-                try:
-                    name = extraNames[index - 258]
-                except IndexError:
-                    name = ""
-            else:
-                # fetch names from standard list
-                name = standardGlyphOrder[index]
-            glyphOrder[glyphID] = name
-        self.build_psNameMapping(ttFont)
-
-    def build_psNameMapping(self, ttFont):
-        mapping = {}
-        allNames = {}
-        for i in range(ttFont["maxp"].numGlyphs):
-            glyphName = psName = self.glyphOrder[i]
-            if glyphName == "":
-                glyphName = "glyph%.5d" % i
-            if glyphName in allNames:
-                # make up a new glyphName that's unique
-                n = allNames[glyphName]
-                while (glyphName + "#" + str(n)) in allNames:
-                    n += 1
-                allNames[glyphName] = n + 1
-                glyphName = glyphName + "#" + str(n)
-
-            self.glyphOrder[i] = glyphName
-            allNames[glyphName] = 1
-            if glyphName != psName:
-                mapping[glyphName] = psName
-
-        self.mapping = mapping
-
-    def decode_format_3_0(self, data, ttFont):
-        # Setting self.glyphOrder to None will cause the TTFont object
-        # try and construct glyph names from a Unicode cmap table.
-        self.glyphOrder = None
-
-    def decode_format_4_0(self, data, ttFont):
-        from fontTools import agl
-
-        numGlyphs = ttFont["maxp"].numGlyphs
-        indices = array.array("H")
-        indices.frombytes(data)
-        if sys.byteorder != "big":
-            indices.byteswap()
-        # In some older fonts, the size of the post table doesn't match
-        # the number of glyphs. Sometimes it's bigger, sometimes smaller.
-        self.glyphOrder = glyphOrder = [""] * int(numGlyphs)
-        for i in range(min(len(indices), numGlyphs)):
-            if indices[i] == 0xFFFF:
-                self.glyphOrder[i] = ""
-            elif indices[i] in agl.UV2AGL:
-                self.glyphOrder[i] = agl.UV2AGL[indices[i]]
-            else:
-                self.glyphOrder[i] = "uni%04X" % indices[i]
-        self.build_psNameMapping(ttFont)
-
-    def encode_format_2_0(self, ttFont):
-        numGlyphs = ttFont["maxp"].numGlyphs
-        glyphOrder = ttFont.getGlyphOrder()
-        assert len(glyphOrder) == numGlyphs
-        indices = array.array("H")
-        extraDict = {}
-        extraNames = self.extraNames = [
-            n for n in self.extraNames if n not in standardGlyphOrder
-        ]
-        for i in range(len(extraNames)):
-            extraDict[extraNames[i]] = i
-        for glyphID in range(numGlyphs):
-            glyphName = glyphOrder[glyphID]
-            if glyphName in self.mapping:
-                psName = self.mapping[glyphName]
-            else:
-                psName = glyphName
-            if psName in extraDict:
-                index = 258 + extraDict[psName]
-            elif psName in standardGlyphOrder:
-                index = standardGlyphOrder.index(psName)
-            else:
-                index = 258 + len(extraNames)
-                extraDict[psName] = len(extraNames)
-                extraNames.append(psName)
-            indices.append(index)
-        if sys.byteorder != "big":
-            indices.byteswap()
-        return (
-            struct.pack(">H", numGlyphs) + indices.tobytes() + packPStrings(extraNames)
-        )
-
-    def encode_format_4_0(self, ttFont):
-        from fontTools import agl
-
-        numGlyphs = ttFont["maxp"].numGlyphs
-        glyphOrder = ttFont.getGlyphOrder()
-        assert len(glyphOrder) == numGlyphs
-        indices = array.array("H")
-        for glyphID in glyphOrder:
-            glyphID = glyphID.split("#")[0]
-            if glyphID in agl.AGL2UV:
-                indices.append(agl.AGL2UV[glyphID])
-            elif len(glyphID) == 7 and glyphID[:3] == "uni":
-                indices.append(int(glyphID[3:], 16))
-            else:
-                indices.append(0xFFFF)
-        if sys.byteorder != "big":
-            indices.byteswap()
-        return indices.tobytes()
-
-    def toXML(self, writer, ttFont):
-        formatstring, names, fixes = sstruct.getformat(postFormat)
-        for name in names:
-            value = getattr(self, name)
-            writer.simpletag(name, value=value)
-            writer.newline()
-        if hasattr(self, "mapping"):
-            writer.begintag("psNames")
-            writer.newline()
-            writer.comment(
-                "This file uses unique glyph names based on the information\n"
-                "found in the 'post' table. Since these names might not be unique,\n"
-                "we have to invent artificial names in case of clashes. In order to\n"
-                "be able to retain the original information, we need a name to\n"
-                "ps name mapping for those cases where they differ. That's what\n"
-                "you see below.\n"
-            )
-            writer.newline()
-            items = sorted(self.mapping.items())
-            for name, psName in items:
-                writer.simpletag("psName", name=name, psName=psName)
-                writer.newline()
-            writer.endtag("psNames")
-            writer.newline()
-        if hasattr(self, "extraNames"):
-            writer.begintag("extraNames")
-            writer.newline()
-            writer.comment(
-                "following are the name that are not taken from the standard Mac glyph order"
-            )
-            writer.newline()
-            for name in self.extraNames:
-                writer.simpletag("psName", name=name)
-                writer.newline()
-            writer.endtag("extraNames")
-            writer.newline()
-        if hasattr(self, "data"):
-            writer.begintag("hexdata")
-            writer.newline()
-            writer.dumphex(self.data)
-            writer.endtag("hexdata")
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name not in ("psNames", "extraNames", "hexdata"):
-            setattr(self, name, safeEval(attrs["value"]))
-        elif name == "psNames":
-            self.mapping = {}
-            for element in content:
-                if not isinstance(element, tuple):
-                    continue
-                name, attrs, content = element
-                if name == "psName":
-                    self.mapping[attrs["name"]] = attrs["psName"]
-        elif name == "extraNames":
-            self.extraNames = []
-            for element in content:
-                if not isinstance(element, tuple):
-                    continue
-                name, attrs, content = element
-                if name == "psName":
-                    self.extraNames.append(attrs["name"])
-        else:
-            self.data = readHex(content)
-
-
-def unpackPStrings(data, n):
-    # extract n Pascal strings from data.
-    # if there is not enough data, use ""
-
-    strings = []
-    index = 0
-    dataLen = len(data)
-
-    for _ in range(n):
-        if dataLen <= index:
-            length = 0
-        else:
-            length = byteord(data[index])
-        index += 1
-
-        if dataLen <= index + length - 1:
-            name = ""
-        else:
-            name = tostr(data[index : index + length], encoding="latin1")
-        strings.append(name)
-        index += length
-
-    if index < dataLen:
-        log.warning("%d extra bytes in post.stringData array", dataLen - index)
-
-    elif dataLen < index:
-        log.warning("not enough data in post.stringData array")
-
-    return strings
-
-
-def packPStrings(strings):
-    data = b""
-    for s in strings:
-        data = data + bytechr(len(s)) + tobytes(s, encoding="latin1")
-    return data
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-74850412.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-74850412.js
deleted file mode 100644
index d4c076eb14c44c6b4e8c2076d7fa865023194085..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-74850412.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{T as s}from"./index-85ff0bcb.js";import"./index-9e76ffee.js";import"./Button-30a08c0b.js";import"./BlockTitle-af232cbc.js";import"./Info-77722665.js";import"./Copy-92242405.js";const i=["static","dynamic"];export{s as Component,i as modes};
-//# sourceMappingURL=index-74850412.js.map
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dynamic_modules_utils.py b/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dynamic_modules_utils.py
deleted file mode 100644
index 1951c4fa2623b6b14b85c035395a738cdd733eea..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dynamic_modules_utils.py
+++ /dev/null
@@ -1,456 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Utilities to dynamically load objects from the Hub."""
-
-import importlib
-import inspect
-import json
-import os
-import re
-import shutil
-import sys
-from distutils.version import StrictVersion
-from pathlib import Path
-from typing import Dict, Optional, Union
-from urllib import request
-
-from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
-
-from .. import __version__
-from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
-
-
-COMMUNITY_PIPELINES_URL = (
-    "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
-)
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def get_diffusers_versions():
-    url = "https://pypi.org/pypi/diffusers/json"
-    releases = json.loads(request.urlopen(url).read())["releases"].keys()
-    return sorted(releases, key=StrictVersion)
-
-
-def init_hf_modules():
-    """
-    Creates the cache directory for modules with an init, and adds it to the Python path.
-    """
-    # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
-    if HF_MODULES_CACHE in sys.path:
-        return
-
-    sys.path.append(HF_MODULES_CACHE)
-    os.makedirs(HF_MODULES_CACHE, exist_ok=True)
-    init_path = Path(HF_MODULES_CACHE) / "__init__.py"
-    if not init_path.exists():
-        init_path.touch()
-
-
-def create_dynamic_module(name: Union[str, os.PathLike]):
-    """
-    Creates a dynamic module in the cache directory for modules.
-    """
-    init_hf_modules()
-    dynamic_module_path = Path(HF_MODULES_CACHE) / name
-    # If the parent module does not exist yet, recursively create it.
-    if not dynamic_module_path.parent.exists():
-        create_dynamic_module(dynamic_module_path.parent)
-    os.makedirs(dynamic_module_path, exist_ok=True)
-    init_path = dynamic_module_path / "__init__.py"
-    if not init_path.exists():
-        init_path.touch()
-
-
-def get_relative_imports(module_file):
-    """
-    Get the list of modules that are relatively imported in a module file.
-
-    Args:
-        module_file (`str` or `os.PathLike`): The module file to inspect.
-    """
-    with open(module_file, "r", encoding="utf-8") as f:
-        content = f.read()
-
-    # Imports of the form `import .xxx`
-    relative_imports = re.findall("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
-    # Imports of the form `from .xxx import yyy`
-    relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
-    # Unique-ify
-    return list(set(relative_imports))
-
-
-def get_relative_import_files(module_file):
-    """
-    Get the list of all files that are needed for a given module. Note that this function recurses through the relative
-    imports (if a imports b and b imports c, it will return module files for b and c).
-
-    Args:
-        module_file (`str` or `os.PathLike`): The module file to inspect.
-    """
-    no_change = False
-    files_to_check = [module_file]
-    all_relative_imports = []
-
-    # Let's recurse through all relative imports
-    while not no_change:
-        new_imports = []
-        for f in files_to_check:
-            new_imports.extend(get_relative_imports(f))
-
-        module_path = Path(module_file).parent
-        new_import_files = [str(module_path / m) for m in new_imports]
-        new_import_files = [f for f in new_import_files if f not in all_relative_imports]
-        files_to_check = [f"{f}.py" for f in new_import_files]
-
-        no_change = len(new_import_files) == 0
-        all_relative_imports.extend(files_to_check)
-
-    return all_relative_imports
-
-
-def check_imports(filename):
-    """
-    Check if the current Python environment contains all the libraries that are imported in a file.
-    """
-    with open(filename, "r", encoding="utf-8") as f:
-        content = f.read()
-
-    # Imports of the form `import xxx`
-    imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
-    # Imports of the form `from xxx import yyy`
-    imports += re.findall("^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
-    # Only keep the top-level module
-    imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
-
-    # Unique-ify and test we got them all
-    imports = list(set(imports))
-    missing_packages = []
-    for imp in imports:
-        try:
-            importlib.import_module(imp)
-        except ImportError:
-            missing_packages.append(imp)
-
-    if len(missing_packages) > 0:
-        raise ImportError(
-            "This modeling file requires the following packages that were not found in your environment: "
-            f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
-        )
-
-    return get_relative_imports(filename)
-
-
-def get_class_in_module(class_name, module_path):
-    """
-    Import a module on the cache directory for modules and extract a class from it.
-    """
-    module_path = module_path.replace(os.path.sep, ".")
-    module = importlib.import_module(module_path)
-
-    if class_name is None:
-        return find_pipeline_class(module)
-    return getattr(module, class_name)
-
-
-def find_pipeline_class(loaded_module):
-    """
-    Retrieve pipeline class that inherits from `DiffusionPipeline`. Note that there has to be exactly one class
-    inheriting from `DiffusionPipeline`.
-    """
-    from ..pipelines import DiffusionPipeline
-
-    cls_members = dict(inspect.getmembers(loaded_module, inspect.isclass))
-
-    pipeline_class = None
-    for cls_name, cls in cls_members.items():
-        if (
-            cls_name != DiffusionPipeline.__name__
-            and issubclass(cls, DiffusionPipeline)
-            and cls.__module__.split(".")[0] != "diffusers"
-        ):
-            if pipeline_class is not None:
-                raise ValueError(
-                    f"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
-                    f" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
-                    f" {loaded_module}."
-                )
-            pipeline_class = cls
-
-    return pipeline_class
-
-
-def get_cached_module_file(
-    pretrained_model_name_or_path: Union[str, os.PathLike],
-    module_file: str,
-    cache_dir: Optional[Union[str, os.PathLike]] = None,
-    force_download: bool = False,
-    resume_download: bool = False,
-    proxies: Optional[Dict[str, str]] = None,
-    use_auth_token: Optional[Union[bool, str]] = None,
-    revision: Optional[str] = None,
-    local_files_only: bool = False,
-):
-    """
-    Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
-    Transformers module.
-
-    Args:
-        pretrained_model_name_or_path (`str` or `os.PathLike`):
-            This can be either:
-
-            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
-              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
-              under a user or organization name, like `dbmdz/bert-base-german-cased`.
-            - a path to a *directory* containing a configuration file saved using the
-              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
-
-        module_file (`str`):
-            The name of the module file containing the class to look for.
-        cache_dir (`str` or `os.PathLike`, *optional*):
-            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
-            cache should not be used.
-        force_download (`bool`, *optional*, defaults to `False`):
-            Whether or not to force to (re-)download the configuration files and override the cached versions if they
-            exist.
-        resume_download (`bool`, *optional*, defaults to `False`):
-            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
-        proxies (`Dict[str, str]`, *optional*):
-            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
-        use_auth_token (`str` or *bool*, *optional*):
-            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-            when running `transformers-cli login` (stored in `~/.huggingface`).
-        revision (`str`, *optional*, defaults to `"main"`):
-            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-            git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-            identifier allowed by git.
-        local_files_only (`bool`, *optional*, defaults to `False`):
-            If `True`, will only try to load the tokenizer configuration from local files.
-
-    <Tip>
-
-    You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private
-    or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).
-
-    </Tip>
-
-    Returns:
-        `str`: The path to the module inside the cache.
-    """
-    # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
-    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-
-    module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
-
-    if os.path.isfile(module_file_or_url):
-        resolved_module_file = module_file_or_url
-        submodule = "local"
-    elif pretrained_model_name_or_path.count("/") == 0:
-        available_versions = get_diffusers_versions()
-        # cut ".dev0"
-        latest_version = "v" + ".".join(__version__.split(".")[:3])
-
-        # retrieve github version that matches
-        if revision is None:
-            revision = latest_version if latest_version in available_versions else "main"
-            logger.info(f"Defaulting to latest_version: {revision}.")
-        elif revision in available_versions:
-            revision = f"v{revision}"
-        elif revision == "main":
-            revision = revision
-        else:
-            raise ValueError(
-                f"`custom_revision`: {revision} does not exist. Please make sure to choose one of"
-                f" {', '.join(available_versions + ['main'])}."
-            )
-
-        # community pipeline on GitHub
-        github_url = COMMUNITY_PIPELINES_URL.format(revision=revision, pipeline=pretrained_model_name_or_path)
-        try:
-            resolved_module_file = cached_download(
-                github_url,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                proxies=proxies,
-                resume_download=resume_download,
-                local_files_only=local_files_only,
-                use_auth_token=False,
-            )
-            submodule = "git"
-            module_file = pretrained_model_name_or_path + ".py"
-        except EnvironmentError:
-            logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
-            raise
-    else:
-        try:
-            # Load from URL or cache if already cached
-            resolved_module_file = hf_hub_download(
-                pretrained_model_name_or_path,
-                module_file,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                proxies=proxies,
-                resume_download=resume_download,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-            )
-            submodule = os.path.join("local", "--".join(pretrained_model_name_or_path.split("/")))
-        except EnvironmentError:
-            logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
-            raise
-
-    # Check we have all the requirements in our environment
-    modules_needed = check_imports(resolved_module_file)
-
-    # Now we move the module inside our cached dynamic modules.
-    full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
-    create_dynamic_module(full_submodule)
-    submodule_path = Path(HF_MODULES_CACHE) / full_submodule
-    if submodule == "local" or submodule == "git":
-        # We always copy local files (we could hash the file to see if there was a change, and give them the name of
-        # that hash, to only copy when there is a modification but it seems overkill for now).
-        # The only reason we do the copy is to avoid putting too many folders in sys.path.
-        shutil.copy(resolved_module_file, submodule_path / module_file)
-        for module_needed in modules_needed:
-            module_needed = f"{module_needed}.py"
-            shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
-    else:
-        # Get the commit hash
-        # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
-        if isinstance(use_auth_token, str):
-            token = use_auth_token
-        elif use_auth_token is True:
-            token = HfFolder.get_token()
-        else:
-            token = None
-
-        commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha
-
-        # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
-        # benefit of versioning.
-        submodule_path = submodule_path / commit_hash
-        full_submodule = full_submodule + os.path.sep + commit_hash
-        create_dynamic_module(full_submodule)
-
-        if not (submodule_path / module_file).exists():
-            shutil.copy(resolved_module_file, submodule_path / module_file)
-        # Make sure we also have every file with relative
-        for module_needed in modules_needed:
-            if not (submodule_path / module_needed).exists():
-                get_cached_module_file(
-                    pretrained_model_name_or_path,
-                    f"{module_needed}.py",
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    resume_download=resume_download,
-                    proxies=proxies,
-                    use_auth_token=use_auth_token,
-                    revision=revision,
-                    local_files_only=local_files_only,
-                )
-    return os.path.join(full_submodule, module_file)
-
-
-def get_class_from_dynamic_module(
-    pretrained_model_name_or_path: Union[str, os.PathLike],
-    module_file: str,
-    class_name: Optional[str] = None,
-    cache_dir: Optional[Union[str, os.PathLike]] = None,
-    force_download: bool = False,
-    resume_download: bool = False,
-    proxies: Optional[Dict[str, str]] = None,
-    use_auth_token: Optional[Union[bool, str]] = None,
-    revision: Optional[str] = None,
-    local_files_only: bool = False,
-    **kwargs,
-):
-    """
-    Extracts a class from a module file, present in the local folder or repository of a model.
-
-    <Tip warning={true}>
-
-    Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
-    therefore only be called on trusted repos.
-
-    </Tip>
-
-    Args:
-        pretrained_model_name_or_path (`str` or `os.PathLike`):
-            This can be either:
-
-            - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
-              huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
-              under a user or organization name, like `dbmdz/bert-base-german-cased`.
-            - a path to a *directory* containing a configuration file saved using the
-              [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
-
-        module_file (`str`):
-            The name of the module file containing the class to look for.
-        class_name (`str`):
-            The name of the class to import in the module.
-        cache_dir (`str` or `os.PathLike`, *optional*):
-            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
-            cache should not be used.
-        force_download (`bool`, *optional*, defaults to `False`):
-            Whether or not to force to (re-)download the configuration files and override the cached versions if they
-            exist.
-        resume_download (`bool`, *optional*, defaults to `False`):
-            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
-        proxies (`Dict[str, str]`, *optional*):
-            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
-            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
-        use_auth_token (`str` or `bool`, *optional*):
-            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
-            when running `transformers-cli login` (stored in `~/.huggingface`).
-        revision (`str`, *optional*, defaults to `"main"`):
-            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-            git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-            identifier allowed by git.
-        local_files_only (`bool`, *optional*, defaults to `False`):
-            If `True`, will only try to load the tokenizer configuration from local files.
-
-    <Tip>
-
-    You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private
-    or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).
-
-    </Tip>
-
-    Returns:
-        `type`: The class, dynamically imported from the module.
-
-    Examples:
-
-    ```python
-    # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
-    # module.
-    cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
-    ```"""
-    # And lastly we get the class inside our newly created module
-    final_module = get_cached_module_file(
-        pretrained_model_name_or_path,
-        module_file,
-        cache_dir=cache_dir,
-        force_download=force_download,
-        resume_download=resume_download,
-        proxies=proxies,
-        use_auth_token=use_auth_token,
-        revision=revision,
-        local_files_only=local_files_only,
-    )
-    return get_class_in_module(class_name, final_module.replace(".py", ""))
diff --git a/spaces/deelerb/3dselfie/PIFu/lib/model/ConvFilters.py b/spaces/deelerb/3dselfie/PIFu/lib/model/ConvFilters.py
deleted file mode 100644
index 1348ddea27e1bb3b0a65592bf78c92305dce0bd7..0000000000000000000000000000000000000000
--- a/spaces/deelerb/3dselfie/PIFu/lib/model/ConvFilters.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.models.resnet as resnet
-import torchvision.models.vgg as vgg
-
-
-class MultiConv(nn.Module):
-    def __init__(self, filter_channels):
-        super(MultiConv, self).__init__()
-        self.filters = []
-
-        for l in range(0, len(filter_channels) - 1):
-            self.filters.append(
-                nn.Conv2d(filter_channels[l], filter_channels[l + 1], kernel_size=4, stride=2))
-            self.add_module("conv%d" % l, self.filters[l])
-
-    def forward(self, image):
-        '''
-        :param image: [BxC_inxHxW] tensor of input image
-        :return: list of [BxC_outxHxW] tensors of output features
-        '''
-        y = image
-        # y = F.relu(self.bn0(self.conv0(y)), True)
-        feat_pyramid = [y]
-        for i, f in enumerate(self.filters):
-            y = f(y)
-            if i != len(self.filters) - 1:
-                y = F.leaky_relu(y)
-            # y = F.max_pool2d(y, kernel_size=2, stride=2)
-            feat_pyramid.append(y)
-        return feat_pyramid
-
-
-class Vgg16(torch.nn.Module):
-    def __init__(self):
-        super(Vgg16, self).__init__()
-        vgg_pretrained_features = vgg.vgg16(pretrained=True).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(23, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        h = self.slice5(h)
-        h_relu5_3 = h
-
-        return [h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3]
-
-
-class ResNet(nn.Module):
-    def __init__(self, model='resnet18'):
-        super(ResNet, self).__init__()
-
-        if model == 'resnet18':
-            net = resnet.resnet18(pretrained=True)
-        elif model == 'resnet34':
-            net = resnet.resnet34(pretrained=True)
-        elif model == 'resnet50':
-            net = resnet.resnet50(pretrained=True)
-        else:
-            raise NameError('Unknown Fan Filter setting!')
-
-        self.conv1 = net.conv1
-
-        self.pool = net.maxpool
-        self.layer0 = nn.Sequential(net.conv1, net.bn1, net.relu)
-        self.layer1 = net.layer1
-        self.layer2 = net.layer2
-        self.layer3 = net.layer3
-        self.layer4 = net.layer4
-
-    def forward(self, image):
-        '''
-        :param image: [BxC_inxHxW] tensor of input image
-        :return: list of [BxC_outxHxW] tensors of output features
-        '''
-
-        y = image
-        feat_pyramid = []
-        y = self.layer0(y)
-        feat_pyramid.append(y)
-        y = self.layer1(self.pool(y))
-        feat_pyramid.append(y)
-        y = self.layer2(y)
-        feat_pyramid.append(y)
-        y = self.layer3(y)
-        feat_pyramid.append(y)
-        y = self.layer4(y)
-        feat_pyramid.append(y)
-
-        return feat_pyramid
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/comic_style/comic_style.py b/spaces/deepskyreal/ai-mixer-hotchpotch/comic_style/comic_style.py
deleted file mode 100644
index 39bfed2306fea8cf50c3778b06a5d9d533d796bc..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/comic_style/comic_style.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import cv2 as cv
-import numpy as np
-import torch
-from PIL import Image, ImageOps
-
-from comic_style.face_detection import align
-
-torch.set_grad_enabled(False)
-model = torch.jit.load('comic_style/u2net_bce_itr_16000_train_3.835149_tar_0.542587-400x_360x.jit.pt')
-model.eval()
-
-
-# https://en.wikipedia.org/wiki/Unsharp_masking
-# https://stackoverflow.com/a/55590133/1495606
-def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=2.0, threshold=0):
-    """Return a sharpened version of the image, using an unsharp mask."""
-    blurred = cv.GaussianBlur(image, kernel_size, sigma)
-    sharpened = float(amount + 1) * image - float(amount) * blurred
-    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
-    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
-    sharpened = sharpened.round().astype(np.uint8)
-    if threshold > 0:
-        low_contrast_mask = np.absolute(image - blurred) < threshold
-        np.copyto(sharpened, image, where=low_contrast_mask)
-    return sharpened
-
-
-def normPRED(d):
-    ma = np.max(d)
-    mi = np.min(d)
-
-    dn = (d - mi) / (ma - mi)
-
-    return dn
-
-
-def array_to_np(array_in):
-    array_in = normPRED(array_in)
-    array_in = np.squeeze(255.0 * (array_in))
-    array_in = np.transpose(array_in, (1, 2, 0))
-    return array_in
-
-
-def array_to_image(array_in):
-    array_in = normPRED(array_in)
-    array_in = np.squeeze(255.0 * (array_in))
-    array_in = np.transpose(array_in, (1, 2, 0))
-    im = Image.fromarray(array_in.astype(np.uint8))
-    return im
-
-
-def image_as_array(image_in):
-    image_in = np.array(image_in, np.float32)
-    tmpImg = np.zeros((image_in.shape[0], image_in.shape[1], 3))
-    image_in = image_in / np.max(image_in)
-    if image_in.shape[2] == 1:
-        tmpImg[:, :, 0] = (image_in[:, :, 0] - 0.485) / 0.229
-        tmpImg[:, :, 1] = (image_in[:, :, 0] - 0.485) / 0.229
-        tmpImg[:, :, 2] = (image_in[:, :, 0] - 0.485) / 0.229
-    else:
-        tmpImg[:, :, 0] = (image_in[:, :, 0] - 0.485) / 0.229
-        tmpImg[:, :, 1] = (image_in[:, :, 1] - 0.456) / 0.224
-        tmpImg[:, :, 2] = (image_in[:, :, 2] - 0.406) / 0.225
-
-    tmpImg = tmpImg.transpose((2, 0, 1))
-    image_out = np.expand_dims(tmpImg, 0)
-    return image_out
-
-
-def find_aligned_face(image_in, size=400):
-    aligned_image, n_faces, quad = align(image_in, face_index=0, output_size=size)
-    return aligned_image, n_faces, quad
-
-
-def align_first_face(image_in, size=400):
-    aligned_image, n_faces, quad = find_aligned_face(image_in, size=size)
-    if n_faces == 0:
-        try:
-            image_in = ImageOps.exif_transpose(image_in)
-        except:
-            print("exif problem, not rotating")
-        image_in = image_in.resize((size, size))
-        im_array = image_as_array(image_in)
-    else:
-        im_array = image_as_array(aligned_image)
-
-    return im_array
-
-
-def img_concat_h(im1, im2):
-    dst = Image.new('RGB', (im1.width + im2.width, im1.height))
-    dst.paste(im1, (0, 0))
-    dst.paste(im2, (im1.width, 0))
-    return dst
-
-
-def face2hero(
-        img: Image.Image,
-        size: int
-) -> Image.Image:
-    aligned_img = align_first_face(img)
-    if aligned_img is None:
-        output = None
-    else:
-        input = torch.Tensor(aligned_img)
-        results = model(input)
-        hero_np_image = array_to_np(results[1].detach().numpy())
-        hero_image = unsharp_mask(hero_np_image)
-        hero_image = Image.fromarray(hero_image)
-        # hero_image = hero_image.resize((int(hero_image.width * 0.3), int(hero_image.height * 0.3)), Image.ANTIALIAS)
-        # output = img_concat_h(array_to_image(aligned_img), hero_image)
-        del results
-    return hero_image
-
-
-def inference(img):
-    out = face2hero(img, 400)
-    return out
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/utils/paste_pic.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/utils/paste_pic.py
deleted file mode 100644
index 3554aa33fd9744e9858dd7ed4f42c651f6af31c8..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/utils/paste_pic.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import cv2, os
-import numpy as np
-from tqdm import tqdm
-import uuid
-
-from sad_talker.src.utils.videoio import save_video_with_watermark 
-
-def paste_pic(video_path, pic_path, crop_info, new_audio_path, full_video_path, extended_crop=False):
-
-    if not os.path.isfile(pic_path):
-        raise ValueError('pic_path must be a valid path to video/image file')
-    elif pic_path.split('.')[-1] in ['jpg', 'png', 'jpeg']:
-        # loader for first frame
-        full_img = cv2.imread(pic_path)
-    else:
-        # loader for videos
-        video_stream = cv2.VideoCapture(pic_path)
-        fps = video_stream.get(cv2.CAP_PROP_FPS)
-        full_frames = [] 
-        while 1:
-            still_reading, frame = video_stream.read()
-            if not still_reading:
-                video_stream.release()
-                break 
-            break 
-        full_img = frame
-    frame_h = full_img.shape[0]
-    frame_w = full_img.shape[1]
-
-    video_stream = cv2.VideoCapture(video_path)
-    fps = video_stream.get(cv2.CAP_PROP_FPS)
-    crop_frames = []
-    while 1:
-        still_reading, frame = video_stream.read()
-        if not still_reading:
-            video_stream.release()
-            break
-        crop_frames.append(frame)
-    
-    if len(crop_info) != 3:
-        print("you didn't crop the image")
-        return
-    else:
-        r_w, r_h = crop_info[0]
-        clx, cly, crx, cry = crop_info[1]
-        lx, ly, rx, ry = crop_info[2]
-        lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        # oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
-        # oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
-
-        if extended_crop:
-            oy1, oy2, ox1, ox2 = cly, cry, clx, crx
-        else:
-            oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
-
-    tmp_path = str(uuid.uuid4())+'.mp4'
-    out_tmp = cv2.VideoWriter(tmp_path, cv2.VideoWriter_fourcc(*'MP4V'), fps, (frame_w, frame_h))
-    for crop_frame in tqdm(crop_frames, 'seamlessClone:'):
-        p = cv2.resize(crop_frame.astype(np.uint8), (ox2-ox1, oy2 - oy1)) 
-
-        mask = 255*np.ones(p.shape, p.dtype)
-        location = ((ox1+ox2) // 2, (oy1+oy2) // 2)
-        gen_img = cv2.seamlessClone(p, full_img, mask, location, cv2.NORMAL_CLONE)
-        out_tmp.write(gen_img)
-
-    out_tmp.release()
-
-    save_video_with_watermark(tmp_path, new_audio_path, full_video_path, watermark=False)
-    os.remove(tmp_path)
diff --git a/spaces/diacanFperku/AutoGPT/Caesar 2 Software Crack REPACK Download.md b/spaces/diacanFperku/AutoGPT/Caesar 2 Software Crack REPACK Download.md
deleted file mode 100644
index b1ccb8cab0617de75fac92922b7d0a04f4e941a1..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Caesar 2 Software Crack REPACK Download.md	
+++ /dev/null
@@ -1,151 +0,0 @@
-
-<h1>CAESAR II Software Crack Download: How to Get the Best Pipe Stress Analysis Software for Free</h1>
-
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-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Lethis - Path Of Progress Cheat Code For Pc.md b/spaces/diacanFperku/AutoGPT/Lethis - Path Of Progress Cheat Code For Pc.md
deleted file mode 100644
index 4b8f392705e247425c615da3ee2c59233185845a..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Lethis - Path Of Progress Cheat Code For Pc.md	
+++ /dev/null
@@ -1,73 +0,0 @@
-<br />
-<h1>Lethis - Path Of Progress Cheat Code For Pc: How to Enhance Your Gaming Experience</h1>
-<p>Lethis - Path Of Progress is a city-building game that takes place in a steampunk world. You have to manage your resources, build your infrastructure, and satisfy your citizens' needs. The game has a campaign mode with 26 missions and a sandbox mode where you can create your own scenarios. The game also has a cheat system that allows you to access some hidden features and options. In this article, we will show you how to use Lethis - Path Of Progress cheat code for pc and what are the benefits and drawbacks of using them.</p>
-<h2>Lethis - Path Of Progress Cheat Code For Pc</h2><br /><p><b><b>Download File</b> &#127383; <a href="https://gohhs.com/2uFTwn">https://gohhs.com/2uFTwn</a></b></p><br /><br />
-<h2>How to use Lethis - Path Of Progress cheat code for pc?</h2>
-<p>To use Lethis - Path Of Progress cheat code for pc, you need to follow these steps:</p>
-<ol>
-<li>Launch the game and start a mission or a sandbox mode.</li>
-<li>Press F7 on your keyboard to open the cheat console.</li>
-<li>Type one of the cheat codes and press Enter to activate it.</li>
-<li>Press F7 again to close the cheat console.</li>
-</ol>
-<p>Here are some of the cheat codes that you can use in Lethis - Path Of Progress:</p>
-<ul>
-<li>WelcomeToBritanny: Fills up all empty warehouse space with beer.</li>
-<li>ItPrintsMoney: Adds 100 000 Florins to your treasury.</li>
-<li>TrickOrTreat: Fills up all empty granary space with pumpkins.</li>
-<li>PourQuelquesRillettesDePlus: Fills up all empty granary space with meat.</li>
-<li>ByeByeNemo: Fills up all empty granary space with fish.</li>
-<li>WinWinSubmarine: Wins the current mission.</li>
-<li>Ambrosia: No effect. Only used to unlock the Achievements: "Ambrosia".</li>
-<li>GuybrushThreepwood: No effect. Only used to unlock the Achievements: "I'm a Pirate!".</li>
-<li>TotalBiscuit: All buildings next to your maintenance buildings collapse.</li>
-<li>Fest-Noz: Activates/deactivates the happiness system.</li>
-<li>DeutscheBand: Plays one of the phantom-songs.</li>
-</ul>
-<h2>What are the benefits of using Lethis - Path Of Progress cheat code for pc?</h2>
-<p>Using Lethis - Path Of Progress cheat code for pc has some benefits for your gaming experience. Some of these benefits are:</p>
-<ul>
-<li>You can have more fun and experiment with different scenarios and outcomes.</li>
-<li>You can overcome some challenges and difficulties that you may encounter in the game.</li>
-<li>You can unlock some achievements that may be hard or impossible to get otherwise.</li>
-<li>You can discover some hidden features and options that may not be available in the normal gameplay.</li>
-</ul>
-<h2>What are the drawbacks of using Lethis - Path Of Progress cheat code for pc?</h2>
-<p>Using Lethis - Path Of Progress cheat code for pc also has some drawbacks for your gaming experience. Some of these drawbacks are:</p>
-<ul>
-<li>You may lose some of the challenge and satisfaction that comes from playing the game legitimately.</li>
-<li>You may encounter some bugs or errors that may affect your game performance or stability.</li>
-<li>You may spoil some of the surprises and secrets that the game has to offer.</li>
-<li>You may affect your game balance and difficulty by using some cheats that may make the game too easy or too hard.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Lethis - Path Of Progress is a city-building game that takes place in a steampunk world. You have to manage your resources, build your infrastructure, and satisfy your citizens' needs. The game also has a cheat system that allows you to access some hidden features and options. To use Lethis - Path Of Progress cheat code for pc, you need to press F7 to open the cheat console, type one of the cheat codes, and press Enter to activate it. Using Lethis - Path Of Progress cheat code for pc has some benefits and drawbacks for your gaming experience. You can have more fun and experiment with different scenarios and outcomes, but you may also lose some of the challenge and satisfaction that comes from playing the game legitimately. You can unlock some achievements that may be hard or impossible to get otherwise, but you may also spoil some of the surprises and secrets that the game has to offer. You can discover some hidden features and options that may not be available in the normal gameplay, but you may also encounter some bugs or errors that may affect your game performance or stability. You can affect your game balance and difficulty by using some cheats that may make the game too easy or too hard, but you may also overcome some challenges and difficulties that you may encounter in the game. Whether you use Lethis - Path Of Progress cheat code for pc or not is up to you, but remember to have fun and enjoy the game!</p>
-<h2>How to find more Lethis - Path Of Progress cheat code for pc?</h2>
-<p>If you want to find more Lethis - Path Of Progress cheat code for pc, you can search on the internet for some websites that offer cheats and trainers for PC games. One of these websites is WeMod, where you can find a trainer for Lethis - Path Of Progress that has 5 cheats and supports Steam. You can download the WeMod app and use it to cheat in this game and more. Another website is GameSpot, where you can find some information and walkthroughs for Lethis - Path Of Progress. You can also find some cheats and codes for this game on their website. Another website is Chapter Cheats, where you can find some cheats, tips, tricks, and secrets for Lethis - Path Of Progress. You can also ask questions and get answers from other players on their website.</p>
-<p></p>
-<h2>Is it safe to use Lethis - Path Of Progress cheat code for pc?</h2>
-<p>Using Lethis - Path Of Progress cheat code for pc is generally safe, as long as you use them from reliable sources and at your own risk. However, there are some risks and consequences that you should be aware of before using them. Some of these risks and consequences are:</p>
-<ul>
-<li>You may get banned or penalized by Steam or the game developers if you use cheats online or in multiplayer mode.</li>
-<li>You may damage or corrupt your game files or your computer system if you use cheats from untrusted sources or if you install them incorrectly.</li>
-<li>You may lose your game progress or your save files if you use cheats that affect your game data or if you overwrite them by mistake.</li>
-<li>You may experience some technical issues or glitches with your game or your computer if you use cheats that are incompatible with your game version or your system specifications.</li>
-</ul>
-<p>Therefore, you should always backup your game files and your computer system before using any cheats. You should also scan any files that you download from the internet with an antivirus program. You should also read the instructions and reviews of any cheats that you use carefully. You should also avoid using cheats online or in multiplayer mode to respect other players and avoid getting banned or penalized.</p>
-<h2>How to get more out of Lethis - Path Of Progress cheat code for pc?</h2>
-<p>If you want to get more out of Lethis - Path Of Progress cheat code for pc, you can try some of these tips and tricks:</p>
-<ul>
-<li>You can use the cheat codes to explore different aspects of the game and see how they affect your gameplay. For example, you can use the WelcomeToBritanny cheat to see how beer affects your citizens' happiness and productivity.</li>
-<li>You can use the cheat codes to test your skills and strategies and see how you can improve them. For example, you can use the WinWinSubmarine cheat to see how you can complete a mission faster or with fewer resources.</li>
-<li>You can use the cheat codes to have some fun and humor and see how they change the game atmosphere. For example, you can use the TrickOrTreat cheat to see how pumpkins look like in your granaries and how your citizens react to them.</li>
-<li>You can use the cheat codes to unlock some achievements that may be hard or impossible to get otherwise. For example, you can use the Ambrosia and GuybrushThreepwood cheats to unlock the Ambrosia and I'm a Pirate! achievements.</li>
-<li>You can use the cheat codes to discover some hidden features and options that may not be available in the normal gameplay. For example, you can use the Fest-Noz cheat to activate or deactivate the happiness system and see how it affects your game balance and difficulty.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Lethis - Path Of Progress is a city-building game that takes place in a steampunk world. You have to manage your resources, build your infrastructure, and satisfy your citizens' needs. The game also has a cheat system that allows you to access some hidden features and options. To use Lethis - Path Of Progress cheat code for pc, you need to press F7 to open the cheat console, type one of the cheat codes, and press Enter to activate it. Using Lethis - Path Of Progress cheat code for pc has some benefits and drawbacks for your gaming experience. You can have more fun and experiment with different scenarios and outcomes, but you may also lose some of the challenge and satisfaction that comes from playing the game legitimately. You can unlock some achievements that may be hard or impossible to get otherwise, but you may also spoil some of the surprises and secrets that the game has to offer. You can discover some hidden features and options that may not be available in the normal gameplay, but you may also encounter some bugs or errors that may affect your game performance or stability. You can affect your game balance and difficulty by using some cheats that may make the game too easy or too hard, but you may also overcome some challenges and difficulties that you may encounter in the game. Whether you use Lethis - Path Of Progress cheat code for pc or not is up to you, but remember to have fun and enjoy the game!</p>
-<h2>Conclusion</h2>
-<p>Lethis - Path Of Progress is a city-building game that takes place in a steampunk world. You have to manage your resources, build your infrastructure, and satisfy your citizens' needs. The game also has a cheat system that allows you to access some hidden features and options. To use Lethis - Path Of Progress cheat code for pc, you need to press F7 to open the cheat console, type one of the cheat codes, and press Enter to activate it. Using Lethis - Path Of Progress cheat code for pc has some benefits and drawbacks for your gaming experience. You can have more fun and experiment with different scenarios and outcomes, but you may also lose some of the challenge and satisfaction that comes from playing the game legitimately. You can unlock some achievements that may be hard or impossible to get otherwise, but you may also spoil some of the surprises and secrets that the game has to offer. You can discover some hidden features and options that may not be available in the normal gameplay, but you may also encounter some bugs or errors that may affect your game performance or stability. You can affect your game balance and difficulty by using some cheats that may make the game too easy or too hard, but you may also overcome some challenges and difficulties that you may encounter in the game. Whether you use Lethis - Path Of Progress cheat code for pc or not is up to you, but remember to have fun and enjoy the game!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/symbols.py b/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-
diff --git a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/sabl_retina_head.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/sabl_retina_head.py
deleted file mode 100644
index 4211622cb8b4fe807230a89bcaab8f4f1681bfc0..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/sabl_retina_head.py
+++ /dev/null
@@ -1,621 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (build_anchor_generator, build_assigner,
-                        build_bbox_coder, build_sampler, images_to_levels,
-                        multi_apply, multiclass_nms, unmap)
-from ..builder import HEADS, build_loss
-from .base_dense_head import BaseDenseHead
-from .guided_anchor_head import GuidedAnchorHead
-
-
-@HEADS.register_module()
-class SABLRetinaHead(BaseDenseHead):
-    """Side-Aware Boundary Localization (SABL) for RetinaNet.
-
-    The anchor generation, assigning and sampling in SABLRetinaHead
-    are the same as GuidedAnchorHead for guided anchoring.
-
-    Please refer to https://arxiv.org/abs/1912.04260 for more details.
-
-    Args:
-        num_classes (int): Number of classes.
-        in_channels (int): Number of channels in the input feature map.
-        stacked_convs (int): Number of Convs for classification \
-            and regression branches. Defaults to 4.
-        feat_channels (int): Number of hidden channels. \
-            Defaults to 256.
-        approx_anchor_generator (dict): Config dict for approx generator.
-        square_anchor_generator (dict): Config dict for square generator.
-        conv_cfg (dict): Config dict for ConvModule. Defaults to None.
-        norm_cfg (dict): Config dict for Norm Layer. Defaults to None.
-        bbox_coder (dict): Config dict for bbox coder.
-        reg_decoded_bbox (bool): If true, the regression loss would be
-            applied directly on decoded bounding boxes, converting both
-            the predicted boxes and regression targets to absolute
-            coordinates format. Default False. It should be `True` when
-            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
-        train_cfg (dict): Training config of SABLRetinaHead.
-        test_cfg (dict): Testing config of SABLRetinaHead.
-        loss_cls (dict): Config of classification loss.
-        loss_bbox_cls (dict): Config of classification loss for bbox branch.
-        loss_bbox_reg (dict): Config of regression loss for bbox branch.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 feat_channels=256,
-                 approx_anchor_generator=dict(
-                     type='AnchorGenerator',
-                     octave_base_scale=4,
-                     scales_per_octave=3,
-                     ratios=[0.5, 1.0, 2.0],
-                     strides=[8, 16, 32, 64, 128]),
-                 square_anchor_generator=dict(
-                     type='AnchorGenerator',
-                     ratios=[1.0],
-                     scales=[4],
-                     strides=[8, 16, 32, 64, 128]),
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 bbox_coder=dict(
-                     type='BucketingBBoxCoder',
-                     num_buckets=14,
-                     scale_factor=3.0),
-                 reg_decoded_bbox=False,
-                 train_cfg=None,
-                 test_cfg=None,
-                 loss_cls=dict(
-                     type='FocalLoss',
-                     use_sigmoid=True,
-                     gamma=2.0,
-                     alpha=0.25,
-                     loss_weight=1.0),
-                 loss_bbox_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.5),
-                 loss_bbox_reg=dict(
-                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)):
-        super(SABLRetinaHead, self).__init__()
-        self.in_channels = in_channels
-        self.num_classes = num_classes
-        self.feat_channels = feat_channels
-        self.num_buckets = bbox_coder['num_buckets']
-        self.side_num = int(np.ceil(self.num_buckets / 2))
-
-        assert (approx_anchor_generator['octave_base_scale'] ==
-                square_anchor_generator['scales'][0])
-        assert (approx_anchor_generator['strides'] ==
-                square_anchor_generator['strides'])
-
-        self.approx_anchor_generator = build_anchor_generator(
-            approx_anchor_generator)
-        self.square_anchor_generator = build_anchor_generator(
-            square_anchor_generator)
-        self.approxs_per_octave = (
-            self.approx_anchor_generator.num_base_anchors[0])
-
-        # one anchor per location
-        self.num_anchors = 1
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-
-        self.reg_decoded_bbox = reg_decoded_bbox
-
-        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-        self.sampling = loss_cls['type'] not in [
-            'FocalLoss', 'GHMC', 'QualityFocalLoss'
-        ]
-        if self.use_sigmoid_cls:
-            self.cls_out_channels = num_classes
-        else:
-            self.cls_out_channels = num_classes + 1
-
-        self.bbox_coder = build_bbox_coder(bbox_coder)
-        self.loss_cls = build_loss(loss_cls)
-        self.loss_bbox_cls = build_loss(loss_bbox_cls)
-        self.loss_bbox_reg = build_loss(loss_bbox_reg)
-
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # use PseudoSampler when sampling is False
-            if self.sampling and hasattr(self.train_cfg, 'sampler'):
-                sampler_cfg = self.train_cfg.sampler
-            else:
-                sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-
-        self.fp16_enabled = False
-        self._init_layers()
-
-    def _init_layers(self):
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.retina_cls = nn.Conv2d(
-            self.feat_channels, self.cls_out_channels, 3, padding=1)
-        self.retina_bbox_reg = nn.Conv2d(
-            self.feat_channels, self.side_num * 4, 3, padding=1)
-        self.retina_bbox_cls = nn.Conv2d(
-            self.feat_channels, self.side_num * 4, 3, padding=1)
-
-    def init_weights(self):
-        for m in self.cls_convs:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.retina_cls, std=0.01, bias=bias_cls)
-        normal_init(self.retina_bbox_reg, std=0.01)
-        normal_init(self.retina_bbox_cls, std=0.01)
-
-    def forward_single(self, x):
-        cls_feat = x
-        reg_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            reg_feat = reg_conv(reg_feat)
-        cls_score = self.retina_cls(cls_feat)
-        bbox_cls_pred = self.retina_bbox_cls(reg_feat)
-        bbox_reg_pred = self.retina_bbox_reg(reg_feat)
-        bbox_pred = (bbox_cls_pred, bbox_reg_pred)
-        return cls_score, bbox_pred
-
-    def forward(self, feats):
-        return multi_apply(self.forward_single, feats)
-
-    def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
-        """Get squares according to feature map sizes and guided anchors.
-
-        Args:
-            featmap_sizes (list[tuple]): Multi-level feature map sizes.
-            img_metas (list[dict]): Image meta info.
-            device (torch.device | str): device for returned tensors
-
-        Returns:
-            tuple: square approxs of each image
-        """
-        num_imgs = len(img_metas)
-
-        # since feature map sizes of all images are the same, we only compute
-        # squares for one time
-        multi_level_squares = self.square_anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-        squares_list = [multi_level_squares for _ in range(num_imgs)]
-
-        return squares_list
-
-    def get_target(self,
-                   approx_list,
-                   inside_flag_list,
-                   square_list,
-                   gt_bboxes_list,
-                   img_metas,
-                   gt_bboxes_ignore_list=None,
-                   gt_labels_list=None,
-                   label_channels=None,
-                   sampling=True,
-                   unmap_outputs=True):
-        """Compute bucketing targets.
-        Args:
-            approx_list (list[list]): Multi level approxs of each image.
-            inside_flag_list (list[list]): Multi level inside flags of each
-                image.
-            square_list (list[list]): Multi level squares of each image.
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
-            gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
-            label_channels (int): Channel of label.
-            sampling (bool): Sample Anchors or not.
-            unmap_outputs (bool): unmap outputs or not.
-
-        Returns:
-            tuple: Returns a tuple containing learning targets.
-
-                - labels_list (list[Tensor]): Labels of each level.
-                - label_weights_list (list[Tensor]): Label weights of each \
-                    level.
-                - bbox_cls_targets_list (list[Tensor]): BBox cls targets of \
-                    each level.
-                - bbox_cls_weights_list (list[Tensor]): BBox cls weights of \
-                    each level.
-                - bbox_reg_targets_list (list[Tensor]): BBox reg targets of \
-                    each level.
-                - bbox_reg_weights_list (list[Tensor]): BBox reg weights of \
-                    each level.
-                - num_total_pos (int): Number of positive samples in all \
-                    images.
-                - num_total_neg (int): Number of negative samples in all \
-                    images.
-        """
-        num_imgs = len(img_metas)
-        assert len(approx_list) == len(inside_flag_list) == len(
-            square_list) == num_imgs
-        # anchor number of multi levels
-        num_level_squares = [squares.size(0) for squares in square_list[0]]
-        # concat all level anchors and flags to a single tensor
-        inside_flag_flat_list = []
-        approx_flat_list = []
-        square_flat_list = []
-        for i in range(num_imgs):
-            assert len(square_list[i]) == len(inside_flag_list[i])
-            inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
-            approx_flat_list.append(torch.cat(approx_list[i]))
-            square_flat_list.append(torch.cat(square_list[i]))
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_labels, all_label_weights, all_bbox_cls_targets,
-         all_bbox_cls_weights, all_bbox_reg_targets, all_bbox_reg_weights,
-         pos_inds_list, neg_inds_list) = multi_apply(
-             self._get_target_single,
-             approx_flat_list,
-             inside_flag_flat_list,
-             square_flat_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             img_metas,
-             label_channels=label_channels,
-             sampling=sampling,
-             unmap_outputs=unmap_outputs)
-        # no valid anchors
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        labels_list = images_to_levels(all_labels, num_level_squares)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_squares)
-        bbox_cls_targets_list = images_to_levels(all_bbox_cls_targets,
-                                                 num_level_squares)
-        bbox_cls_weights_list = images_to_levels(all_bbox_cls_weights,
-                                                 num_level_squares)
-        bbox_reg_targets_list = images_to_levels(all_bbox_reg_targets,
-                                                 num_level_squares)
-        bbox_reg_weights_list = images_to_levels(all_bbox_reg_weights,
-                                                 num_level_squares)
-        return (labels_list, label_weights_list, bbox_cls_targets_list,
-                bbox_cls_weights_list, bbox_reg_targets_list,
-                bbox_reg_weights_list, num_total_pos, num_total_neg)
-
-    def _get_target_single(self,
-                           flat_approxs,
-                           inside_flags,
-                           flat_squares,
-                           gt_bboxes,
-                           gt_bboxes_ignore,
-                           gt_labels,
-                           img_meta,
-                           label_channels=None,
-                           sampling=True,
-                           unmap_outputs=True):
-        """Compute regression and classification targets for anchors in a
-        single image.
-
-        Args:
-            flat_approxs (Tensor): flat approxs of a single image,
-                shape (n, 4)
-            inside_flags (Tensor): inside flags of a single image,
-                shape (n, ).
-            flat_squares (Tensor): flat squares of a single image,
-                shape (approxs_per_octave * n, 4)
-            gt_bboxes (Tensor): Ground truth bboxes of a single image, \
-                shape (num_gts, 4).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            img_meta (dict): Meta info of the image.
-            label_channels (int): Channel of label.
-            sampling (bool): Sample Anchors or not.
-            unmap_outputs (bool): unmap outputs or not.
-
-        Returns:
-            tuple:
-
-                - labels_list (Tensor): Labels in a single image
-                - label_weights (Tensor): Label weights in a single image
-                - bbox_cls_targets (Tensor): BBox cls targets in a single image
-                - bbox_cls_weights (Tensor): BBox cls weights in a single image
-                - bbox_reg_targets (Tensor): BBox reg targets in a single image
-                - bbox_reg_weights (Tensor): BBox reg weights in a single image
-                - num_total_pos (int): Number of positive samples \
-                    in a single image
-                - num_total_neg (int): Number of negative samples \
-                    in a single image
-        """
-        if not inside_flags.any():
-            return (None, ) * 8
-        # assign gt and sample anchors
-        expand_inside_flags = inside_flags[:, None].expand(
-            -1, self.approxs_per_octave).reshape(-1)
-        approxs = flat_approxs[expand_inside_flags, :]
-        squares = flat_squares[inside_flags, :]
-
-        assign_result = self.assigner.assign(approxs, squares,
-                                             self.approxs_per_octave,
-                                             gt_bboxes, gt_bboxes_ignore)
-        sampling_result = self.sampler.sample(assign_result, squares,
-                                              gt_bboxes)
-
-        num_valid_squares = squares.shape[0]
-        bbox_cls_targets = squares.new_zeros(
-            (num_valid_squares, self.side_num * 4))
-        bbox_cls_weights = squares.new_zeros(
-            (num_valid_squares, self.side_num * 4))
-        bbox_reg_targets = squares.new_zeros(
-            (num_valid_squares, self.side_num * 4))
-        bbox_reg_weights = squares.new_zeros(
-            (num_valid_squares, self.side_num * 4))
-        labels = squares.new_full((num_valid_squares, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = squares.new_zeros(num_valid_squares, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            (pos_bbox_reg_targets, pos_bbox_reg_weights, pos_bbox_cls_targets,
-             pos_bbox_cls_weights) = self.bbox_coder.encode(
-                 sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-
-            bbox_cls_targets[pos_inds, :] = pos_bbox_cls_targets
-            bbox_reg_targets[pos_inds, :] = pos_bbox_reg_targets
-            bbox_cls_weights[pos_inds, :] = pos_bbox_cls_weights
-            bbox_reg_weights[pos_inds, :] = pos_bbox_reg_weights
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_squares.size(0)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags, fill=self.num_classes)
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_cls_targets = unmap(bbox_cls_targets, num_total_anchors,
-                                     inside_flags)
-            bbox_cls_weights = unmap(bbox_cls_weights, num_total_anchors,
-                                     inside_flags)
-            bbox_reg_targets = unmap(bbox_reg_targets, num_total_anchors,
-                                     inside_flags)
-            bbox_reg_weights = unmap(bbox_reg_weights, num_total_anchors,
-                                     inside_flags)
-        return (labels, label_weights, bbox_cls_targets, bbox_cls_weights,
-                bbox_reg_targets, bbox_reg_weights, pos_inds, neg_inds)
-
-    def loss_single(self, cls_score, bbox_pred, labels, label_weights,
-                    bbox_cls_targets, bbox_cls_weights, bbox_reg_targets,
-                    bbox_reg_weights, num_total_samples):
-        # classification loss
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-        cls_score = cls_score.permute(0, 2, 3,
-                                      1).reshape(-1, self.cls_out_channels)
-        loss_cls = self.loss_cls(
-            cls_score, labels, label_weights, avg_factor=num_total_samples)
-        # regression loss
-        bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4)
-        bbox_cls_weights = bbox_cls_weights.reshape(-1, self.side_num * 4)
-        bbox_reg_targets = bbox_reg_targets.reshape(-1, self.side_num * 4)
-        bbox_reg_weights = bbox_reg_weights.reshape(-1, self.side_num * 4)
-        (bbox_cls_pred, bbox_reg_pred) = bbox_pred
-        bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(
-            -1, self.side_num * 4)
-        bbox_reg_pred = bbox_reg_pred.permute(0, 2, 3, 1).reshape(
-            -1, self.side_num * 4)
-        loss_bbox_cls = self.loss_bbox_cls(
-            bbox_cls_pred,
-            bbox_cls_targets.long(),
-            bbox_cls_weights,
-            avg_factor=num_total_samples * 4 * self.side_num)
-        loss_bbox_reg = self.loss_bbox_reg(
-            bbox_reg_pred,
-            bbox_reg_targets,
-            bbox_reg_weights,
-            avg_factor=num_total_samples * 4 * self.bbox_coder.offset_topk)
-        return loss_cls, loss_bbox_cls, loss_bbox_reg
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
-
-        device = cls_scores[0].device
-
-        # get sampled approxes
-        approxs_list, inside_flag_list = GuidedAnchorHead.get_sampled_approxs(
-            self, featmap_sizes, img_metas, device=device)
-
-        square_list = self.get_anchors(featmap_sizes, img_metas, device=device)
-
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_target(
-            approxs_list,
-            inside_flag_list,
-            square_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-            sampling=self.sampling)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_cls_targets_list,
-         bbox_cls_weights_list, bbox_reg_targets_list, bbox_reg_weights_list,
-         num_total_pos, num_total_neg) = cls_reg_targets
-        num_total_samples = (
-            num_total_pos + num_total_neg if self.sampling else num_total_pos)
-        losses_cls, losses_bbox_cls, losses_bbox_reg = multi_apply(
-            self.loss_single,
-            cls_scores,
-            bbox_preds,
-            labels_list,
-            label_weights_list,
-            bbox_cls_targets_list,
-            bbox_cls_weights_list,
-            bbox_reg_targets_list,
-            bbox_reg_weights_list,
-            num_total_samples=num_total_samples)
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox_cls=losses_bbox_cls,
-            loss_bbox_reg=losses_bbox_reg)
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   img_metas,
-                   cfg=None,
-                   rescale=False):
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-
-        device = cls_scores[0].device
-        mlvl_anchors = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        result_list = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_cls_pred_list = [
-                bbox_preds[i][0][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_reg_pred_list = [
-                bbox_preds[i][1][img_id].detach() for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            proposals = self.get_bboxes_single(cls_score_list,
-                                               bbox_cls_pred_list,
-                                               bbox_reg_pred_list,
-                                               mlvl_anchors[img_id], img_shape,
-                                               scale_factor, cfg, rescale)
-            result_list.append(proposals)
-        return result_list
-
-    def get_bboxes_single(self,
-                          cls_scores,
-                          bbox_cls_preds,
-                          bbox_reg_preds,
-                          mlvl_anchors,
-                          img_shape,
-                          scale_factor,
-                          cfg,
-                          rescale=False):
-        cfg = self.test_cfg if cfg is None else cfg
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_confids = []
-        assert len(cls_scores) == len(bbox_cls_preds) == len(
-            bbox_reg_preds) == len(mlvl_anchors)
-        for cls_score, bbox_cls_pred, bbox_reg_pred, anchors in zip(
-                cls_scores, bbox_cls_preds, bbox_reg_preds, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_cls_pred.size(
-            )[-2:] == bbox_reg_pred.size()[-2::]
-            cls_score = cls_score.permute(1, 2,
-                                          0).reshape(-1, self.cls_out_channels)
-            if self.use_sigmoid_cls:
-                scores = cls_score.sigmoid()
-            else:
-                scores = cls_score.softmax(-1)
-            bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(
-                -1, self.side_num * 4)
-            bbox_reg_pred = bbox_reg_pred.permute(1, 2, 0).reshape(
-                -1, self.side_num * 4)
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[0] > nms_pre:
-                if self.use_sigmoid_cls:
-                    max_scores, _ = scores.max(dim=1)
-                else:
-                    max_scores, _ = scores[:, :-1].max(dim=1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                anchors = anchors[topk_inds, :]
-                bbox_cls_pred = bbox_cls_pred[topk_inds, :]
-                bbox_reg_pred = bbox_reg_pred[topk_inds, :]
-                scores = scores[topk_inds, :]
-            bbox_preds = [
-                bbox_cls_pred.contiguous(),
-                bbox_reg_pred.contiguous()
-            ]
-            bboxes, confids = self.bbox_coder.decode(
-                anchors.contiguous(), bbox_preds, max_shape=img_shape)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_confids.append(confids)
-        mlvl_bboxes = torch.cat(mlvl_bboxes)
-        if rescale:
-            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
-        mlvl_scores = torch.cat(mlvl_scores)
-        mlvl_confids = torch.cat(mlvl_confids)
-        if self.use_sigmoid_cls:
-            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
-            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
-        det_bboxes, det_labels = multiclass_nms(
-            mlvl_bboxes,
-            mlvl_scores,
-            cfg.score_thr,
-            cfg.nms,
-            cfg.max_per_img,
-            score_factors=mlvl_confids)
-        return det_bboxes, det_labels
diff --git a/spaces/dirge/voicevox/voicevox_engine/setting/SettingLoader.py b/spaces/dirge/voicevox/voicevox_engine/setting/SettingLoader.py
deleted file mode 100644
index a78952e96835f5bcf2b4fee23d38312dfa2ca573..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/voicevox_engine/setting/SettingLoader.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from pathlib import Path
-
-import yaml
-
-from ..utility import engine_root, get_save_dir
-from .Setting import Setting
-
-DEFAULT_SETTING_PATH: Path = engine_root() / "default_setting.yml"
-USER_SETTING_PATH: Path = get_save_dir() / "setting.yml"
-
-
-class SettingLoader:
-    def __init__(self, setting_file_path: Path) -> None:
-        self.setting_file_path = setting_file_path
-
-    def load_setting_file(self) -> Setting:
-        if not self.setting_file_path.is_file():
-            setting = yaml.safe_load(DEFAULT_SETTING_PATH.read_text(encoding="utf-8"))
-        else:
-            setting = yaml.safe_load(self.setting_file_path.read_text(encoding="utf-8"))
-
-        setting = Setting(
-            cors_policy_mode=setting["cors_policy_mode"],
-            allow_origin=setting["allow_origin"],
-        )
-
-        return setting
-
-    def dump_setting_file(self, settings: Setting) -> None:
-        settings_dict = settings.dict()
-
-        with open(self.setting_file_path, mode="w", encoding="utf-8") as f:
-            yaml.safe_dump(settings_dict, f)
diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/util/logger.py b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/util/logger.py
deleted file mode 100644
index 18145f54c927abd59b95f3fa6e6da8002bc2ce97..0000000000000000000000000000000000000000
--- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/util/logger.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import functools
-import logging
-import os
-import sys
-
-from termcolor import colored
-
-
-class _ColorfulFormatter(logging.Formatter):
-    def __init__(self, *args, **kwargs):
-        self._root_name = kwargs.pop("root_name") + "."
-        self._abbrev_name = kwargs.pop("abbrev_name", "")
-        if len(self._abbrev_name):
-            self._abbrev_name = self._abbrev_name + "."
-        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
-
-    def formatMessage(self, record):
-        record.name = record.name.replace(self._root_name, self._abbrev_name)
-        log = super(_ColorfulFormatter, self).formatMessage(record)
-        if record.levelno == logging.WARNING:
-            prefix = colored("WARNING", "red", attrs=["blink"])
-        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
-            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
-        else:
-            return log
-        return prefix + " " + log
-
-
-# so that calling setup_logger multiple times won't add many handlers
-@functools.lru_cache()
-def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
-    """
-    Initialize the detectron2 logger and set its verbosity level to "INFO".
-
-    Args:
-        output (str): a file name or a directory to save log. If None, will not save log file.
-            If ends with ".txt" or ".log", assumed to be a file name.
-            Otherwise, logs will be saved to `output/log.txt`.
-        name (str): the root module name of this logger
-
-    Returns:
-        logging.Logger: a logger
-    """
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-
-    if abbrev_name is None:
-        abbrev_name = name
-
-    plain_formatter = logging.Formatter(
-        "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
-    )
-    # stdout logging: master only
-    if distributed_rank == 0:
-        ch = logging.StreamHandler(stream=sys.stdout)
-        ch.setLevel(logging.DEBUG)
-        if color:
-            formatter = _ColorfulFormatter(
-                colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
-                datefmt="%m/%d %H:%M:%S",
-                root_name=name,
-                abbrev_name=str(abbrev_name),
-            )
-        else:
-            formatter = plain_formatter
-        ch.setFormatter(formatter)
-        logger.addHandler(ch)
-
-    # file logging: all workers
-    if output is not None:
-        if output.endswith(".txt") or output.endswith(".log"):
-            filename = output
-        else:
-            filename = os.path.join(output, "log.txt")
-        if distributed_rank > 0:
-            filename = filename + f".rank{distributed_rank}"
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
-
-        fh = logging.StreamHandler(_cached_log_stream(filename))
-        fh.setLevel(logging.DEBUG)
-        fh.setFormatter(plain_formatter)
-        logger.addHandler(fh)
-
-    return logger
-
-
-# cache the opened file object, so that different calls to `setup_logger`
-# with the same file name can safely write to the same file.
-@functools.lru_cache(maxsize=None)
-def _cached_log_stream(filename):
-    return open(filename, "a")
diff --git a/spaces/eddydecena/cat-vs-dog/src/preprocessing.py b/spaces/eddydecena/cat-vs-dog/src/preprocessing.py
deleted file mode 100644
index e5f6c055b85dbc113e5a65e9fa1f5c77a746e92c..0000000000000000000000000000000000000000
--- a/spaces/eddydecena/cat-vs-dog/src/preprocessing.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os
-from typing import Tuple
-
-import tensorflow as tf
-
-def delete_corrupted_image(dataset_path: str, categories: Tuple[str]) -> int:
-    num_skipped = 0
-    
-    for folder_name in categories:
-        folder_path = os.path.join(dataset_path, folder_name)
-        for fname in os.listdir(folder_path):
-            fpath = os.path.join(folder_path, fname)
-            try:
-                fobj = open(fpath, 'rb')
-                is_jfif = tf.compat.as_bytes("JFIF") in fobj.peek(10)
-            finally:
-                fobj.close()
-
-            if not is_jfif:
-                num_skipped += 1
-                os.remove(fpath)
-            
-    return num_skipped
-
-def get_data_augmentation() -> tf.keras.Sequential:
-    return tf.keras.Sequential([
-        tf.keras.layers.RandomFlip('horizontal'),
-        tf.keras.layers.RandomRotation(0.1)
-    ])
\ No newline at end of file
diff --git a/spaces/evaluate-metric/precision/app.py b/spaces/evaluate-metric/precision/app.py
deleted file mode 100644
index ec80edf5a54da759b8e1509650826f0a8e8b8bd7..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/precision/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("precision")
-launch_gradio_widget(module)
diff --git a/spaces/evaluate-metric/rl_reliability/app.py b/spaces/evaluate-metric/rl_reliability/app.py
deleted file mode 100644
index 58f118ded69c2b589eb1ab89e9c5fd93ab7eb41d..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/rl_reliability/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("rl_reliability", "online")
-launch_gradio_widget(module)
diff --git a/spaces/f2api/gpt-academic/docs/waifu_plugin/waifu-tips.js b/spaces/f2api/gpt-academic/docs/waifu_plugin/waifu-tips.js
deleted file mode 100644
index 8f9533a19e7d4914bde888ee2a107e4430242968..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/docs/waifu_plugin/waifu-tips.js
+++ /dev/null
@@ -1,405 +0,0 @@
-window.live2d_settings = Array(); /*
-
-    く__,.ヘヽ.　　　　/　,ー､ 〉
-    　　　　　＼ ', !-─‐-i　/　/´
-    　　　 　 ／｀ｰ'　　　 L/／｀ヽ､            Live2D 看板娘 参数设置
-    　　 　 /　 ／,　 /|　 ,　 ,　　　 ',                                           Version 1.4.2
-    　　　ｲ 　/ /-‐/　ｉ　L_ ﾊ ヽ!　 i                            Update 2018.11.12
-    　　　 ﾚ ﾍ 7ｲ｀ﾄ　 ﾚ'ｧ-ﾄ､!ハ|　 |  
-    　　　　 !,/7 '0'　　 ´0iソ| 　 |　　　
-    　　　　 |.从"　　_　　 ,,,, / |./ 　 |             网页添加 Live2D 看板娘
-    　　　　 ﾚ'| i＞.､,,__　_,.イ / 　.i 　|                    https://www.fghrsh.net/post/123.html
-    　　　　　 ﾚ'| | / k_７_/ﾚ'ヽ,　ﾊ.　|           
-    　　　　　　 | |/i 〈|/　 i　,.ﾍ |　i　|    Thanks
-    　　　　　　.|/ /　ｉ： 　 ﾍ!　　＼　|          journey-ad / https://github.com/journey-ad/live2d_src
-    　　　 　 　 kヽ>､ﾊ 　 _,.ﾍ､ 　 /､!            xiazeyu / https://github.com/xiazeyu/live2d-widget.js
-    　　　　　　 !'〈//｀Ｔ´', ＼ ｀'7'ｰr'          Live2d Cubism SDK WebGL 2.1 Projrct & All model authors.
-    　　　　　　 ﾚ'ヽL__|___i,___,ンﾚ|ノ
-    　　　　　 　　　ﾄ-,/　|___./
-    　　　　　 　　　'ｰ'　　!_,.:*********************************************************************************/
-
-
-// 后端接口
-live2d_settings['modelAPI']             = '//live2d.fghrsh.net/api/';   // 自建 API 修改这里
-live2d_settings['tipsMessage']          = 'waifu-tips.json';            // 同目录下可省略路径
-live2d_settings['hitokotoAPI']          = 'lwl12.com';                  // 一言 API，可选 'lwl12.com', 'hitokoto.cn', 'jinrishici.com'(古诗词)
-
-// 默认模型
-live2d_settings['modelId']              = 1;            // 默认模型 ID，可在 F12 控制台找到
-live2d_settings['modelTexturesId']      = 53;           // 默认材质 ID，可在 F12 控制台找到
-
-// 工具栏设置
-live2d_settings['showToolMenu']         = true;         // 显示 工具栏          ，可选 true(真), false(假)
-live2d_settings['canCloseLive2d']       = true;         // 显示 关闭看板娘  按钮，可选 true(真), false(假)
-live2d_settings['canSwitchModel']       = true;         // 显示 模型切换    按钮，可选 true(真), false(假)
-live2d_settings['canSwitchTextures']    = true;         // 显示 材质切换    按钮，可选 true(真), false(假)
-live2d_settings['canSwitchHitokoto']    = true;         // 显示 一言切换    按钮，可选 true(真), false(假)
-live2d_settings['canTakeScreenshot']    = true;         // 显示 看板娘截图  按钮，可选 true(真), false(假)
-live2d_settings['canTurnToHomePage']    = true;         // 显示 返回首页    按钮，可选 true(真), false(假)
-live2d_settings['canTurnToAboutPage']   = true;         // 显示 跳转关于页  按钮，可选 true(真), false(假)
-
-// 模型切换模式
-live2d_settings['modelStorage']         = true;         // 记录 ID (刷新后恢复)，可选 true(真), false(假)
-live2d_settings['modelRandMode']        = 'switch';     // 模型切换，可选 'rand'(随机), 'switch'(顺序)
-live2d_settings['modelTexturesRandMode']= 'rand';       // 材质切换，可选 'rand'(随机), 'switch'(顺序)
-
-// 提示消息选项
-live2d_settings['showHitokoto']         = true;         // 显示一言
-live2d_settings['showF12Status']        = true;         // 显示加载状态
-live2d_settings['showF12Message']       = false;        // 显示看板娘消息
-live2d_settings['showF12OpenMsg']       = true;         // 显示控制台打开提示
-live2d_settings['showCopyMessage']      = true;         // 显示 复制内容 提示
-live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
-
-//看板娘样式设置
-live2d_settings['waifuSize']            = '280x250';    // 看板娘大小，例如 '280x250', '600x535'
-live2d_settings['waifuTipsSize']        = '250x70';     // 提示框大小，例如 '250x70', '570x150'
-live2d_settings['waifuFontSize']        = '12px';       // 提示框字体，例如 '12px', '30px'
-live2d_settings['waifuToolFont']        = '14px';       // 工具栏字体，例如 '14px', '36px'
-live2d_settings['waifuToolLine']        = '20px';       // 工具栏行高，例如 '20px', '36px'
-live2d_settings['waifuToolTop']         = '0px'         // 工具栏顶部边距，例如 '0px', '-60px'
-live2d_settings['waifuMinWidth']        = '768px';      // 面页小于 指定宽度 隐藏看板娘，例如 'disable'(禁用), '768px'
-live2d_settings['waifuEdgeSide']        = 'left:0';     // 看板娘贴边方向，例如 'left:0'(靠左 0px), 'right:30'(靠右 30px)
-live2d_settings['waifuDraggable']       = 'disable';    // 拖拽样式，例如 'disable'(禁用), 'axis-x'(只能水平拖拽), 'unlimited'(自由拖拽)
-live2d_settings['waifuDraggableRevert'] = true;         // 松开鼠标还原拖拽位置，可选 true(真), false(假)
-
-// 其他杂项设置
-live2d_settings['l2dVersion']           = '1.4.2';        // 当前版本
-live2d_settings['l2dVerDate']           = '2018.11.12'; // 版本更新日期
-live2d_settings['homePageUrl']          = 'auto';       // 主页地址，可选 'auto'(自动), '{URL 网址}'
-live2d_settings['aboutPageUrl']         = 'https://www.fghrsh.net/post/123.html';   // 关于页地址, '{URL 网址}'
-live2d_settings['screenshotCaptureName']= 'live2d.png'; // 看板娘截图文件名，例如 'live2d.png'
-
-/****************************************************************************************************/
-
-String.prototype.render = function(context) {
-    var tokenReg = /(\\)?\{([^\{\}\\]+)(\\)?\}/g;
-
-    return this.replace(tokenReg, function (word, slash1, token, slash2) {
-        if (slash1 || slash2) { return word.replace('\\', ''); }
-        
-        var variables = token.replace(/\s/g, '').split('.');
-        var currentObject = context;
-        var i, length, variable;
-        
-        for (i = 0, length = variables.length; i < length; ++i) {
-            variable = variables[i];
-            currentObject = currentObject[variable];
-            if (currentObject === undefined || currentObject === null) return '';
-        }
-        return currentObject;
-    });
-};
-
-var re = /x/;
-console.log(re);
-
-function empty(obj) {return typeof obj=="undefined"||obj==null||obj==""?true:false}
-function getRandText(text) {return Array.isArray(text) ? text[Math.floor(Math.random() * text.length + 1)-1] : text}
-
-function showMessage(text, timeout, flag) {
-    if(flag || sessionStorage.getItem('waifu-text') === '' || sessionStorage.getItem('waifu-text') === null){
-        if(Array.isArray(text)) text = text[Math.floor(Math.random() * text.length + 1)-1];
-        if (live2d_settings.showF12Message) console.log('[Message]', text.replace(/<[^<>]+>/g,''));
-        
-        if(flag) sessionStorage.setItem('waifu-text', text);
-        
-        $('.waifu-tips').stop();
-        $('.waifu-tips').html(text).fadeTo(200, 1);
-        if (timeout === undefined) timeout = 5000;
-        hideMessage(timeout);
-    }
-}
-
-function hideMessage(timeout) {
-    $('.waifu-tips').stop().css('opacity',1);
-    if (timeout === undefined) timeout = 5000;
-    window.setTimeout(function() {sessionStorage.removeItem('waifu-text')}, timeout);
-    $('.waifu-tips').delay(timeout).fadeTo(200, 0);
-}
-
-function initModel(waifuPath, type) {
-    /* console welcome message */
-    eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('8.d(" ");8.d("\\U,.\\y\\5.\\1\\1\\1\\1/\\1,\\u\\2 \\H\\n\\1\\1\\1\\1\\1\\b \', !-\\r\\j-i\\1/\\1/\\g\\n\\1\\1\\1 \\1 \\a\\4\\f\'\\1\\1\\1 L/\\a\\4\\5\\2\\n\\1\\1 \\1 /\\1 \\a,\\1 /|\\1 ,\\1 ,\\1\\1\\1 \',\\n\\1\\1\\1\\q \\1/ /-\\j/\\1\\h\\E \\9 \\5!\\1 i\\n\\1\\1\\1 \\3 \\6 7\\q\\4\\c\\1 \\3\'\\s-\\c\\2!\\t|\\1 |\\n\\1\\1\\1\\1 !,/7 \'0\'\\1\\1 \\X\\w| \\1 |\\1\\1\\1\\n\\1\\1\\1\\1 |.\\x\\"\\1\\l\\1\\1 ,,,, / |./ \\1 |\\n\\1\\1\\1\\1 \\3\'| i\\z.\\2,,A\\l,.\\B / \\1.i \\1|\\n\\1\\1\\1\\1\\1 \\3\'| | / C\\D/\\3\'\\5,\\1\\9.\\1|\\n\\1\\1\\1\\1\\1\\1 | |/i \\m|/\\1 i\\1,.\\6 |\\F\\1|\\n\\1\\1\\1\\1\\1\\1.|/ /\\1\\h\\G \\1 \\6!\\1\\1\\b\\1|\\n\\1\\1\\1 \\1 \\1 k\\5>\\2\\9 \\1 o,.\\6\\2 \\1 /\\2!\\n\\1\\1\\1\\1\\1\\1 !\'\\m//\\4\\I\\g\', \\b \\4\'7\'\\J\'\\n\\1\\1\\1\\1\\1\\1 \\3\'\\K|M,p,\\O\\3|\\P\\n\\1\\1\\1\\1\\1 \\1\\1\\1\\c-,/\\1|p./\\n\\1\\1\\1\\1\\1 \\1\\1\\1\'\\f\'\\1\\1!o,.:\\Q \\R\\S\\T v"+e.V+" / W "+e.N);8.d(" ");',60,60,'|u3000|uff64|uff9a|uff40|u30fd|uff8d||console|uff8a|uff0f|uff3c|uff84|log|live2d_settings|uff70|u00b4|uff49||u2010||u3000_|u3008||_|___|uff72|u2500|uff67|u30cf|u30fc||u30bd|u4ece|u30d8|uff1e|__|u30a4|k_|uff17_|u3000L_|u3000i|uff1a|u3009|uff34|uff70r|u30fdL__||___i|l2dVerDate|u30f3|u30ce|nLive2D|u770b|u677f|u5a18|u304f__|l2dVersion|FGHRSH|u00b40i'.split('|'),0,{}));
-    
-    /* 判断 JQuery */
-    if (typeof($.ajax) != 'function') typeof(jQuery.ajax) == 'function' ? window.$ = jQuery : console.log('[Error] JQuery is not defined.');
-    
-    /* 加载看板娘样式 */
-    live2d_settings.waifuSize = live2d_settings.waifuSize.split('x');
-    live2d_settings.waifuTipsSize = live2d_settings.waifuTipsSize.split('x');
-    live2d_settings.waifuEdgeSide = live2d_settings.waifuEdgeSide.split(':');
-    
-    $("#live2d").attr("width",live2d_settings.waifuSize[0]);
-    $("#live2d").attr("height",live2d_settings.waifuSize[1]);
-    $(".waifu-tips").width(live2d_settings.waifuTipsSize[0]);
-    $(".waifu-tips").height(live2d_settings.waifuTipsSize[1]);
-    $(".waifu-tips").css("top",live2d_settings.waifuToolTop);
-    $(".waifu-tips").css("font-size",live2d_settings.waifuFontSize);
-    $(".waifu-tool").css("font-size",live2d_settings.waifuToolFont);
-    $(".waifu-tool span").css("line-height",live2d_settings.waifuToolLine);
-    
-    if (live2d_settings.waifuEdgeSide[0] == 'left') $(".waifu").css("left",live2d_settings.waifuEdgeSide[1]+'px');
-    else if (live2d_settings.waifuEdgeSide[0] == 'right') $(".waifu").css("right",live2d_settings.waifuEdgeSide[1]+'px');
-    
-    window.waifuResize = function() { $(window).width() <= Number(live2d_settings.waifuMinWidth.replace('px','')) ? $(".waifu").hide() : $(".waifu").show(); };
-    if (live2d_settings.waifuMinWidth != 'disable') { waifuResize(); $(window).resize(function() {waifuResize()}); }
-    
-    try {
-        if (live2d_settings.waifuDraggable == 'axis-x') $(".waifu").draggable({ axis: "x", revert: live2d_settings.waifuDraggableRevert });
-        else if (live2d_settings.waifuDraggable == 'unlimited') $(".waifu").draggable({ revert: live2d_settings.waifuDraggableRevert });
-        else $(".waifu").css("transition", 'all .3s ease-in-out');
-    } catch(err) { console.log('[Error] JQuery UI is not defined.') }
-    
-    live2d_settings.homePageUrl = live2d_settings.homePageUrl == 'auto' ? window.location.protocol+'//'+window.location.hostname+'/' : live2d_settings.homePageUrl;
-    if (window.location.protocol == 'file:' && live2d_settings.modelAPI.substr(0,2) == '//') live2d_settings.modelAPI = 'http:'+live2d_settings.modelAPI;
-    
-    $('.waifu-tool .fui-home').click(function (){
-        //window.location = 'https://www.fghrsh.net/';
-        window.location = live2d_settings.homePageUrl;
-    });
-    
-    $('.waifu-tool .fui-info-circle').click(function (){
-        //window.open('https://imjad.cn/archives/lab/add-dynamic-poster-girl-with-live2d-to-your-blog-02');
-        window.open(live2d_settings.aboutPageUrl);
-    });
-    
-    if (typeof(waifuPath) == "object") loadTipsMessage(waifuPath); else {
-        $.ajax({
-            cache: true,
-            url: waifuPath == '' ? live2d_settings.tipsMessage : (waifuPath.substr(waifuPath.length-15)=='waifu-tips.json'?waifuPath:waifuPath+'waifu-tips.json'),
-            dataType: "json",
-            success: function (result){ loadTipsMessage(result); }
-        });
-    }
-    
-    if (!live2d_settings.showToolMenu) $('.waifu-tool').hide();
-    if (!live2d_settings.canCloseLive2d) $('.waifu-tool .fui-cross').hide();
-    if (!live2d_settings.canSwitchModel) $('.waifu-tool .fui-eye').hide();
-    if (!live2d_settings.canSwitchTextures) $('.waifu-tool .fui-user').hide();
-    if (!live2d_settings.canSwitchHitokoto) $('.waifu-tool .fui-chat').hide();
-    if (!live2d_settings.canTakeScreenshot) $('.waifu-tool .fui-photo').hide();
-    if (!live2d_settings.canTurnToHomePage) $('.waifu-tool .fui-home').hide();
-    if (!live2d_settings.canTurnToAboutPage) $('.waifu-tool .fui-info-circle').hide();
-
-    if (waifuPath === undefined) waifuPath = '';
-    var modelId = localStorage.getItem('modelId');
-    var modelTexturesId = localStorage.getItem('modelTexturesId');
-    
-    if (!live2d_settings.modelStorage || modelId == null) {
-        var modelId = live2d_settings.modelId;
-        var modelTexturesId = live2d_settings.modelTexturesId;
-    } loadModel(modelId, modelTexturesId);
-}
-
-function loadModel(modelId, modelTexturesId=0) {
-    if (live2d_settings.modelStorage) {
-        localStorage.setItem('modelId', modelId);
-        localStorage.setItem('modelTexturesId', modelTexturesId);
-    } else {
-        sessionStorage.setItem('modelId', modelId);
-        sessionStorage.setItem('modelTexturesId', modelTexturesId);
-    } loadlive2d('live2d', live2d_settings.modelAPI+'get/?id='+modelId+'-'+modelTexturesId, (live2d_settings.showF12Status ? console.log('[Status]','live2d','模型',modelId+'-'+modelTexturesId,'加载完成'):null));
-}
-
-function loadTipsMessage(result) {
-    window.waifu_tips = result;
-    
-    $.each(result.mouseover, function (index, tips){
-        $(document).on("mouseover", tips.selector, function (){
-            var text = getRandText(tips.text);
-            text = text.render({text: $(this).text()});
-            showMessage(text, 3000);
-        });
-    });
-    $.each(result.click, function (index, tips){
-        $(document).on("click", tips.selector, function (){
-            var text = getRandText(tips.text);
-            text = text.render({text: $(this).text()});
-            showMessage(text, 3000, true);
-        });
-    });
-    $.each(result.seasons, function (index, tips){
-        var now = new Date();
-        var after = tips.date.split('-')[0];
-        var before = tips.date.split('-')[1] || after;
-        
-        if((after.split('/')[0] <= now.getMonth()+1 && now.getMonth()+1 <= before.split('/')[0]) && 
-           (after.split('/')[1] <= now.getDate() && now.getDate() <= before.split('/')[1])){
-            var text = getRandText(tips.text);
-            text = text.render({year: now.getFullYear()});
-            showMessage(text, 6000, true);
-        }
-    });
-    
-    if (live2d_settings.showF12OpenMsg) {
-        re.toString = function() {
-            showMessage(getRandText(result.waifu.console_open_msg), 5000, true);
-            return '';
-        };
-    }
-    
-    if (live2d_settings.showCopyMessage) {
-        $(document).on('copy', function() {
-            showMessage(getRandText(result.waifu.copy_message), 5000, true);
-        });
-    }
-    
-    $('.waifu-tool .fui-photo').click(function(){
-        showMessage(getRandText(result.waifu.screenshot_message), 5000, true);
-        window.Live2D.captureName = live2d_settings.screenshotCaptureName;
-        window.Live2D.captureFrame = true;
-    });
-    
-    $('.waifu-tool .fui-cross').click(function(){
-        sessionStorage.setItem('waifu-dsiplay', 'none');
-        showMessage(getRandText(result.waifu.hidden_message), 1300, true);
-        window.setTimeout(function() {$('.waifu').hide();}, 1300);
-    });
-    
-    window.showWelcomeMessage = function(result) {
-        var text;
-        if (window.location.href == live2d_settings.homePageUrl) {
-            var now = (new Date()).getHours();
-            if (now > 23 || now <= 5) text = getRandText(result.waifu.hour_tips['t23-5']);
-            else if (now > 5 && now <= 7) text = getRandText(result.waifu.hour_tips['t5-7']);
-            else if (now > 7 && now <= 11) text = getRandText(result.waifu.hour_tips['t7-11']);
-            else if (now > 11 && now <= 14) text = getRandText(result.waifu.hour_tips['t11-14']);
-            else if (now > 14 && now <= 17) text = getRandText(result.waifu.hour_tips['t14-17']);
-            else if (now > 17 && now <= 19) text = getRandText(result.waifu.hour_tips['t17-19']);
-            else if (now > 19 && now <= 21) text = getRandText(result.waifu.hour_tips['t19-21']);
-            else if (now > 21 && now <= 23) text = getRandText(result.waifu.hour_tips['t21-23']);
-            else text = getRandText(result.waifu.hour_tips.default);
-        } else {
-            var referrer_message = result.waifu.referrer_message;
-            if (document.referrer !== '') {
-                var referrer = document.createElement('a');
-                referrer.href = document.referrer;
-                var domain = referrer.hostname.split('.')[1];
-                if (window.location.hostname == referrer.hostname)
-                    text = referrer_message.localhost[0] + document.title.split(referrer_message.localhost[2])[0] + referrer_message.localhost[1];
-                else if (domain == 'baidu')
-                    text = referrer_message.baidu[0] + referrer.search.split('&wd=')[1].split('&')[0] + referrer_message.baidu[1];
-                else if (domain == 'so')
-                    text = referrer_message.so[0] + referrer.search.split('&q=')[1].split('&')[0] + referrer_message.so[1];
-                else if (domain == 'google')
-                    text = referrer_message.google[0] + document.title.split(referrer_message.google[2])[0] + referrer_message.google[1];
-                else {
-                    $.each(result.waifu.referrer_hostname, function(i,val) {if (i==referrer.hostname) referrer.hostname = getRandText(val)});
-                    text = referrer_message.default[0] + referrer.hostname + referrer_message.default[1];
-                }
-            } else text = referrer_message.none[0] + document.title.split(referrer_message.none[2])[0] + referrer_message.none[1];
-        }
-        showMessage(text, 6000);
-    }; if (live2d_settings.showWelcomeMessage) showWelcomeMessage(result);
-    
-    var waifu_tips = result.waifu;
-    
-    function loadOtherModel() {
-        var modelId = modelStorageGetItem('modelId');
-        var modelRandMode = live2d_settings.modelRandMode;
-        
-        $.ajax({
-            cache: modelRandMode == 'switch' ? true : false,
-            url: live2d_settings.modelAPI+modelRandMode+'/?id='+modelId,
-            dataType: "json",
-            success: function(result) {
-                loadModel(result.model['id']);
-                var message = result.model['message'];
-                $.each(waifu_tips.model_message, function(i,val) {if (i==result.model['id']) message = getRandText(val)});
-                showMessage(message, 3000, true);
-            }
-        });
-    }
-    
-    function loadRandTextures() {
-        var modelId = modelStorageGetItem('modelId');
-        var modelTexturesId = modelStorageGetItem('modelTexturesId');
-        var modelTexturesRandMode = live2d_settings.modelTexturesRandMode;
-        
-        $.ajax({
-            cache: modelTexturesRandMode == 'switch' ? true : false,
-            url: live2d_settings.modelAPI+modelTexturesRandMode+'_textures/?id='+modelId+'-'+modelTexturesId,
-            dataType: "json",
-            success: function(result) {
-                if (result.textures['id'] == 1 && (modelTexturesId == 1 || modelTexturesId == 0))
-                    showMessage(waifu_tips.load_rand_textures[0], 3000, true);
-                else showMessage(waifu_tips.load_rand_textures[1], 3000, true);
-                loadModel(modelId, result.textures['id']);
-            }
-        });
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-    
-    function modelStorageGetItem(key) { return live2d_settings.modelStorage ? localStorage.getItem(key) : sessionStorage.getItem(key); }
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-    if (live2d_settings.showHitokoto) {
-        window.getActed = false; window.hitokotoTimer = 0; window.hitokotoInterval = false;
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-    $('.waifu-tool .fui-eye').click(function (){loadOtherModel()});
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diff --git a/spaces/fabiogra/moseca/app/service/vocal_remover/__init__.py b/spaces/fabiogra/moseca/app/service/vocal_remover/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/falterWliame/Face_Mask_Detection/Download Film Makdee Free.md b/spaces/falterWliame/Face_Mask_Detection/Download Film Makdee Free.md
deleted file mode 100644
index b2c8618e9d70f92cdcb09251ed1de9303abc73bb..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Download Film Makdee Free.md	
+++ /dev/null
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Mstar Usb Debug Tool Driver Download TOP.md b/spaces/falterWliame/Face_Mask_Detection/Mstar Usb Debug Tool Driver Download TOP.md
deleted file mode 100644
index 68a40a4fed34b1bf2100a4b4d9c687a34292bb2f..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Mstar Usb Debug Tool Driver Download TOP.md	
+++ /dev/null
@@ -1,51 +0,0 @@
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-<h1>How to Download and Install Mstar USB Debug Tool Driver</h1>
-<p>If you are looking for a way to connect your Mstar device to your computer for debugging purposes, you will need to install the Mstar USB Debug Tool driver. This driver will allow your computer to recognize your device and communicate with it via a USB cable. In this article, we will show you how to download and install the Mstar USB Debug Tool driver on your Windows system.</p>
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-<h2>Step 1: Download the Mstar USB Debug Tool driver</h2>
-<p>There are several sources where you can download the Mstar USB Debug Tool driver, but we recommend using the DriverDouble website[^1^] as it is reliable and easy to use. To download the driver from DriverDouble, follow these steps:</p>
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-<li>Click on the "Download Now" button and save the file to your computer.</li>
-<li>The file name should be something like "Mstar_USB_Debug_Tool_driver.zip". Extract the zip file to a folder on your computer.</li>
-</ul>
-<h2>Step 2: Install the Mstar USB Debug Tool driver</h2>
-<p>Once you have downloaded and extracted the driver file, you can proceed to install it on your computer. To install the driver, follow these steps:</p>
-<ul>
-<li>Connect your Mstar device to your computer via a USB cable.</li>
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-<li>Find your Mstar device under the "Other devices" or "Ports (COM & LPT)" category. It should have a yellow exclamation mark next to it.</li>
-<li>Right-click on your device and select "Update driver software".</li>
-<li>Select "Browse my computer for driver software" and then "Let me pick from a list of device drivers on my computer".</li>
-<li>Click on "Have Disk" and then "Browse". Navigate to the folder where you extracted the driver file and select the "Mstar_USB_Debug_Tool.inf" file.</li>
-<li>Click on "Open" and then "OK". The driver installation should start.</li>
-<li>Follow the on-screen instructions and wait for the installation to finish.</li>
-<li>Restart your computer if prompted.</li>
-</ul>
-<h2>Step 3: Verify the Mstar USB Debug Tool driver installation</h2>
-<p>To make sure that the driver installation was successful, you can use a tool called USBView[^3^] that shows you detailed information about all USB devices connected to your computer. To use USBView, follow these steps:</p>
-<ul>
-<li>Download and install the Windows SDK from <a href="https://developer.microsoft.com/en-us/windows/downloads/windows-10-sdk/">https://developer.microsoft.com/en-us/windows/downloads/windows-10-sdk/</a>. During the installation, select only the Debugging Tools for Windows box and clear all other boxes.</li>
-<li>By default, on a x64 PC the SDK will install USBView to the following directory: C:\Program Files (x86)\Windows Kits\10\Debuggers\x64. Open this folder and double-click on "usbview.exe" to launch USBView.</li>
-<li>In USBView, expand the tree view on the left side and find your Mstar device under one of the USB hubs. It should have a name like "Mstar USB Debug Tool".</li>
-<li>Select your device and check the information on the right side. You should see something like this:</li>
-
-```
-Device Descriptor:
-bcdUSB:             0x0200
-bDeviceClass:         0x00
-bDeviceSubClass:      0x00
-bDeviceProtocol:      0x00
-bMaxPacketSize0:      0x40 (64)
-idVendor:           0x0548 (MStar Semiconductor)
-idProduct:          0x1005
-bcdDevice:          0x0100
-iManufacturer:        0x01
-iProduct:             0x02
-iSerialNumber:        0x03
-bNumConfigurations:   0x01
-
-Connection</p>
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Nettime Server 3 04 Cracked Fix.md b/spaces/falterWliame/Face_Mask_Detection/Nettime Server 3 04 Cracked Fix.md
deleted file mode 100644
index 6621da0424f94497e61c01628e4728ba05304559..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Nettime Server 3 04 Cracked Fix.md	
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Nicelabel 6 [REPACK] Keygen.md b/spaces/falterWliame/Face_Mask_Detection/Nicelabel 6 [REPACK] Keygen.md
deleted file mode 100644
index b04e1296f7876b4f590075560aeeaeebac536e3e..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Nicelabel 6 [REPACK] Keygen.md	
+++ /dev/null
@@ -1,33 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Download Certificate of Registration from RNI Online.md b/spaces/fatiXbelha/sd/Download Certificate of Registration from RNI Online.md
deleted file mode 100644
index ded30deecc45705843aa0204af4d1cc5d5689387..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Certificate of Registration from RNI Online.md	
+++ /dev/null
@@ -1,218 +0,0 @@
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-<br> - Benefits of registering your newspaper or magazine with RNI | | H2: How to apply for title verification with RNI? | - Eligibility criteria and documents required <br> - Steps to fill the online application form <br> - Fees and payment modes <br> - How to check the status of your application and download the title letter | | H2: How to register your publication with RNI? | - Guidelines and checklist for registration <br> - Steps to fill the online registration application form <br> - How to download your online certificate of registration (CR) <br> - How to check the registration data and update it if needed | | H2: How to file annual statements with RNI? | - Importance and benefits of filing annual statements <br> - How to access the e-filing portal and fill the online form <br> - Documents and information required for filing annual statements <br> - How to pay the penalty for late filing or non-filing | | H2: How to get circulation verification certificates from RNI? | - What are circulation verification certificates and why are they needed? <br> - How to apply for circulation verification through PIB offices <br> - Documents and information required for circulation verification <br> - How to download the circulation verification certificates online | | H2: How to contact RNI for any queries or complaints? | - Contact details of RNI headquarters and regional offices <br> - How to send an email or fax to RNI <br> - How to file an RTI application with RNI <br> - How to follow RNI on social media platforms | | H1: Conclusion | - Summary of the main points of the article <br> - Call to action for the readers | Table 2: Article with HTML formatting <h1>What is RNI and why do you need it?</h1>
-<p>If you are a publisher or owner of a newspaper or magazine in India, you might have heard of RNI. But what exactly is RNI and why do you need it? In this article, we will answer these questions and more. We will also guide you through the process of applying for title verification, registration, annual statements, and circulation verification with RNI. So, let's get started!</p>
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-<p>RNI stands for Registrar of Newspapers for India. It is a statutory body under the Ministry of Information and Broadcasting, Government of India. It was established in 1956 under the Press and Registration of Books Act, 1867. Its main functions are:</p>
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-<li>To register newspapers and magazines published in India.</li>
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- <h2>How to apply for title verification with RNI?</h2>
-<p>The first step to register your newspaper or magazine with RNI is to apply for title verification. Title verification is a process of checking whether the proposed title of your publication is unique, suitable, and not in conflict with any existing title. You have to apply for title verification before you start publishing your newspaper or magazine.</p>
-<p>To apply for title verification with RNI, you have to meet the following eligibility criteria and submit the following documents:</p>
-<ul>
-<li>You have to be a citizen of India or a company registered in India.</li>
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-<li>You have to submit a self-attested copy of your experience certificate, if any, in the field of journalism or publishing.</li>
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-<li>You have to submit a self-declaration form stating that you will abide by the code of ethics and conduct for journalists and publishers as prescribed by the Press Council of India.</li>
-</ul>
-<p>Once you have all the documents ready, you can follow these steps to fill the online application form for title verification:</p>
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-<li>Read the instructions carefully and click on "Proceed".</li>
-<li>Enter your email ID and mobile number and click on "Send OTP".</li>
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-<li>Fill in the personal details, such as name, gender, date of birth, address, etc.</li>
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-<li>Preview your application and make any changes if needed.</li>
-<li>Click on "Submit" and note down the application number for future reference.</li>
-</ol>
-<p>The fees for title verification are Rs. 500 for each proposed title. You can pay the fees online through debit card, credit card, net banking, UPI, or wallet. You will receive an acknowledgement receipt after successful payment.</p>
-<p>You can check the status of your application online by visiting <a href="">https://rni.nic.in/title_status.aspx</a> and entering your application number. You can also download your title letter online once it is issued by RNI. The title letter is valid for one year from the date of issue. You have to apply for registration within this period.</p>
- <h2>How to register your publication with RNI?</h2>
-<p>After obtaining the title letter from RNI, you can proceed to register your publication with RNI. Registration is a process of obtaining a certificate of registration (CR) from RNI, which is a legal document that certifies that your publication is registered under the Press and Registration of Books Act, 1867. You have to apply for registration within one year from the date of issue of the title letter.</p>
-<p>To apply for registration with RNI, you have to follow these guidelines and checklist:</p>
-<ul>
-<li>You have to publish at least four issues of your newspaper or magazine before applying for registration.</li>
-<li>You have to appoint an editor who is responsible for the selection and arrangement of the content of your publication.</li>
-<li>You have to print and publish your publication from a press that is registered under the Press and Registration of Books Act, 1867.</li>
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-<li>You have to print and publish your publication in compliance with the code of ethics and conduct for journalists and publishers as prescribed by the Press Council of India.</li>
-<li>You have to print and publish your publication without any obscene, seditious, defamatory, or inflammatory content that may harm the public interest or national security.</li>
-</ul> <p>Once you have followed these guidelines and checklist, you can follow these steps to fill the online registration application form:</p>
-<ol>
-<li>Visit the official website of RNI at <a href="">https://rni.nic.in</a>.</li>
-<li>Click on the "Online Services" tab and select "Registration".</li>
-<li>Read the instructions carefully and click on "Proceed".</li>
-<li>Enter your title letter number and click on "Verify".</li>
-<li>Fill in the publication details, such as title, language, periodicity, size, price, etc.</li>
-<li>Fill in the editor details, such as name, address, email ID, mobile number, etc.</li>
-<li>Fill in the printer details, such as name, address, email ID, mobile number, etc.</li>
-<li>Fill in the publisher details, such as name, address, email ID, mobile number, etc.</li>
-<li>Upload the scanned copies of the following documents in PDF format:</li>
-<ul>
-<li>A self-attested copy of your title letter.</li>
-<li>A self-attested copy of your Aadhaar card or PAN card as proof of identity.</li>
-<li>A self-attested copy of your address proof, such as electricity bill, water bill, telephone bill, etc.</li>
-<li>A self-attested copy of your educational qualification certificate, such as degree, diploma, etc.</li>
-<li>A self-attested copy of your experience certificate, if any, in the field of journalism or publishing.</li>
-<li>A self-attested copy of your editor's appointment letter.</li>
-<li>A self-attested copy of your editor's declaration form.</li>
-<li>A self-attested copy of your printer's declaration form.</li>
-<li>A self-attested copy of your publisher's declaration form.</li>
-<li>A self-attested copy of your press registration certificate.</li>
-<li>A self-attested copy of your first four issues of your publication.</li>
-</ul>
-<li>Preview your application and make any changes if needed.</li>
-<li>Click on "Submit" and note down the application number for future reference.</li>
-</ol>
-<p>The fees for registration are Rs. 1000 for each publication. You can pay the fees online through debit card, credit card, net banking, UPI, or wallet. You will receive an acknowledgement receipt after successful payment.</p>
-<p>You can check the status of your application online by visiting <a href="">https://rni.nic.in/reg_status.aspx</a> and entering your application number. You can also download your online certificate of registration (CR) online once it is issued by RNI. The CR is valid for five years from the date of issue. You have to renew it before it expires.</p>
- <h2>How to file annual statements with RNI?</h2>
-<p>As a registered publisher or owner of a newspaper or magazine in India, you have to file annual statements with RNI every year. Annual statements are reports that contain information about the circulation, distribution, ownership, management, finances, and editorial policies of your publication. You have to file annual statements within 45 days from the last day of the financial year (i.e., by May 15).</p>
-<p>Filing annual statements with RNI is important and beneficial for several reasons, such as:</p>
-<ul>
-<li>You can update and verify the data and information about your publication with RNI.</li>
-<li>You can maintain and improve the quality and credibility of your publication.</li>
-<li>You can comply with the legal obligations under the Press and Registration of Books Act, 1867 and other rules and regulations.</li>
-<li>You can avail various government schemes and subsidies for newspapers and magazines.</li>
-</ul>
- <p>To file annual statements with RNI, you have to access the e-filing portal at <a href="">https://rni.nic.in/efiling/</a>. You have to fill the online form and upload the scanned copies of the following documents in PDF format:</p>
- <ul>
-<li>A self-attested copy of your certificate of registration (CR).</li>
-<li>A self-attested copy of your circulation verification certificate (CVC), if any.</li>
-<li>A self-attested copy of your income tax return (ITR) or audited balance sheet for the financial year.</li>
-<li>A self-attested copy of your GST return or invoice for the financial year.</li>
-</ul>
- <p>You also have to provide the following information for filing annual statements:</p>
- <ul>
-<li>The name and address of your publication.</li>
-<li>The name and address of your editor, printer, and publisher.</li>
-<li>The language and periodicity of your publication.</li>
-<li>The average net paid circulation per issue for the financial year.</li>
-<li>The average The average number of pages and columns per issue for the financial year.</li>
-<li>The average price per copy and subscription rate for the financial year.</li>
-<li>The sources and amounts of income and expenditure for the financial year.</li>
-<li>The editorial policy and objectives of your publication.</li>
-<li>The changes, if any, in the title, language, periodicity, size, price, ownership, management, or editorial staff of your publication during the financial year.</li>
-</ul>
- <p>Once you have filled the online form and uploaded the documents, you can preview your annual statement and make any changes if needed. You can then submit your annual statement online and note down the acknowledgement number for future reference.</p>
- <p>If you fail to file your annual statement within the prescribed time limit, you may have to pay a penalty of Rs. 500 for each month of delay or part thereof. If you fail to file your annual statement for three consecutive years, your registration may be cancelled by RNI.</p>
- <h2>How to get circulation verification certificates from RNI?</h2>
-<p>Circulation verification certificates (CVCs) are documents that certify the net paid circulation of your publication as verified by an independent agency appointed by RNI. CVCs are issued by RNI on the basis of the applications made by the publishers or owners of newspapers and magazines through the Press Information Bureau (PIB) offices in various states and union territories.</p>
- <p>CVCs are needed for various purposes, such as:</p>
-<ul>
-<li>To claim government advertisements and subsidies for newspapers and magazines.</li>
-<li>To participate in tenders and auctions for newspapers and magazines.</li>
-<li>To get recognition and accreditation from various media bodies and associations.</li>
-<li>To enhance the reputation and credibility of your publication among the readers and advertisers.</li>
-</ul>
- <p>To apply for circulation verification with RNI, you have to follow these steps:</p>
-<ol>
-<li>Visit the nearest PIB office in your state or union territory and collect the application form for circulation verification.</li>
-<li>Fill in the application form with the details of your publication, such as title, language, periodicity, size, price, etc.</li>
-<li>Attach a self-attested copy of your certificate of registration (CR) issued by RNI.</li>
-<li>Attach a self-attested copy of your latest annual statement filed with RNI.</li>
-<li>Attach a self-attested copy of your latest income tax return (ITR) or audited balance sheet for the financial year.</li>
-<li>Attach a self-attested copy of your latest GST return or invoice for the financial year.</li>
-<li>Attach a self-attested copy of your latest print order and print bill from your printer.</li>
-<li>Attach a self-attested copy of your latest distribution list and distribution bill from your distributor.</li>
-<li>Submit the application form along with the documents and fees to the PIB office. The fees for circulation verification are Rs. 2000 for each publication.</li>
-</ol>
- <p>The PIB office will forward your application to RNI, which will appoint an independent agency to conduct the circulation verification. The agency will visit your printer, distributor, subscribers, agents, etc. and verify the net paid circulation of your publication. The agency will submit its report to RNI within 90 days from the date of appointment.</p>
- <p>RNI will issue the circulation verification certificate (CVC) based on the report of the agency. You can download the CVC online from <a href="">https://rni.nic.in/cvc.aspx</a> by entering your CR number. The CVC is valid for two years from the date of issue. You have to apply for renewal before it expires.</p>
- <h2>How to contact RNI for any queries or complaints?</h2>
-<p>If you have any queries or complaints regarding title verification, registration, annual statements, circulation verification, or any other matter related to RNI, you can contact RNI through various modes, such as:</p>
- <ul>
-<li>You can visit or write to RNI headquarters at: <br><br>Registrar of Newspapers for India <br>West Block VIII <br>R.K. Puram <br>New Delhi - 110066 <br><br>You can also visit or write to any of the regional offices of RNI located in Mumbai, Kolkata, Chennai, Guwahati, Nagpur, Bhopal, Lucknow, Chandigarh, Bangalore, Hyderabad, Patna, Ahmedabad, Jaipur, Bhubaneswar and Thiruvananthapuram. You can find the contact details of the regional offices at <a href="">https://rni.nic.in/ro.aspx</a>.</li>
-<li>You can call the toll-free helpline number of RNI at 1800-11-2676 from Monday to Friday between 9:30 am to 6:00 pm.</li>
-<li>You can send an email to RNI at rni-mib@nic.in or rnireg-mib@nic.in for any queries or complaints related to title verification or registration.</li>
-<li>You can send a fax to RNI at 011-26175516 or 011-26179781 for any queries or complaints related to title verification or registration.</li>
-<li>You can file an RTI application with RNI to seek information on any matter related to RNI under the Right to Information Act, 2005. You can file the RTI application online at <a href="">https://rtionline.gov.in</a> or offline by sending a written request along with a fee of Rs. 10 to the Central Public Information Officer (CPIO) of RNI.</li>
-<li>You can follow RNI on social media platforms, such as Facebook, Twitter, YouTube, and Instagram, to get the latest updates and news about RNI. You can also share your feedback and suggestions with RNI through these platforms. You can find the links to the social media accounts of RNI at <a href="">https://rni.nic.in/social_media.aspx</a>.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>In this article, we have explained what is RNI and why do you need it. We have also guided you through the process of applying for title verification, registration, annual statements, and circulation verification with RNI. We hope that this article has helped you understand the role and functions of RNI and how to avail its services and facilities. If you have any queries or complaints regarding RNI, you can contact RNI through various modes as mentioned above.</p>
-<p>If you are a publisher or owner of a newspaper or magazine in India, we urge you to register your publication with RNI and comply with its rules and regulations. By doing so, you will not only protect your title and rights, but also contribute to the development and growth of the print media industry in India. You will also be able to access various government schemes and subsidies for newspapers and magazines. So, what are you waiting for? Apply for title verification and registration with RNI today!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions (FAQs) about RNI:</p>
-<ol>
-<li><b>What is the difference between title verification and registration?</b><br>Title verification is a process of checking whether the proposed title of your publication is unique, suitable, and not in conflict with any existing title. Registration is a process of obtaining a certificate of registration (CR) from RNI, which is a legal document that certifies that your publication is registered under the Press and Registration of Books Act, 1867.</li>
-<li><b>How long does it take to get title verification and registration from RNI?</b><br>It usually takes about 30 days to get title verification from RNI after submitting your online application form and paying the fees. It usually takes about 60 days to get registration from RNI after submitting your online application form, paying the fees, and publishing at least four issues of your publication.</li>
-<li><b>How can I check the status of my title verification or registration application?</b><br>You can check the status of your title verification or registration application online by visiting <a href="">https://rni.nic.in/title_status.aspx</a> or <a href="">https://rni.nic.in/reg_status.aspx</a> respectively and entering your application number. You can also download your title letter or certificate of registration (CR) online once they are issued by RNI.</li>
-<li><b>What are the penalties for not filing annual statements or not renewing registration?</b><br>If you fail to file your annual statement within 45 days from the last day of the financial year (i.e., by May 15), you may have to pay a penalty of Rs. 500 for each month of delay or part thereof. If you fail to file your annual statement for three consecutive years, your registration may be cancelled by RNI. If you fail to renew your registration before it expires (i.e., within five years from the date of issue), you may have to pay a penalty of Rs. 1000 for each year of delay or part thereof.</li>
-<li><b>What are the benefits of getting circulation verification certificates from RNI?</b><br>Circulation verification certificates (CVCs) are documents that certify the net paid circulation of your publication as verified by an independent agency appointed by RNI. CVCs are needed for various purposes, such as:</p>
-<ul>
-<li>To claim government advertisements and subsidies for newspapers and magazines.</li>
-<li>To participate in tenders and auctions for newspapers and magazines.</li>
-<li>To get recognition and accreditation from various media bodies and associations.</li>
-<li>To enhance the reputation and credibility of your publication among the readers and advertisers.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/Download Paint the Town Red Mod APK and Unleash Your Fury.md b/spaces/fatiXbelha/sd/Download Paint the Town Red Mod APK and Unleash Your Fury.md
deleted file mode 100644
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-
-<h1>How to Download Paint the Town Red Mod APK for Android</h1>
-<p>Are you looking for a fun and exciting game to play on your Android device? Do you want to unleash your inner fighter and bash some voxel-based enemies in different locations and time periods? If yes, then you should try <strong>Paint the Town Red</strong>, a chaotic first person melee combat game that features a massive Rogue-Lite adventure. In this article, we will tell you what Paint the Town Red is, why you should download its mod apk, and how to do it safely and easily.</p>
- <h2>What is Paint the Town Red?</h2>
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-<p>Paint the Town Red is a game developed by South East Games and released on Steam in 2021. It is also available for Xbox One and Xbox Series X|S. The game is set in different locations and time periods, such as a biker bar, a disco, a prison, a pirate cove, and a western saloon. The game has voxel-based enemies that can be punched, bashed, kicked, stabbed, and sliced completely dynamically using almost anything that isn't nailed down. The game also has a level editor and custom texture and music options that allow players to create their own scenarios and share them on the Steam workshop.</p>
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- <h3>Features of Paint the Town Red</h3>
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-<ul>
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-<li>Dive into action-packed scenarios with unique enemies and weapons</li>
-<li>Face the ultimate challenge in Beneath, a massive roguelike campaign with deadly creatures, secrets, and bosses</li>
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-<li>Create your own levels with the intuitive editor and share them with the community</li>
-</ul>
- <h2>Why Download Paint the Town Red Mod APK?</h2>
-<h3>Benefits of Mod APK</h3>
-<p>A mod apk is a modified version of an original app that offers some extra features or advantages that are not available in the official app. For example, a mod apk may have unlimited money, unlocked items, or premium features for free. Some of the benefits of downloading Paint the Town Red mod apk are:</p>
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-<ol>
-<li>Go to a trusted website that offers Paint the Town Red mod apk file. Make sure the website is safe and reliable and has positive reviews from other users. You can also scan the file with an antivirus software before downloading it.</li>
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-</ol>
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-<ul>
-<li>Use different weapons and objects to attack your enemies. You can use anything from chairs, bottles, pool cues, knives, swords, axes, guns, grenades, and more. Experiment with different combinations and see what works best for you.</li>
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-<li>Explore the levels and find hidden secrets and collectibles. You can find health packs, ammo, weapons, keys, coins, and other items that will help you survive and progress in the game. You can also find easter eggs, references, and jokes that will make you laugh.</li>
-<li>Try different modes and challenges to test your skills and have more variety. You can play the Scenario mode to experience different scenarios with unique enemies and objectives. You can play the Beneath mode to embark on a roguelike adventure with random levels and enemies. You can play the Arena mode to face waves of enemies in different arenas. You can play the Endless mode to see how long you can survive in a randomly generated level.</li>
-<li>Create your own levels with the level editor and share them with the community. You can use the level editor to design your own scenarios with custom enemies, weapons, objects, textures, music, and more. You can also download and play levels created by other players on the Steam workshop.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Paint the Town Red is a chaotic first person melee combat game that features a massive roguelike adventure. It is a fun and exciting game that lets you unleash your inner fighter and bash some voxel-based enemies in different locations and time periods. You can download Paint the Town Red mod apk for your Android device to enjoy all the features of the game for free. However, you should also be careful of the risks of downloading a mod apk and follow the steps to download and install it safely and easily. We hope this article has helped you learn how to download Paint the Town Red mod apk for Android. If you have any questions or feedback, please feel free to leave a comment below.</p>
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-<tr><td><strong>Q: Is Paint the Town Red mod apk safe to download?</strong></td><td><strong>A: It depends on where you download it from. Some websites may offer fake or malicious files that may harm your device or steal your data. Therefore, you should always download Paint the Town Red mod apk from a trusted website that has positive reviews from other users. You should also scan the file with an antivirus software before downloading it.</strong></td></tr>
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diff --git a/spaces/fatiXbelha/sd/Explore a Magical World with Pink Princess House Craft Game Mod APK.md b/spaces/fatiXbelha/sd/Explore a Magical World with Pink Princess House Craft Game Mod APK.md
deleted file mode 100644
index fa910c8aa358d62dd83b815e4d013afc7031d07e..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Explore a Magical World with Pink Princess House Craft Game Mod APK.md	
+++ /dev/null
@@ -1,67 +0,0 @@
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-<h1>Pink Princess House Craft Game Mod APK: A Fun and Creative Game for Girls</h1>
-<p>If you are looking for a game that lets you unleash your imagination and creativity, then you should try Pink Princess House Craft Game. This is a game that allows you to build your own dream house, decorate it with various items, and customize your character and pets. You can also explore different worlds and themes, such as fairy tale, candy land, winter wonderland, and more. And the best part is, you can download the mod apk version of this game and enjoy unlimited resources and features for free. In this article, we will tell you more about this game and how to download and install the mod apk file on your device.</p>
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-<h3>What is Pink Princess House Craft Game?</h3>
-<p>Pink Princess House Craft Game is a simulation game that is designed for girls who love crafting, building, and designing. The game is developed by Wild Games Studio and has over 10 million downloads on Google Play Store. The game is rated 4.1 out of 5 stars by more than 100 thousand users.</p>
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-<h3>Step 1: Enable unknown sources on your device</h3>
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-<h3>Step 2: Download the mod apk file from a trusted source</h3>
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-<p>Pink Princess House Craft Game is a fun and creative game for girls who love crafting, building, and designing. The game allows you to build your own dream house, decorate it with various items, and customize your character and pets. You can also explore different worlds and themes, such as fairy tale, candy land, winter wonderland, and more. And by downloading the mod apk version of this game, you can enjoy unlimited resources and features for free.</p>
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- <h4>Frequently Asked Questions</h4>
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-<table>
-<tr><td><b>Q: Is Pink Princess House Craft Game Mod APK safe to use?</b></td><td><b>A: Yes, it is safe to use as long as you download it from a trusted source. The mod apk file does not contain any viruses or malware that can harm your device or data.</b></td></tr>
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-<tr><td><b>Q: How can I share my creations with my friends in Pink Princess House Craft Game?</b></td><td><b>A: You can share your creations with your friends in Pink Princess House Craft Game by inviting them to visit your house or visiting their houses. You can chat with them, exchange gifts, rate their houses, and take screenshots. You can also share your screenshots on social media platforms like Facebook or Instagram.</b></td></tr>
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\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/speaker_encoder/params_model.py b/spaces/fb700/chatglm-fitness-RLHF/speaker_encoder/params_model.py
deleted file mode 100644
index 3e356472fb5a27f370cb3920976a11d12a76c1b7..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/speaker_encoder/params_model.py
+++ /dev/null
@@ -1,11 +0,0 @@
-
-## Model parameters
-model_hidden_size = 256
-model_embedding_size = 256
-model_num_layers = 3
-
-
-## Training parameters
-learning_rate_init = 1e-4
-speakers_per_batch = 64
-utterances_per_speaker = 10
diff --git a/spaces/fclong/summary/fengshen/data/megatron_dataloader/bert_dataset.py b/spaces/fclong/summary/fengshen/data/megatron_dataloader/bert_dataset.py
deleted file mode 100644
index 2c007f060fd07fc9c6302b7f88e191469d599222..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/data/megatron_dataloader/bert_dataset.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2020, NVIDIA CORPORATION.  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.
-
-"""BERT Style dataset."""
-
-
-import numpy as np
-import torch
-
-from fengshen.data.megatron_dataloader.dataset_utils import (
-    get_samples_mapping,
-    get_a_and_b_segments,
-    create_masked_lm_predictions,
-    create_tokens_and_tokentypes,
-)
-
-
-class BertDataset(torch.utils.data.Dataset):
-
-    def __init__(self, name, indexed_dataset, data_prefix,
-                 num_epochs, max_num_samples, masked_lm_prob,
-                 max_seq_length, short_seq_prob, seed, binary_head, tokenizer, masking_style):
-        # Params to store.
-        self.name = name
-        self.seed = seed
-        self.masked_lm_prob = masked_lm_prob
-        self.max_seq_length = max_seq_length
-        self.short_seq_prob = short_seq_prob
-        self.binary_head = binary_head
-        self.masking_style = masking_style
-
-        # Dataset.
-        self.indexed_dataset = indexed_dataset
-
-        # Build the samples mapping.
-        self.samples_mapping = get_samples_mapping(self.indexed_dataset,
-                                                   data_prefix,
-                                                   num_epochs,
-                                                   max_num_samples,
-                                                   # account for added tokens
-                                                   self.max_seq_length - 3,
-                                                   short_seq_prob,
-                                                   self.seed,
-                                                   self.name,
-                                                   self.binary_head)
-        inv_vocab = {v: k for k, v in tokenizer.vocab.items()}
-        self.vocab_id_list = list(inv_vocab.keys())
-        self.vocab_id_to_token_dict = inv_vocab
-        self.cls_id = tokenizer.cls_token_id
-        self.sep_id = tokenizer.sep_token_id
-        self.mask_id = tokenizer.mask_token_id
-        self.pad_id = tokenizer.pad_token_id
-        self.tokenizer = tokenizer
-
-    def __len__(self):
-        return self.samples_mapping.shape[0]
-
-    def __getitem__(self, idx):
-        start_idx, end_idx, seq_length = self.samples_mapping[idx]
-        sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]
-        # Note that this rng state should be numpy and not python since
-        # python randint is inclusive whereas the numpy one is exclusive.
-        # We % 2**32 since numpy requres the seed to be between 0 and 2**32 - 1
-        np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))
-        return build_training_sample(sample, seq_length,
-                                     self.max_seq_length,  # needed for padding
-                                     self.vocab_id_list,
-                                     self.vocab_id_to_token_dict,
-                                     self.cls_id, self.sep_id,
-                                     self.mask_id, self.pad_id,
-                                     self.masked_lm_prob, np_rng,
-                                     self.binary_head,
-                                     tokenizer=self.tokenizer,
-                                     masking_style=self.masking_style)
-
-
-def build_training_sample(sample,
-                          target_seq_length, max_seq_length,
-                          vocab_id_list, vocab_id_to_token_dict,
-                          cls_id, sep_id, mask_id, pad_id,
-                          masked_lm_prob, np_rng, binary_head,
-                          tokenizer,
-                          masking_style='bert'):
-    """Biuld training sample.
-
-    Arguments:
-        sample: A list of sentences in which each sentence is a list token ids.
-        target_seq_length: Desired sequence length.
-        max_seq_length: Maximum length of the sequence. All values are padded to
-            this length.
-        vocab_id_list: List of vocabulary ids. Used to pick a random id.
-        vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
-        cls_id: Start of example id.
-        sep_id: Separator id.
-        mask_id: Mask token id.
-        pad_id: Padding token id.
-        masked_lm_prob: Probability to mask tokens.
-        np_rng: Random number genenrator. Note that this rng state should be
-              numpy and not python since python randint is inclusive for
-              the opper bound whereas the numpy one is exclusive.
-    """
-
-    if binary_head:
-        # We assume that we have at least two sentences in the sample
-        assert len(sample) > 1
-    assert target_seq_length <= max_seq_length
-
-    # Divide sample into two segments (A and B).
-    if binary_head:
-        tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample,
-                                                                  np_rng)
-    else:
-        tokens_a = []
-        for j in range(len(sample)):
-            tokens_a.extend(sample[j])
-        tokens_b = []
-        is_next_random = False
-
-    if len(tokens_a) >= max_seq_length-3:
-        tokens_a = tokens_a[:max_seq_length-3]
-
-    # Truncate to `target_sequence_length`.
-    max_num_tokens = target_seq_length
-    ''''
-    truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a),
-                                  len(tokens_b), max_num_tokens, np_rng)
-    '''
-
-    # Build tokens and toketypes.
-    tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b,
-                                                      cls_id, sep_id)
-    # Masking.
-    max_predictions_per_seq = masked_lm_prob * max_num_tokens
-    (tokens, masked_positions, masked_labels, _, _) = create_masked_lm_predictions(
-        tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob,
-        cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng,
-        tokenizer=tokenizer,
-        masking_style=masking_style)
-
-    # Padding.
-    tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \
-        = pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
-                                   masked_labels, pad_id, max_seq_length)
-
-    train_sample = {
-        'input_ids': tokens_np,
-        'token_type_ids': tokentypes_np,
-        'labels': labels_np,
-        'next_sentence_label': int(is_next_random),
-        'attention_mask': padding_mask_np}
-    return train_sample
-
-
-def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
-                             masked_labels, pad_id, max_seq_length):
-    """Pad sequences and convert them to numpy."""
-
-    # Some checks.
-    num_tokens = len(tokens)
-    padding_length = max_seq_length - num_tokens
-    assert padding_length >= 0
-    assert len(tokentypes) == num_tokens
-    assert len(masked_positions) == len(masked_labels)
-
-    # Tokens and token types.
-    filler = [pad_id] * padding_length
-    tokens_np = np.array(tokens + filler, dtype=np.int64)
-    tokentypes_np = np.array(tokentypes + filler, dtype=np.int64)
-
-    # Padding mask.
-    padding_mask_np = np.array([1] * num_tokens + [0] * padding_length,
-                               dtype=np.int64)
-
-    # Lables and loss mask.
-    labels = [-100] * max_seq_length
-    loss_mask = [0] * max_seq_length
-    for i in range(len(masked_positions)):
-        assert masked_positions[i] < num_tokens
-        labels[masked_positions[i]] = masked_labels[i]
-        loss_mask[masked_positions[i]] = 1
-    labels_np = np.array(labels, dtype=np.int64)
-    loss_mask_np = np.array(loss_mask, dtype=np.int64)
-
-    return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np
diff --git a/spaces/fclong/summary/fengshen/examples/clue1.1/run_clue_ubert.sh b/spaces/fclong/summary/fengshen/examples/clue1.1/run_clue_ubert.sh
deleted file mode 100644
index 3375f1d5358ac32bc0fb7f3d4c2bc543642ebb06..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/clue1.1/run_clue_ubert.sh
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=slurm-test # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=1 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --mem-per-cpu=3G # memory per cpu-core (4G is default)
-#SBATCH --gres=gpu:1 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. 
-
-#SBATCH --requeue
-#SBATCH --qos=preemptive
-
-
-DATA_DIR=./data/cmrc2018  #数据集路径
-
-PRETRAINED_MODEL_PATH=IDEA-CCNL/Erlangshen-Ubert-110M-Chinese
-
-CHECKPOINT_PATH=./checkpoints
-
-LOAD_CHECKPOINT_PATH=./checkpoints/last.ckpt
-
-OUTPUT_PATH=./predict/cmrc2018_predict.json
-
-DEFAULT_ROOT_DIR=./log
-
-
-DATA_ARGS="\
-        --data_dir $DATA_DIR \
-        --train_data train.json \
-        --valid_data dev.json \
-        --test_data dev.json \
-        --batchsize 32 \
-        --max_length 314 \
-        "
-
-MODEL_ARGS="\
-        --learning_rate 0.00002 \
-        --weight_decay 0.1 \
-        --warmup 0.01 \
-        --num_labels 1 \
-        "
-
-MODEL_CHECKPOINT_ARGS="\
-        --monitor val_span_acc \
-        --save_top_k 5 \
-        --mode max \
-        --every_n_train_steps 100 \
-        --save_weights_only true \
-        --checkpoint_path $CHECKPOINT_PATH \
-        --filename model-{epoch:02d}-{val_span_acc:.4f} \
-        "
-
-#--load_checkpoints_path $LOAD_CHECKPOINT_PATH \
-TRAINER_ARGS="\
-        --max_epochs 11 \
-        --gpus 1 \
-        --check_val_every_n_epoch 1 \
-        --gradient_clip_val 0.25 \
-        --val_check_interval 0.05 \
-        --limit_val_batches 100 \
-        --default_root_dir $DEFAULT_ROOT_DIR \
-        "
-
-options=" \
-        --pretrained_model_path $PRETRAINED_MODEL_PATH \
-        --output_path $OUTPUT_PATH \
-        --threshold 0.001 \
-        --train \
-        $DATA_ARGS \
-        $MODEL_ARGS \
-        $MODEL_CHECKPOINT_ARGS \
-        $TRAINER_ARGS \
-        "
-
-SCRIPT_PATH=./solution/clue_ubert.py
-python3 $SCRIPT_PATH $options
-
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bus Simulator  Ultimate Mod Apk 1.4.9 - How to Install and Play on Your Device.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bus Simulator  Ultimate Mod Apk 1.4.9 - How to Install and Play on Your Device.md
deleted file mode 100644
index ad5a1f6bfa680c302d7b9abd07ee36a3edcc0002..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bus Simulator  Ultimate Mod Apk 1.4.9 - How to Install and Play on Your Device.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-
-<h1>Bus Simulator Ultimate Mod APK 1.4 9: A Review</h1>
-<p>Do you love driving buses and exploring different cities? Do you want to experience the realistic and immersive gameplay of a bus simulator? If yes, then you should try Bus Simulator Ultimate, a popular and fun game developed by Zuuks Games. And if you want to enjoy the game even more, you should download and install Bus Simulator Ultimate Mod APK 1.4 9, a modified version of the game that offers unlimited money, unlocked buses, and more features. In this article, we will review Bus Simulator Ultimate Mod APK 1.4 9 and tell you why you should use it.</p>
- <h2>What is Bus Simulator Ultimate?</h2>
-<p>Bus Simulator Ultimate is a game that lets you drive various types of buses across different countries and cities. You can create your own bus company, customize your buses, hire drivers, and manage your routes. You can also interact with passengers, follow traffic rules, and deal with realistic situations such as weather, accidents, and road conditions. The game has stunning graphics, realistic sounds, and smooth controls that make you feel like you are driving a real bus.</p>
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- <h3>Features of Bus Simulator Ultimate</h3>
-<p>Some of the features of Bus Simulator Ultimate are:</p>
-<ul>
-<li>You can drive over 100 buses from famous brands such as Mercedes-Benz, Volvo, Scania, and more.</li>
-<li>You can travel across 25 countries and more than 250 cities with realistic maps and landmarks.</li>
-<li>You can customize your buses with different skins, colors, accessories, and logos.</li>
-<li>You can create your own routes and set your own fares.</li>
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-<ol>
-<li>Click on this link  to download the mod apk file.</li>
-<li>Allow unknown sources in your device settings.</li>
-<li>Locate the downloaded file in your file manager and tap on it to install it.</li>
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-</ol>
- <h2>Why use Bus Simulator Ultimate Mod APK 1.4 9?</h2>
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-<ul>
-<li>You get unlimited money that you can use to buy new buses, upgrade your company, and hire more drivers.</li>
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-</ul>
- <h3>Drawbacks of Bus Simulator Ultimate Mod APK 1.4 9</h3>
-<ul>
-<li>You may face some compatibility issues with some devices or Android versions.</li>
-<li>You may encounter some bugs or glitches in the game that may affect your gameplay or performance.</li>
-<li>You may lose your progress or data if you uninstall the mod apk or update the original version of the game.</li>
-<li>You may get banned from the online mode or the game server if you use the mod apk.</li>
-</ul>
- <h2>Conclusion</h2>
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- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Bus Simulator Ultimate Mod APK 1.4 9:</p>
-<ol>
-<li>Is Bus Simulator Ultimate Mod APK 1.4 9 safe to use?</li>
-<p>Bus Simulator Ultimate Mod APK 1.4 9 is not an official version of the game, so it may not be safe to use. It may contain viruses, malware, or spyware that can harm your device or steal your data. Therefore, you should only download and install the mod apk from trusted sources and scan it with an antivirus before using it.</p>
-<li>Is Bus Simulator Ultimate Mod APK 1.4 9 free to download and install?</li>
-<p>Yes, Bus Simulator Ultimate Mod APK 1.4 9 is free to download and install. You do not need to pay any money or register any account to use the mod apk. However, you may need to enable unknown sources in your device settings to allow the installation of the mod apk.</p>
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-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cook Game Experience the Joy of Cooking in These Entertaining and Educational Games.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cook Game Experience the Joy of Cooking in These Entertaining and Educational Games.md
deleted file mode 100644
index 44d1fcd5bfcca54e8060c441063066b161e9f727..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cook Game Experience the Joy of Cooking in These Entertaining and Educational Games.md	
+++ /dev/null
@@ -1,122 +0,0 @@
-<br />
-<h1>How to Cook Game Meat Like a Pro</h1>
-<p>Game meat is any meat that comes from wild animals that are hunted for food, such as deer, rabbit, duck, pheasant, quail, boar, and elk. Game meat is not only delicious, but also nutritious, lean, and sustainable. However, many people are intimidated by the idea of cooking game meat, as they think it is too tough, gamey, or difficult to handle. In this article, we will show you how to cook game meat like a pro, from choosing the right type of game meat, to preparing it for cooking, to cooking it in different ways, to serving it with the best accompaniments. By following these tips, you will be able to enjoy game meat at its best and impress your family and friends with your culinary skills.</p>
- <h2>What is Game Meat and Why Should You Try It?</h2>
-<p>Game meat is any meat that comes from wild animals that are hunted for food, such as deer, rabbit, duck, pheasant, quail, boar, and elk. Unlike domestic animals that are raised on farms, game animals live in their natural habitats and feed on a varied diet of grasses, herbs, berries, nuts, seeds, insects, and other animals. This gives game meat a distinctive flavor and texture that is different from domestic meat.</p>
-<h2>cook game</h2><br /><p><b><b>Download</b> &#9881;&#9881;&#9881; <a href="https://gohhs.com/2uPvkm">https://gohhs.com/2uPvkm</a></b></p><br /><br />
- <h3>Game Meat vs. Domestic Meat: The Differences and Benefits</h3>
-<p>Game meat has several differences and benefits compared to domestic meat. Here are some of them:</p>
-<ul>
-<li>Game meat is leaner than domestic meat, as game animals have less fat and more muscle due to their active lifestyle. This means game meat has fewer calories and saturated fat than domestic meat, which is good for your health.</li>
-<li>Game meat is richer in protein than domestic meat, as game animals have more muscle mass than domestic animals. Protein is essential for building and repairing your muscles, bones, skin, hair, and nails.</li>
-<li>Game meat is higher in iron than domestic meat, as game animals have more hemoglobin in their blood than domestic animals. Iron is important for transporting oxygen throughout your body and preventing anemia.</li>
-<li>Game meat is more flavorful than domestic meat, as game animals have a more varied diet than domestic animals. This gives game meat a complex and unique taste that varies depending on the type of animal, its habitat, its diet, and the season.</li>
-</ul>
- <h3>Types of Game Meat and How to Source Them</h3>
-<p>There are many types of game meat available in the market, depending on the region, the season, and the availability. Some of the most common types of game meat are:</p>
-<ul>
-<li>Venison: This is the meat from deer, such as whitetail deer, mule deer, red deer, fallow deer, or roe deer. Venison is tender, lean, and mild in flavor. It can be cooked in various ways, such as roasting, grilling, frying, or stewing.</li>
-<li>Rabbit: This is the meat from rabbits or hares. Rabbit is tender, lean, and delicate in flavor. It can be cooked in various ways, such as roasting, braising, frying or stewing.</li>
-<li>Duck: This is the meat from ducks or geese. Duck is rich, fatty,and flavorful. It can be cooked in various ways, such as roasting,baking,frying or confit.</ chunky and bony cuts of game meat, such as shanks, ribs, shoulders, or necks. Braising can make the game meat very tender and flavorful, as it absorbs the liquid and the aromatics in the pot. To braise game meat in a pot, you should:</p>
-<ol>
-<li>Preheat the oven to 325°F (160°C) and place a rack in the lower position.</li>
-<li>Season and flavor the game meat as desired and sear it in a large ovenproof pot over high heat until browned on all sides, about 10 minutes. Transfer the game meat to a plate and set aside.</li>
-<li>In the same pot, sauté some chopped onion, garlic, celery, and carrot over medium heat until soft, about 15 minutes. Add some tomato paste, flour, salt, pepper, and other spices and cook for another 5 minutes, stirring constantly.</li>
-<li>Add some liquid, such as water, broth, wine, or beer, to the pot and bring it to a boil. Scrape up any browned bits from the bottom of the pot with a wooden spoon.</li>
-<li>Return the game meat to the pot and add some herbs, such as bay leaves, thyme, rosemary, or parsley. The liquid should cover at least two-thirds of the game meat. If not, add more liquid as needed.</li>
-<li>Cover the pot with a lid and transfer it to the oven. Braise the game meat in the oven until it is fork-tender, turning it once halfway through. The cooking time will vary depending on the type and size of the meat, but generally it will take about 2 to 3 hours.</li>
-<li>Remove the game meat from the oven and transfer it to a platter. Strain the liquid from the pot and reduce it over high heat until thickened, about 15 minutes. Serve the game meat with the sauce over it or on the side.</li>
-</ol>
- <h2>How to Serve and Enjoy Game Meat</h2>
-<p>Game meat can be served and enjoyed in various ways, depending on your preference and occasion. Here are some tips to follow:</p>
- <h3>How to Pair Game Meat with Sauces and Sides</h3>
-<p>Game meat can be paired with different sauces and sides to complement its flavor and texture. Here are some examples of sauces and sides that go well with game meat:</p>
-<ul>
-<li>Venison: You can serve venison with a red wine sauce, a cranberry sauce, a mushroom sauce, or a peppercorn sauce. You can also serve venison with mashed potatoes, roasted vegetables, wild rice, or polenta.</li>
-<li>Rabbit: You can serve rabbit with a mustard sauce, a cream sauce, a tomato sauce, or a herb sauce. You can also serve rabbit with pasta, couscous, salad, or bread.</li>
-<li>Duck: You can serve duck with an orange sauce, a cherry sauce, a plum sauce, or a honey sauce. You can also serve duck with roasted potatoes, green beans, cabbage, or apples.</li>
-<li>Pheasant: You can serve pheasant with a gravy sauce, a cider sauce, a cranberry sauce, or a bacon sauce. You can also serve pheasant with stuffing, mashed turnips, Brussels sprouts, or chestnuts.</li>
-<li>Boar: You can serve boar with a barbecue sauce, a beer sauce, a apple sauce, or a mustard sauce. You can also serve boar with cornbread, baked beans, coleslaw, or fries.</li>
-<li>Elk: You can serve elk with a brown sauce, a blueberry sauce, a juniper sauce, or a horseradish sauce. You can also serve elk with roasted root vegetables, wild mushrooms, quinoa, or barley.</li>
-</ul>
- <h3>How to Store and Reheat Leftover Game Meat</h3>
-<p>Game meat can be stored and reheated for later use if you have any leftovers. Here are some tips to follow:</p>
-<ul>
-<li>Store leftover game meat in an airtight container or a ziplock bag in the refrigerator for up to 3 days or in the freezer for up to 3 months.</li>
-<li>Reheat leftover game meat in the oven at 350°F (175°C) for 10 to 15 minutes or in the microwave on high for 2 to 3 minutes until heated through. You can also reheat leftover game meat in a skillet over medium heat with some oil or butter for 5 to 10 minutes until browned and crisp.</li>
-<li>Avoid overcooking or drying out leftover game meat by adding some liquid, such as water, broth, wine, or juice, to the cooking pan or dish. You can also cover the game meat with foil or a lid to retain moisture and prevent burning.</li>
-</ul>
- <h4>Conclusion</h4>
-<p>Game meat is a great alternative to domestic meat that offers many benefits and possibilities. By following these tips on how to cook game meat like a pro, you will be able to enjoy game meat at its best and impress your family and friends with your culinary skills. Game meat is not only delicious, but also nutritious, lean, and sustainable. So why not give it a try and discover its amazing flavor and texture?</p>
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- <h4>FAQs</h4>
-<p>Here are some frequently asked questions about game meat and how to cook it:</p>
-<ul>
-<li>Q: Is game meat safe to eat?</li>
-<li>A: Game meat is safe to eat as long as it is fresh, clean, and properly handled. You should always buy game meat from reputable sources and check the regulations and laws regarding hunting and selling game meat in your area. You should also cook game meat to the appropriate temperature to kill any harmful bacteria or parasites that may be present in the meat.</li>
-<li>Q: How do I know when game meat is done?</li>
-<li>A: The best way to know when game meat is done is to use a meat thermometer and check the internal temperature of the thickest part of the meat without touching any bone. The recommended temperature for game meat varies depending on the type and preference of the meat, but generally it ranges from 145°F (63°C) for medium-rare venison, elk, or boar, to 165°F (74°C) for well-done duck, pheasant, or rabbit.</li>
-<li>Q: How do I tenderize game meat?</li>
-<li>A: Game meat can be tenderized by marinating it in a liquid mixture of acid, oil, and seasonings for at least 4 hours or up to 24 hours in the refrigerator. The acid helps break down the tough fibers and connective tissues in the game meat, making it more tender and juicy. You can also tenderize game meat by pounding it with a meat mallet or scoring it with a knife before cooking.</li>
-<li>Q: How do I prevent game meat from drying out?</li>
-<li>A: Game meat can dry out easily due to its lower fat content and higher muscle density. To prevent game meat from drying out, you should avoid overcooking it by using a meat thermometer and checking the doneness frequently. You should also add some liquid, such as water, broth, wine, or juice, to the cooking pan or pot to create steam and prevent evaporation. You should also cover the game meat with foil or a lid to retain moisture and prevent burning.</li>
-<li>Q: What are some good recipes for game meat?</li> <li>A: There are many good recipes for game meat that you can find online or in cookbooks. Some of the most popular ones are:</li>
-</ul>
-<ul>
-<li>Venison Stew: A hearty and comforting dish that combines tender chunks of venison with potatoes, carrots, onions, and herbs in a rich and savory broth. You can make this dish in a slow cooker, a pressure cooker, or a Dutch oven.</li>
-<li>Rabbit Pie: A classic and delicious dish that features succulent pieces of rabbit with bacon, mushrooms, onions, and herbs in a creamy and cheesy sauce, topped with a flaky and golden crust. You can make this dish in a pie dish, a casserole dish, or individual ramekins.</li>
-<li>Duck Confit: A fancy and flavorful dish that involves slowly cooking duck legs in their own fat until they are crisp and tender. You can make this dish in a skillet, a baking dish, or a sous vide machine.</li>
-<li>Pheasant Casserole: A cozy and warming dish that combines pheasant breasts with leeks, celery, carrots, apples, and cider in a creamy and tangy sauce. You can make this dish in a casserole dish, a Dutch oven, or a slow cooker.</li>
-<li>Boar Ribs: A smoky and spicy dish that involves rubbing boar ribs with a mixture of brown sugar, paprika, garlic, salt, pepper, and cumin, and then grilling them over low heat until they are charred and tender. You can make this dish on a gas grill, a charcoal grill, or a smoker.</li>
-<li>Elk Burgers: A simple and satisfying dish that involves shaping ground elk meat into patties and then grilling them over high heat until they are juicy and cooked to your liking. You can make this dish on a grill pan, a cast iron skillet, or a barbecue.</li>
-</ul>
- <p>I hope you enjoyed this article on how to cook game meat like a pro. If you have any questions or comments, please feel free to leave them below. Happy cooking!</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git "a/spaces/fkhuggingme/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py" "b/spaces/fkhuggingme/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py"
deleted file mode 100644
index 2f4201438c4d8597c251726fe99c02d40f0cadf0..0000000000000000000000000000000000000000
--- "a/spaces/fkhuggingme/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py"
+++ /dev/null
@@ -1,166 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-import re
-import unicodedata
-fast_debug = False
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-def is_paragraph_break(match):
-    """
-    根据给定的匹配结果来判断换行符是否表示段落分隔。
-    如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
-    也可以根据之前的内容长度来判断段落是否已经足够长。
-    """
-    prev_char, next_char = match.groups()
-
-    # 句子结束标志
-    sentence_endings = ".!?"
-
-    # 设定一个最小段落长度阈值
-    min_paragraph_length = 140
-
-    if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
-        return "\n\n" 
-    else:
-        return " "
-
-def normalize_text(text):
-    """
-    通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
-    例如，将连字 "fi" 转换为 "f" 和 "i"。
-    """
-    # 对文本进行归一化处理，分解连字
-    normalized_text = unicodedata.normalize("NFKD", text)
-
-    # 替换其他特殊字符
-    cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
-
-    return cleaned_text
-
-def clean_text(raw_text):
-    """
-    对从 PDF 提取出的原始文本进行清洗和格式化处理。
-    1. 对原始文本进行归一化处理。
-    2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
-    3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
-    """
-    # 对文本进行归一化处理
-    normalized_text = normalize_text(raw_text)
-
-    # 替换跨行的连词
-    text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
-
-    # 根据前后相邻字符的特点，找到原文本中的换行符
-    newlines = re.compile(r'(\S)\n(\S)')
-
-    # 根据 heuristic 规则，用空格或段落分隔符替换原换行符
-    final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
-
-    return final_text.strip()
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os, fitz
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with fitz.open(fp) as doc:
-            file_content = ""
-            for page in doc:
-                file_content += page.get_text()
-            file_content = clean_text(file_content)
-            print(file_content)
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )  # 带超时倒计时
-                
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, 
-            inputs_show_user=i_say, 
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, 
-            history=history,
-            sys_prompt="总结文章。"
-        )  # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-@CatchException
-def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/floriankrempl/mtg_rules_bot/mtg/utils/logging.py b/spaces/floriankrempl/mtg_rules_bot/mtg/utils/logging.py
deleted file mode 100644
index 5f7a7539d0bffbde9fc5da31300021e629b65e14..0000000000000000000000000000000000000000
--- a/spaces/floriankrempl/mtg_rules_bot/mtg/utils/logging.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import logging
-
-
-class CustomFormatter(logging.Formatter):
-    grey = "\x1b[38;20m"
-    yellow = "\x1b[33;20m"
-    red = "\x1b[31;20m"
-    bold_red = "\x1b[31;1m"
-    reset = "\x1b[0m"
-    format = (
-        "%(asctime)s - %(name)s - %(levelname)s - %(message)s (%(filename)s:%(lineno)d)"
-    )
-
-    FORMATS = {
-        logging.DEBUG: grey + format + reset,
-        logging.INFO: grey + format + reset,
-        logging.WARNING: yellow + format + reset,
-        logging.ERROR: red + format + reset,
-        logging.CRITICAL: bold_red + format + reset,
-    }
-
-    def format(self, record):
-        log_fmt = self.FORMATS.get(record.levelno)
-        formatter = logging.Formatter(log_fmt)
-        return formatter.format(record)
-
-
-def get_logger(name="mtg-bot"):
-    # logger
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-
-    # file handler
-    file_handler = logging.FileHandler("chat_logs.log", mode="a")
-    file_handler.setLevel(logging.DEBUG)
-    file_handler.setFormatter(CustomFormatter())
-
-    # create console handler with a higher log level
-    console_handler = logging.StreamHandler()
-    console_handler.setLevel(logging.DEBUG)
-    console_handler.setFormatter(CustomFormatter())
-
-    # add handlers
-    logger.addHandler(file_handler)
-    logger.addHandler(console_handler)
-
-    return logger
diff --git a/spaces/gaviego/mnist/app_conv.py b/spaces/gaviego/mnist/app_conv.py
deleted file mode 100644
index 9a4526c7ab28f5793bb7f44c90ebc6f617d2088f..0000000000000000000000000000000000000000
--- a/spaces/gaviego/mnist/app_conv.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import gradio as gr
-from PIL import Image
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from models import NetConv
-
-    
-net_conv = torch.load('mnist_conv.pth')
-net_conv.eval()
-
-def predict(img):
-    arr = np.array(img) / 255  # Assuming img is in the range [0, 255]
-    arr.reshape(28,28)
-    arr = np.expand_dims(arr, axis=0)  # Add batch dimension
-    arr = np.expand_dims(arr, axis=0)  # Add batch dimension
-    arr = torch.from_numpy(arr).float()  # Convert to PyTorch tensor
-    output = net_conv(arr)
-    topk_values, topk_indices = torch.topk(output, 2)  # Get the top 2 classes
-    return [str(k) for k in topk_indices[0].tolist()]
-
-with gr.Blocks() as iface:
-    gr.Markdown("# MNIST + Gradio End to End")
-    gr.HTML("Shows end to end MNIST training with Gradio interface")
-    with gr.Row():
-        with gr.Column():
-            sp = gr.Sketchpad(shape=(28, 28))
-            with gr.Row():
-                with gr.Column():
-                    pred_button = gr.Button("Predict")
-                with gr.Column():
-                    clear = gr.Button("Clear")
-        with gr.Column():
-            label1 = gr.Label(label='1st Pred')
-            label2 = gr.Label(label='2nd Pred')
-
-    pred_button.click(predict, inputs=sp, outputs=[label1,label2])
-    clear.click(lambda: None, None, sp, queue=False)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/ggwvits/vits-uma-genshin-honkai/models.py b/spaces/ggwvits/vits-uma-genshin-honkai/models.py
deleted file mode 100644
index 52e15d1b9775038fd6e82b2efe6f95f51c66802d..0000000000000000000000000000000000000000
--- a/spaces/ggwvits/vits-uma-genshin-honkai/models.py
+++ /dev/null
@@ -1,534 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    device = next(self.parameters()).device  # 获取模型所在的设备
-    x, m_p, logs_p, x_mask = self.enc_p(x.to(device), x_lengths.to(device))
-    if self.n_speakers > 0:
-      g = self.emb_g(sid.to(device)).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/giswqs/solara-maxar/Dockerfile b/spaces/giswqs/solara-maxar/Dockerfile
deleted file mode 100644
index 271b19ce6fd8f70d42243166136a200328b1fd0f..0000000000000000000000000000000000000000
--- a/spaces/giswqs/solara-maxar/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM jupyter/base-notebook:latest
-
-RUN mamba install -c conda-forge leafmap geopandas localtileserver -y && \
-    fix-permissions "${CONDA_DIR}" && \
-    fix-permissions "/home/${NB_USER}"
-
-COPY requirements.txt .
-RUN pip install -r requirements.txt
-
-RUN mkdir ./pages
-COPY /pages ./pages
-
-ENV PROJ_LIB='/opt/conda/share/proj'
-
-USER root
-RUN chown -R ${NB_UID} ${HOME}
-USER ${NB_USER}
-
-EXPOSE 8765
-
-CMD ["solara", "run", "./pages", "--host=0.0.0.0"]
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Canon In D Classical Guitar Tab Pdf 57 Gigante Regata Initr REPACK.md b/spaces/gotiQspiryo/whisper-ui/examples/Canon In D Classical Guitar Tab Pdf 57 Gigante Regata Initr REPACK.md
deleted file mode 100644
index 8dc33287cd7e97ee51d8d6025916c82e06d1a0a8..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Canon In D Classical Guitar Tab Pdf 57 Gigante Regata Initr REPACK.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Canon In D Classical Guitar Tab Pdf 57 gigante regata initr</h2><br /><p><b><b>DOWNLOAD</b> &#10027; <a href="https://urlgoal.com/2uyMw6">https://urlgoal.com/2uyMw6</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gpecile/encrypted-image-recognition/app.py b/spaces/gpecile/encrypted-image-recognition/app.py
deleted file mode 100644
index 90c915bfbbab11f8a8dfe14447365cee618680e7..0000000000000000000000000000000000000000
--- a/spaces/gpecile/encrypted-image-recognition/app.py
+++ /dev/null
@@ -1,506 +0,0 @@
-"""A local gradio app that filters images using FHE."""
-
-import os
-import shutil
-import subprocess
-import time
-import gradio as gr
-import numpy
-import requests
-from itertools import chain
-
-from common import (
-    AVAILABLE_FILTERS,
-    CLIENT_TMP_PATH,
-    SERVER_TMP_PATH,
-    EXAMPLES,
-    FILTERS_PATH,
-    INPUT_SHAPE,
-    KEYS_PATH,
-    REPO_DIR,
-    SERVER_URL,
-)
-from client_server_interface import FHEClient
-
-# Uncomment here to have both the server and client in the same terminal
-subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR)
-time.sleep(3)
-
-
-def decrypt_output_with_wrong_key(encrypted_image, filter_name):
-    """Decrypt the encrypted output using a different private key.
-    """
-    # Retrieve the filter's deployment path
-    filter_path = FILTERS_PATH / f"{filter_name}/deployment"
-
-    # Instantiate the client interface and generate a new private key
-    wrong_client = FHEClient(filter_path, filter_name)
-    wrong_client.generate_private_and_evaluation_keys(force=True)
-
-    # Deserialize, decrypt and post-process the encrypted output using the new private key
-    output_image = wrong_client.deserialize_decrypt_post_process(encrypted_image)
-
-    # For filters that are expected to output black and white images, generate two other random 
-    # channels for better display 
-    if filter_name in ["black and white", "ridge detection"]:
-        # Green channel
-        wrong_client.generate_private_and_evaluation_keys(force=True)
-        output_image[:, :, 1] = wrong_client.deserialize_decrypt_post_process(encrypted_image)[:, :, 0]
-
-        # Blue channel
-        wrong_client.generate_private_and_evaluation_keys(force=True)
-        output_image[:, :, 2] = wrong_client.deserialize_decrypt_post_process(encrypted_image)[:, :, 0]
-
-    return output_image
-
-
-def shorten_bytes_object(bytes_object, limit=500):
-    """Shorten the input bytes object to a given length.
-
-    Encrypted data is too large for displaying it in the browser using Gradio. This function
-    provides a shorten representation of it.
-
-    Args:
-        bytes_object (bytes): The input to shorten
-        limit (int): The length to consider. Default to 500.
-
-    Returns:
-        str: Hexadecimal string shorten representation of the input byte object. 
-
-    """
-    # Define a shift for better display
-    shift = 100
-    return bytes_object[shift : limit + shift].hex()
-
-
-def get_client(user_id, filter_name):
-    """Get the client API.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The filter chosen by the user
-
-    Returns:
-        FHEClient: The client API.
-    """
-    return FHEClient(
-        FILTERS_PATH / f"{filter_name}/deployment", 
-        filter_name,
-        key_dir=KEYS_PATH / f"{filter_name}_{user_id}", 
-    )
-
-
-def get_client_file_path(name, user_id, filter_name):
-    """Get the correct temporary file path for the client.
-
-    Args:
-        name (str): The desired file name.
-        user_id (int): The current user's ID.
-        filter_name (str): The filter chosen by the user
-
-    Returns:
-        pathlib.Path: The file path.
-    """
-    return CLIENT_TMP_PATH / f"{name}_{filter_name}_{user_id}"
-
-
-def clean_temporary_files(n_keys=20):
-    """Clean keys and encrypted images.
-
-    A maximum of n_keys keys and associated temporary files are allowed to be stored. Once this 
-    limit is reached, the oldest files are deleted.
-
-    Args:
-        n_keys (int): The maximum number of keys and associated files to be stored. Default to 20.
-
-    """
-    # Get the oldest key files in the key directory
-    key_dirs = sorted(KEYS_PATH.iterdir(), key=os.path.getmtime)
-
-    # If more than n_keys keys are found, remove the oldest
-    user_ids = []
-    if len(key_dirs) > n_keys:
-        n_keys_to_delete = len(key_dirs) - n_keys
-        for key_dir in key_dirs[:n_keys_to_delete]:
-            user_ids.append(key_dir.name)
-            shutil.rmtree(key_dir)
-
-    # Get all the encrypted objects in the temporary folder
-    client_files = CLIENT_TMP_PATH.iterdir()
-    server_files = SERVER_TMP_PATH.iterdir()
-
-    # Delete all files related to the ids whose keys were deleted
-    for file in chain(client_files, server_files):
-        for user_id in user_ids:
-            if user_id in file.name:
-                file.unlink()
-
-
-def keygen(filter_name):
-    """Generate the private key associated to a filter.
-
-    Args:
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        (user_id, True) (Tuple[int, bool]): The current user's ID and a boolean used for visual display.
-
-    """
-    # Clean temporary files
-    clean_temporary_files()
-
-    # Create an ID for the current user
-    user_id = numpy.random.randint(0, 2**32)
-
-    # Retrieve the client API
-    client = get_client(user_id, filter_name)
-
-    # Generate a private key
-    client.generate_private_and_evaluation_keys(force=True)
-
-    # Retrieve the serialized evaluation key. In this case, as circuits are fully leveled, this
-    # evaluation key is empty. However, for software reasons, it is still needed for proper FHE
-    # execution
-    evaluation_key = client.get_serialized_evaluation_keys()
-
-    # Save evaluation_key as bytes in a file as it is too large to pass through regular Gradio
-    # buttons (see https://github.com/gradio-app/gradio/issues/1877)
-    evaluation_key_path = get_client_file_path("evaluation_key", user_id, filter_name)
-
-    with evaluation_key_path.open("wb") as evaluation_key_file:
-        evaluation_key_file.write(evaluation_key)
-
-    return (user_id, True)
-
-
-def encrypt(user_id, input_image, filter_name):
-    """Encrypt the given image for a specific user and filter.
-
-    Args:
-        user_id (int): The current user's ID.
-        input_image (numpy.ndarray): The image to encrypt.
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        (input_image, encrypted_image_short) (Tuple[bytes]): The encrypted image and one of its
-        representation.
-
-    """
-    if user_id == "":
-        raise gr.Error("Please generate the private key first.")
-
-    if input_image is None:
-        raise gr.Error("Please choose an image first.")
-
-    # Retrieve the client API
-    client = get_client(user_id, filter_name)
-
-    # Pre-process, encrypt and serialize the image
-    encrypted_image = client.encrypt_serialize(input_image)
-
-    # Save encrypted_image to bytes in a file, since too large to pass through regular Gradio
-    # buttons, https://github.com/gradio-app/gradio/issues/1877
-    encrypted_image_path = get_client_file_path("encrypted_image", user_id, filter_name)
-
-    with encrypted_image_path.open("wb") as encrypted_image_file:
-        encrypted_image_file.write(encrypted_image)
-
-    # Create a truncated version of the encrypted image for display
-    encrypted_image_short = shorten_bytes_object(encrypted_image)
-
-    return (input_image, encrypted_image_short)
-
-
-def send_input(user_id, filter_name):
-    """Send the encrypted input image as well as the evaluation key to the server.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-    """
-    # Get the evaluation key path
-    evaluation_key_path = get_client_file_path("evaluation_key", user_id, filter_name)
-
-    if user_id == "" or not evaluation_key_path.is_file():
-        raise gr.Error("Please generate the private key first.")
-
-    encrypted_input_path = get_client_file_path("encrypted_image", user_id, filter_name)
-
-    if not encrypted_input_path.is_file():
-        raise gr.Error("Please generate the private key and then encrypt an image first.")
-
-    # Define the data and files to post
-    data = {
-        "user_id": user_id,
-        "filter": filter_name,
-    }
-
-    files = [
-        ("files", open(encrypted_input_path, "rb")),
-        ("files", open(evaluation_key_path, "rb")),
-    ]
-
-    # Send the encrypted input image and evaluation key to the server
-    url = SERVER_URL + "send_input"
-    with requests.post(
-        url=url,
-        data=data,
-        files=files,
-    ) as response:
-        return response.ok
-
-
-def run_fhe(user_id, filter_name):
-    """Apply the filter on the encrypted image previously sent using FHE.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-    """
-    data = {
-        "user_id": user_id,
-        "filter": filter_name,
-    }
-
-    # Trigger the FHE execution on the encrypted image previously sent
-    url = SERVER_URL + "run_fhe"
-    with requests.post(
-        url=url,
-        data=data,
-    ) as response:
-        if response.ok:
-            return response.json()
-        else:
-            raise gr.Error("Please wait for the input image to be sent to the server.")
-
-
-def get_output(user_id, filter_name):
-    """Retrieve the encrypted output image.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        encrypted_output_image_short (bytes): A representation of the encrypted result.
-
-    """
-    data = {
-        "user_id": user_id,
-        "filter": filter_name,
-    }
-
-    # Retrieve the encrypted output image
-    url = SERVER_URL + "get_output"
-    with requests.post(
-        url=url,
-        data=data,
-    ) as response:
-        if response.ok:
-            encrypted_output = response.content
-
-            # Save the encrypted output to bytes in a file as it is too large to pass through regular
-            # Gradio buttons (see https://github.com/gradio-app/gradio/issues/1877)
-            encrypted_output_path = get_client_file_path("encrypted_output", user_id, filter_name)
-
-            with encrypted_output_path.open("wb") as encrypted_output_file:
-                encrypted_output_file.write(encrypted_output)
-
-            # Decrypt the image using a different (wrong) key for display
-            output_image_representation = decrypt_output_with_wrong_key(encrypted_output, filter_name)
-
-            return output_image_representation
-        else:
-            raise gr.Error("Please wait for the FHE execution to be completed.")
-
-
-def decrypt_output(user_id, filter_name):
-    """Decrypt the result.
-
-    Args:
-        user_id (int): The current user's ID.
-        filter_name (str): The current filter to consider.
-
-    Returns:
-        (output_image, False, False) ((Tuple[numpy.ndarray, bool, bool]): The decrypted output, as
-            well as two booleans used for resetting Gradio checkboxes
-
-    """
-    if user_id == "":
-        raise gr.Error("Please generate the private key first.")
-
-    # Get the encrypted output path
-    encrypted_output_path = get_client_file_path("encrypted_output", user_id, filter_name)
-
-    if not encrypted_output_path.is_file():
-        raise gr.Error("Please run the FHE execution first.")
-
-    # Load the encrypted output as bytes
-    with encrypted_output_path.open("rb") as encrypted_output_file:
-        encrypted_output_image = encrypted_output_file.read()
-
-    # Retrieve the client API
-    client = get_client(user_id, filter_name)
-
-    # Deserialize, decrypt and post-process the encrypted output
-    output_image = client.deserialize_decrypt_post_process(encrypted_output_image)
-
-    return output_image, False, False
-
-
-demo = gr.Blocks()
-
-
-print("Starting the demo...")
-with demo:
-    gr.Markdown("## Client side")
-    gr.Markdown("### Step 1: Upload an image. ")
-    gr.Markdown(
-        f"The image will automatically be resized to shape ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}). "
-        "The image here, however, is displayed in its original resolution. The true image used "
-        "in this demo can be seen in Step 8."
-    )
-    with gr.Row():
-        input_image = gr.Image(
-            value=None, label="Upload an image here.", shape=INPUT_SHAPE, source="upload", interactive=True,
-        )
-
-        examples = gr.Examples(
-            examples=EXAMPLES, inputs=[input_image], examples_per_page=5, label="Examples to use."
-        )
-
-    gr.Markdown("### Step 2: Choose your filter.")
-    filter_name = gr.Dropdown(
-        choices=AVAILABLE_FILTERS, value="inverted", label="Choose your filter", interactive=True
-    )
-
-    gr.Markdown("#### Notes")
-    gr.Markdown(
-        """
-        - The private key is used to encrypt and decrypt the data and will never be shared.
-        - No public key is required for these filter operators.
-        """
-    )
-    
-    gr.Markdown("### Step 3: Generate the private key.")
-    keygen_button = gr.Button("Generate the private key.")
-
-    with gr.Row():
-        keygen_checkbox = gr.Checkbox(label="Private key generated:", interactive=False)
-
-    user_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)
-    
-    gr.Markdown("### Step 4: Encrypt the image using FHE.")
-    encrypt_button = gr.Button("Encrypt the image using FHE.")
-
-    with gr.Row():
-        encrypted_input = gr.Textbox(
-            label="Encrypted input representation:", max_lines=2, interactive=False
-        )
-
-    gr.Markdown("## Server side")
-    gr.Markdown(
-        "The encrypted value is received by the server. The server can then compute the filter "
-        "directly over encrypted values. Once the computation is finished, the server returns "
-        "the encrypted results to the client."
-    )
-
-    gr.Markdown("### Step 5: Send the encrypted image to the server.")
-    send_input_button = gr.Button("Send the encrypted image to the server.")
-    send_input_checkbox = gr.Checkbox(label="Encrypted image sent.", interactive=False)
-
-    gr.Markdown("### Step 6: Run FHE execution.")
-    execute_fhe_button = gr.Button("Run FHE execution.")
-    fhe_execution_time = gr.Textbox(
-        label="Total FHE execution time (in seconds):", max_lines=1, interactive=False
-    )
-
-    gr.Markdown("### Step 7: Receive the encrypted output image from the server.")
-    gr.Markdown(
-        "The image displayed here is the encrypted result sent by the server, which has been "
-        "decrypted using a different private key. This is only used to visually represent an "
-        "encrypted image."
-    )
-    get_output_button = gr.Button("Receive the encrypted output image from the server.")
-
-    with gr.Row():
-        encrypted_output_representation = gr.Image(
-            label=f"Encrypted output representation ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}):", 
-            interactive=False,
-            height=256,
-            width=256,
-        )
-
-    gr.Markdown("## Client side")
-    gr.Markdown(
-        "The encrypted output is sent back to the client, who can finally decrypt it with the "
-        "private key. Only the client is aware of the original image and its transformed version."
-    )
-
-    gr.Markdown("### Step 8: Decrypt the output.")
-    gr.Markdown(
-        "The image displayed on the left is the input image used during the demo. The output image "
-        "can be seen on the right."
-    )   
-    decrypt_button = gr.Button("Decrypt the output")
-
-    # Final input vs output display
-    with gr.Row():
-        original_image = gr.Image(
-            input_image.value,
-            label=f"Input image ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}):",
-            interactive=False,
-            height=256,
-            width=256,   
-        )
-
-        output_image = gr.Image(
-            label=f"Output image ({INPUT_SHAPE[0]}x{INPUT_SHAPE[1]}):", 
-            interactive=False,
-            height=256,
-            width=256,
-        )
-
-    # Button to generate the private key
-    keygen_button.click(
-        keygen,
-        inputs=[filter_name],
-        outputs=[user_id, keygen_checkbox],
-    )
-
-    # Button to encrypt inputs on the client side
-    encrypt_button.click(
-        encrypt,
-        inputs=[user_id, input_image, filter_name],
-        outputs=[original_image, encrypted_input],
-    )
-
-    # Button to send the encodings to the server using post method
-    send_input_button.click(
-        send_input, inputs=[user_id, filter_name], outputs=[send_input_checkbox]
-    )
-
-    # Button to send the encodings to the server using post method
-    execute_fhe_button.click(run_fhe, inputs=[user_id, filter_name], outputs=[fhe_execution_time])
-
-    # Button to send the encodings to the server using post method
-    get_output_button.click(
-        get_output, 
-        inputs=[user_id, filter_name], 
-        outputs=[encrypted_output_representation]
-    )
-
-    # Button to decrypt the output on the client side
-    decrypt_button.click(
-        decrypt_output,
-        inputs=[user_id, filter_name],
-        outputs=[output_image, keygen_checkbox, send_input_checkbox],
-    )
-
-    gr.Markdown(
-        "The app was built with [Concrete-ML](https://github.com/zama-ai/concrete-ml), a "
-        "Privacy-Preserving Machine Learning (PPML) open-source set of tools by [Zama](https://zama.ai/). "
-        "Try it yourself and don't forget to star on Github &#11088;."
-    )
-
-demo.launch(share=True)
diff --git a/spaces/gradio/multiple-api-name-test/README.md b/spaces/gradio/multiple-api-name-test/README.md
deleted file mode 100644
index 7bdbc69aa0f8f6de81b1fd764a8bfccc117a95a2..0000000000000000000000000000000000000000
--- a/spaces/gradio/multiple-api-name-test/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multiple Api Name Test
-emoji: 🐢
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gradio/video_identity/run.py b/spaces/gradio/video_identity/run.py
deleted file mode 100644
index 206f47c7c2f6fc19afde9dceda5f67fc8ce0d4df..0000000000000000000000000000000000000000
--- a/spaces/gradio/video_identity/run.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-import os
-
-
-def video_identity(video):
-    return video
-
-
-demo = gr.Interface(video_identity, 
-                    gr.Video(), 
-                    "playable_video", 
-                    examples=[
-                        os.path.join(os.path.dirname(__file__), 
-                                     "video/video_sample.mp4")], 
-                    cache_examples=True)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/h2oai/h2ogpt-chatbot2/app.py b/spaces/h2oai/h2ogpt-chatbot2/app.py
deleted file mode 100644
index d4bb1f140028f8d79d99dce983e4fd15522be605..0000000000000000000000000000000000000000
--- a/spaces/h2oai/h2ogpt-chatbot2/app.py
+++ /dev/null
@@ -1 +0,0 @@
-generate.py
\ No newline at end of file
diff --git a/spaces/haakohu/deep_privacy2/dp2/detection/__init__.py b/spaces/haakohu/deep_privacy2/dp2/detection/__init__.py
deleted file mode 100644
index 613969b28384cd1c64fc8db685e7622f4cc02615..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/dp2/detection/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .cse_mask_face_detector import CSeMaskFaceDetector
-from .person_detector import CSEPersonDetector
-from .structures import PersonDetection, VehicleDetection, FaceDetection
diff --git a/spaces/hahahafofo/ChatGLM-Chinese-Summary/utils/singleton.py b/spaces/hahahafofo/ChatGLM-Chinese-Summary/utils/singleton.py
deleted file mode 100644
index ca13d0039db89c95f44b12772737b65fd5cc3dcf..0000000000000000000000000000000000000000
--- a/spaces/hahahafofo/ChatGLM-Chinese-Summary/utils/singleton.py
+++ /dev/null
@@ -1,37 +0,0 @@
-class Singleton:
-    """
-    A non-thread-safe helper class to ease implementing singletons.
-    This should be used as a decorator -- not a metaclass -- to the
-    class that should be a singleton.
-
-    The decorated class can define one `__init__` function that
-    takes only the `self` argument. Also, the decorated class cannot be
-    inherited from. Other than that, there are no restrictions that apply
-    to the decorated class.
-
-    To get the singleton instance, use the `instance` method. Trying
-    to use `__call__` will result in a `TypeError` being raised.
-
-    """
-
-    def __init__(self, decorated):
-        self._decorated = decorated
-
-    def instance(self):
-        """
-        Returns the singleton instance. Upon its first call, it creates a
-        new instance of the decorated class and calls its `__init__` method.
-        On all subsequent calls, the already created instance is returned.
-
-        """
-        try:
-            return self._instance
-        except AttributeError:
-            self._instance = self._decorated()
-            return self._instance
-
-    def __call__(self):
-        raise TypeError('Singletons must be accessed through `instance()`.')
-
-    def __instancecheck__(self, inst):
-        return isinstance(inst, self._decorated)
\ No newline at end of file
diff --git a/spaces/hallll/text_image_forgery_detection/image_forgery_detection/models/__init__.py b/spaces/hallll/text_image_forgery_detection/image_forgery_detection/models/__init__.py
deleted file mode 100644
index cb4c91df93674fdb3ed42662cb9bd5f180fdbc35..0000000000000000000000000000000000000000
--- a/spaces/hallll/text_image_forgery_detection/image_forgery_detection/models/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from .builder import build_detector, build_loss, build_base_model, build_backbone
-from .detector import Detector
-from .losses import BinaryDiceLoss, CrossEntropyLoss, FocalLoss
-from .pan import PAN
-from .deeplabv3 import DeepLabV3, DeepLabV3Plus
-from .fpn import FPN
-from .hooks import CosineAnnealingWarmRestartsLrUpdaterHook
-from .pvtv2 import PVTv2B2, PVTv2B0
-from .transforensics import TransForensics, TransForensics_v2, TransForensics_v3, TransForensics_v4
-from .transforensics_detector import TransForensicsDetector
-from .psccnet import PSCCNet
-from .psccnet_detector import PSCCDetector
-from .network_CAT import CAT_Net, CAT_Net_ORI
-
-
-__all__ = ['build_detector', 'build_loss', 'build_base_model', 'FocalLoss',
-           'Detector', 'BinaryDiceLoss', 'CrossEntropyLoss', 'PAN', 'DeepLabV3',
-           'DeepLabV3Plus', 'FPN', 'CosineAnnealingWarmRestartsLrUpdaterHook',
-           'PVTv2B2', 'PVTv2B0', 
-           'build_backbone', 
-           'TransForensics', 'TransForensicsDetector', 'TransForensics_v2', 'TransForensics_v3']
diff --git a/spaces/haonanzhang/ChatGPT-BOT/app.py b/spaces/haonanzhang/ChatGPT-BOT/app.py
deleted file mode 100644
index d82dafc2acbb3cd30f01fdb36ed58316d1a3995c..0000000000000000000000000000000000000000
--- a/spaces/haonanzhang/ChatGPT-BOT/app.py
+++ /dev/null
@@ -1,452 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-import logging
-import sys
-
-import gradio as gr
-
-from modules.utils import *
-from modules.presets import *
-from modules.overwrites import *
-from modules.chat_func import *
-from modules.openai_func import get_usage
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-my_api_key = "" #"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"  # 在这里输入你的 API 密钥
-
-# if we are running in Docker
-if os.environ.get("dockerrun") == "yes":
-    dockerflag = True
-else:
-    dockerflag = False
-
-authflag = False
-auth_list = []
-
-if not my_api_key:
-    my_api_key = os.environ.get("my_api_key")
-if dockerflag:
-    if my_api_key == "empty":
-        logging.error("Please give a api key!")
-        sys.exit(1)
-    # auth
-    username = os.environ.get("USERNAME")
-    password = os.environ.get("PASSWORD")
-    if not (isinstance(username, type(None)) or isinstance(password, type(None))):
-        auth_list.append((os.environ.get("USERNAME"), os.environ.get("PASSWORD")))
-        authflag = True
-else:
-    if (
-        not my_api_key
-        and os.path.exists("api_key.txt")
-        and os.path.getsize("api_key.txt")
-    ):
-        with open("api_key.txt", "r") as f:
-            my_api_key = f.read().strip()
-    if os.path.exists("auth.json"):
-        authflag = True
-        with open("auth.json", "r", encoding='utf-8') as f:
-            auth = json.load(f)
-            for _ in auth:
-                if auth[_]["username"] and auth[_]["password"]:
-                    auth_list.append((auth[_]["username"], auth[_]["password"]))
-                else:
-                    logging.error("请检查auth.json文件中的用户名和密码！")
-                    sys.exit(1)
-
-gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
-
-with open("assets/custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-
-with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
-    history = gr.State([])
-    token_count = gr.State([])
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    user_api_key = gr.State(my_api_key)
-    user_question = gr.State("")
-    outputing = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.HTML(title)
-        with gr.Column(scale=4):
-            gr.HTML('<center><a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>')
-        with gr.Column(scale=4):
-            status_display = gr.Markdown(get_geoip(), elem_id="status_display")
-
-    with gr.Row().style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row():
-                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="100%")
-            with gr.Row():
-                with gr.Column(scale=12):
-                    user_input = gr.Textbox(
-                        show_label=False, placeholder="在这里输入"
-                    ).style(container=False)
-                with gr.Column(min_width=70, scale=1):
-                    submitBtn = gr.Button("发送", variant="primary")
-                    cancelBtn = gr.Button("取消", variant="secondary", visible=False)
-            with gr.Row():
-                emptyBtn = gr.Button(
-                    "🧹 新的对话",
-                )
-                retryBtn = gr.Button("🔄 重新生成")
-                delFirstBtn = gr.Button("🗑️ 删除最旧对话")
-                delLastBtn = gr.Button("🗑️ 删除最新对话")
-                reduceTokenBtn = gr.Button("♻️ 总结对话")
-
-        with gr.Column():
-            with gr.Column(min_width=50, scale=1):
-                with gr.Tab(label="ChatGPT"):
-                    keyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"OpenAI API-key...",
-                        value=hide_middle_chars(my_api_key),
-                        type="password",
-                        visible=not HIDE_MY_KEY,
-                        label="API-Key",
-                    )
-                    usageTxt = gr.Markdown("**发送消息** 或 **提交key** 以显示额度", elem_id="usage_display")
-                    model_select_dropdown = gr.Dropdown(
-                        label="选择模型", choices=MODELS, multiselect=False, value=MODELS[0]
-                    )
-                    use_streaming_checkbox = gr.Checkbox(
-                        label="实时传输回答", value=True, visible=enable_streaming_option
-                    )
-                    use_websearch_checkbox = gr.Checkbox(label="使用在线搜索", value=False)
-                    language_select_dropdown = gr.Dropdown(
-                        label="选择回复语言（针对搜索&索引功能）",
-                        choices=REPLY_LANGUAGES,
-                        multiselect=False,
-                        value=REPLY_LANGUAGES[0],
-                    )
-                    index_files = gr.Files(label="上传索引文件", type="file", multiple=True)
-
-                with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"在这里输入System Prompt...",
-                        label="System prompt",
-                        value=initial_prompt,
-                        lines=10,
-                    ).style(container=False)
-                    with gr.Accordion(label="加载Prompt模板", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(
-                                        label="选择Prompt模板集合文件",
-                                        choices=get_template_names(plain=True),
-                                        multiselect=False,
-                                        value=get_template_names(plain=True)[0],
-                                    ).style(container=False)
-                                with gr.Column(scale=1):
-                                    templateRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(
-                                        label="从Prompt模板中加载",
-                                        choices=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        ),
-                                        multiselect=False,
-                                        value=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        )[0],
-                                    ).style(container=False)
-
-                with gr.Tab(label="保存/加载"):
-                    with gr.Accordion(label="保存/加载对话历史记录", open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(
-                                        label="从列表中加载对话",
-                                        choices=get_history_names(plain=True),
-                                        multiselect=False,
-                                        value=get_history_names(plain=True)[0],
-                                    )
-                                with gr.Column(scale=1):
-                                    historyRefreshBtn = gr.Button("🔄 刷新")
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    saveFileName = gr.Textbox(
-                                        show_label=True,
-                                        placeholder=f"设置文件名: 默认为.json，可选为.md",
-                                        label="设置保存文件名",
-                                        value="对话历史记录",
-                                    ).style(container=True)
-                                with gr.Column(scale=1):
-                                    saveHistoryBtn = gr.Button("💾 保存对话")
-                                    exportMarkdownBtn = gr.Button("📝 导出为Markdown")
-                                    gr.Markdown("默认保存于history文件夹")
-                            with gr.Row():
-                                with gr.Column():
-                                    downloadFile = gr.File(interactive=True)
-
-                with gr.Tab(label="高级"):
-                    gr.Markdown("# ⚠️ 务必谨慎更改 ⚠️\n\n如果无法使用请恢复默认设置")
-                    default_btn = gr.Button("🔙 恢复默认设置")
-
-                    with gr.Accordion("参数", open=False):
-                        top_p = gr.Slider(
-                            minimum=-0,
-                            maximum=1.0,
-                            value=1.0,
-                            step=0.05,
-                            interactive=True,
-                            label="Top-p",
-                        )
-                        temperature = gr.Slider(
-                            minimum=-0,
-                            maximum=2.0,
-                            value=1.0,
-                            step=0.1,
-                            interactive=True,
-                            label="Temperature",
-                        )
-
-                    with gr.Accordion("网络设置", open=False, visible=False):
-                        apiurlTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=f"在这里输入API地址...",
-                            label="API地址",
-                            value="https://api.openai.com/v1/chat/completions",
-                            lines=2,
-                        )
-                        changeAPIURLBtn = gr.Button("🔄 切换API地址")
-                        proxyTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=f"在这里输入代理地址...",
-                            label="代理地址（示例：http://127.0.0.1:10809）",
-                            value="",
-                            lines=2,
-                        )
-                        changeProxyBtn = gr.Button("🔄 设置代理地址")
-
-    gr.Markdown(description)
-    gr.HTML(footer.format(versions=versions_html()), elem_id="footer")
-    chatgpt_predict_args = dict(
-        fn=predict,
-        inputs=[
-            user_api_key,
-            systemPromptTxt,
-            history,
-            user_question,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            use_websearch_checkbox,
-            index_files,
-            language_select_dropdown,
-        ],
-        outputs=[chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-
-    start_outputing_args = dict(
-        fn=start_outputing,
-        inputs=[],
-        outputs=[submitBtn, cancelBtn],
-        show_progress=True,
-    )
-
-    end_outputing_args = dict(
-        fn=end_outputing, inputs=[], outputs=[submitBtn, cancelBtn]
-    )
-
-    reset_textbox_args = dict(
-        fn=reset_textbox, inputs=[], outputs=[user_input]
-    )
-
-    transfer_input_args = dict(
-        fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True
-    )
-
-    get_usage_args = dict(
-        fn=get_usage, inputs=[user_api_key], outputs=[usageTxt], show_progress=False
-    )
-
-
-    # Chatbot
-    cancelBtn.click(cancel_outputing, [], [])
-
-    user_input.submit(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
-    user_input.submit(**get_usage_args)
-
-    submitBtn.click(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
-    submitBtn.click(**get_usage_args)
-
-    emptyBtn.click(
-        reset_state,
-        outputs=[chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-    emptyBtn.click(**reset_textbox_args)
-
-    retryBtn.click(**start_outputing_args).then(
-        retry,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            use_streaming_checkbox,
-            model_select_dropdown,
-            language_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    ).then(**end_outputing_args)
-    retryBtn.click(**get_usage_args)
-
-    delFirstBtn.click(
-        delete_first_conversation,
-        [history, token_count],
-        [history, token_count, status_display],
-    )
-
-    delLastBtn.click(
-        delete_last_conversation,
-        [chatbot, history, token_count],
-        [chatbot, history, token_count, status_display],
-        show_progress=True,
-    )
-
-    reduceTokenBtn.click(
-        reduce_token_size,
-        [
-            user_api_key,
-            systemPromptTxt,
-            history,
-            chatbot,
-            token_count,
-            top_p,
-            temperature,
-            gr.State(sum(token_count.value[-4:])),
-            model_select_dropdown,
-            language_select_dropdown,
-        ],
-        [chatbot, history, status_display, token_count],
-        show_progress=True,
-    )
-    reduceTokenBtn.click(**get_usage_args)
-
-    # ChatGPT
-    keyTxt.change(submit_key, keyTxt, [user_api_key, status_display]).then(**get_usage_args)
-    keyTxt.submit(**get_usage_args)
-
-    # Template
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templateFileSelectDropdown.change(
-        load_template,
-        [templateFileSelectDropdown],
-        [promptTemplates, templateSelectDropdown],
-        show_progress=True,
-    )
-    templateSelectDropdown.change(
-        get_template_content,
-        [promptTemplates, templateSelectDropdown, systemPromptTxt],
-        [systemPromptTxt],
-        show_progress=True,
-    )
-
-    # S&L
-    saveHistoryBtn.click(
-        save_chat_history,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    exportMarkdownBtn.click(
-        export_markdown,
-        [saveFileName, systemPromptTxt, history, chatbot],
-        downloadFile,
-        show_progress=True,
-    )
-    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(
-        load_chat_history,
-        [historyFileSelectDropdown, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-        show_progress=True,
-    )
-    downloadFile.change(
-        load_chat_history,
-        [downloadFile, systemPromptTxt, history, chatbot],
-        [saveFileName, systemPromptTxt, history, chatbot],
-    )
-
-    # Advanced
-    default_btn.click(
-        reset_default, [], [apiurlTxt, proxyTxt, status_display], show_progress=True
-    )
-    changeAPIURLBtn.click(
-        change_api_url,
-        [apiurlTxt],
-        [status_display],
-        show_progress=True,
-    )
-    changeProxyBtn.click(
-        change_proxy,
-        [proxyTxt],
-        [status_display],
-        show_progress=True,
-    )
-
-logging.info(
-    colorama.Back.GREEN
-    + "\n温馨提示：访问 http://localhost:7860 查看界面"
-    + colorama.Style.RESET_ALL
-)
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = "ChatGPT-BOT 🍃"
-
-if __name__ == "__main__":
-    reload_javascript()
-    # if running in Docker
-    if dockerflag:
-        if authflag:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                server_name="0.0.0.0",
-                server_port=7860,
-                auth=auth_list,
-                favicon_path="./assets/favicon.ico",
-            )
-        else:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                server_name="0.0.0.0",
-                server_port=7860,
-                share=False,
-                favicon_path="./assets/favicon.ico",
-            )
-    # if not running in Docker
-    else:
-        if authflag:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                share=False,
-                auth=auth_list,
-                favicon_path="./assets/favicon.ico",
-                inbrowser=True,
-            )
-        else:
-            demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-                share=False, favicon_path="./assets/favicon.ico", inbrowser=True
-            )  # 改为 share=True 可以创建公开分享链接
-        # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-        # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-        # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/haotiz/glip-zeroshot-demo/app.py b/spaces/haotiz/glip-zeroshot-demo/app.py
deleted file mode 100644
index 9e35027f63fda77909ec7de6d1e4db03ca9efa36..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import requests
-import os
-from io import BytesIO
-from PIL import Image
-import numpy as np
-from pathlib import Path
-import gradio as gr
-
-import warnings
-
-warnings.filterwarnings("ignore")
-
-# os.system(
-#     "pip install einops shapely timm yacs tensorboardX ftfy prettytable pymongo click opencv-python inflect nltk scipy scikit-learn pycocotools")
-# os.system("pip install transformers")
-os.system("python setup.py build develop --user")
-
-from maskrcnn_benchmark.config import cfg
-from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo
-
-# Use this command for evaluate the GLIP-T model
-config_file = "configs/pretrain/glip_Swin_T_O365_GoldG.yaml"
-weight_file = "MODEL/glip_tiny_model_o365_goldg_cc_sbu.pth"
-
-# Use this command if you want to try the GLIP-L model
-# ! wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_large_model.pth -O MODEL/glip_large_model.pth
-# config_file = "configs/pretrain/glip_Swin_L.yaml"
-# weight_file = "MODEL/glip_large_model.pth"
-
-# update the config options with the config file
-# manual override some options
-cfg.local_rank = 0
-cfg.num_gpus = 1
-cfg.merge_from_file(config_file)
-cfg.merge_from_list(["MODEL.WEIGHT", weight_file])
-cfg.merge_from_list(["MODEL.DEVICE", "cuda"])
-
-glip_demo = GLIPDemo(
-    cfg,
-    min_image_size=800,
-    confidence_threshold=0.7,
-    show_mask_heatmaps=False
-)
-
-
-def predict(image, text):
-    result, _ = glip_demo.run_on_web_image(image[:, :, [2, 1, 0]], text, 0.5)
-    return result[:, :, [2, 1, 0]]
-
-
-image = gr.inputs.Image()
-
-gr.Interface(
-    description="Object Detection in the Wild through GLIP (https://github.com/microsoft/GLIP).",
-    fn=predict,
-    inputs=["image", "text"],
-    outputs=[
-        gr.outputs.Image(
-            type="pil",
-            # label="grounding results"
-        ),
-    ],
-    examples=[
-        ["./flickr_9472793441.jpg", "bobble heads on top of the shelf ."],
-        ["./flickr_9472793441.jpg", "sofa . remote . dog . person . car . sky . plane ."],
-        ["./coco_000000281759.jpg", "A green umbrella. A pink striped umbrella. A plain white umbrella."],
-        ["./coco_000000281759.jpg", "a flowery top. A blue dress. An orange shirt ."],
-        ["./coco_000000281759.jpg", "a car . An electricity box ."],
-        ["./flickr_7520721.jpg", "A woman figure skater in a blue costume holds her leg by the blade of her skate ."]
-    ],
-    article=Path("docs/intro.md").read_text()
-).launch()
-# ).launch(server_name="0.0.0.0", server_port=7000, share=True)
diff --git a/spaces/hbestm/gpt-academic-play/crazy_functions/test_project/python/dqn/policies.py b/spaces/hbestm/gpt-academic-play/crazy_functions/test_project/python/dqn/policies.py
deleted file mode 100644
index 4ecf39a5fc04b24ad1b809232b186728366987b6..0000000000000000000000000000000000000000
--- a/spaces/hbestm/gpt-academic-play/crazy_functions/test_project/python/dqn/policies.py
+++ /dev/null
@@ -1,237 +0,0 @@
-from typing import Any, Dict, List, Optional, Type
-
-import gym
-import torch as th
-from torch import nn
-
-from stable_baselines3.common.policies import BasePolicy, register_policy
-from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
-from stable_baselines3.common.type_aliases import Schedule
-
-
-class QNetwork(BasePolicy):
-    """
-    Action-Value (Q-Value) network for DQN
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        features_extractor: nn.Module,
-        features_dim: int,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        normalize_images: bool = True,
-    ):
-        super(QNetwork, self).__init__(
-            observation_space,
-            action_space,
-            features_extractor=features_extractor,
-            normalize_images=normalize_images,
-        )
-
-        if net_arch is None:
-            net_arch = [64, 64]
-
-        self.net_arch = net_arch
-        self.activation_fn = activation_fn
-        self.features_extractor = features_extractor
-        self.features_dim = features_dim
-        self.normalize_images = normalize_images
-        action_dim = self.action_space.n  # number of actions
-        q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
-        self.q_net = nn.Sequential(*q_net)
-
-    def forward(self, obs: th.Tensor) -> th.Tensor:
-        """
-        Predict the q-values.
-
-        :param obs: Observation
-        :return: The estimated Q-Value for each action.
-        """
-        return self.q_net(self.extract_features(obs))
-
-    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        q_values = self.forward(observation)
-        # Greedy action
-        action = q_values.argmax(dim=1).reshape(-1)
-        return action
-
-    def _get_constructor_parameters(self) -> Dict[str, Any]:
-        data = super()._get_constructor_parameters()
-
-        data.update(
-            dict(
-                net_arch=self.net_arch,
-                features_dim=self.features_dim,
-                activation_fn=self.activation_fn,
-                features_extractor=self.features_extractor,
-            )
-        )
-        return data
-
-
-class DQNPolicy(BasePolicy):
-    """
-    Policy class with Q-Value Net and target net for DQN
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param lr_schedule: Learning rate schedule (could be constant)
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param features_extractor_class: Features extractor to use.
-    :param features_extractor_kwargs: Keyword arguments
-        to pass to the features extractor.
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    :param optimizer_class: The optimizer to use,
-        ``th.optim.Adam`` by default
-    :param optimizer_kwargs: Additional keyword arguments,
-        excluding the learning rate, to pass to the optimizer
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
-        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
-        normalize_images: bool = True,
-        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
-        optimizer_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        super(DQNPolicy, self).__init__(
-            observation_space,
-            action_space,
-            features_extractor_class,
-            features_extractor_kwargs,
-            optimizer_class=optimizer_class,
-            optimizer_kwargs=optimizer_kwargs,
-        )
-
-        if net_arch is None:
-            if features_extractor_class == FlattenExtractor:
-                net_arch = [64, 64]
-            else:
-                net_arch = []
-
-        self.net_arch = net_arch
-        self.activation_fn = activation_fn
-        self.normalize_images = normalize_images
-
-        self.net_args = {
-            "observation_space": self.observation_space,
-            "action_space": self.action_space,
-            "net_arch": self.net_arch,
-            "activation_fn": self.activation_fn,
-            "normalize_images": normalize_images,
-        }
-
-        self.q_net, self.q_net_target = None, None
-        self._build(lr_schedule)
-
-    def _build(self, lr_schedule: Schedule) -> None:
-        """
-        Create the network and the optimizer.
-
-        :param lr_schedule: Learning rate schedule
-            lr_schedule(1) is the initial learning rate
-        """
-
-        self.q_net = self.make_q_net()
-        self.q_net_target = self.make_q_net()
-        self.q_net_target.load_state_dict(self.q_net.state_dict())
-
-        # Setup optimizer with initial learning rate
-        self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
-
-    def make_q_net(self) -> QNetwork:
-        # Make sure we always have separate networks for features extractors etc
-        net_args = self._update_features_extractor(self.net_args, features_extractor=None)
-        return QNetwork(**net_args).to(self.device)
-
-    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        return self._predict(obs, deterministic=deterministic)
-
-    def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
-        return self.q_net._predict(obs, deterministic=deterministic)
-
-    def _get_constructor_parameters(self) -> Dict[str, Any]:
-        data = super()._get_constructor_parameters()
-
-        data.update(
-            dict(
-                net_arch=self.net_args["net_arch"],
-                activation_fn=self.net_args["activation_fn"],
-                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone
-                optimizer_class=self.optimizer_class,
-                optimizer_kwargs=self.optimizer_kwargs,
-                features_extractor_class=self.features_extractor_class,
-                features_extractor_kwargs=self.features_extractor_kwargs,
-            )
-        )
-        return data
-
-
-MlpPolicy = DQNPolicy
-
-
-class CnnPolicy(DQNPolicy):
-    """
-    Policy class for DQN when using images as input.
-
-    :param observation_space: Observation space
-    :param action_space: Action space
-    :param lr_schedule: Learning rate schedule (could be constant)
-    :param net_arch: The specification of the policy and value networks.
-    :param activation_fn: Activation function
-    :param features_extractor_class: Features extractor to use.
-    :param normalize_images: Whether to normalize images or not,
-         dividing by 255.0 (True by default)
-    :param optimizer_class: The optimizer to use,
-        ``th.optim.Adam`` by default
-    :param optimizer_kwargs: Additional keyword arguments,
-        excluding the learning rate, to pass to the optimizer
-    """
-
-    def __init__(
-        self,
-        observation_space: gym.spaces.Space,
-        action_space: gym.spaces.Space,
-        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
-        activation_fn: Type[nn.Module] = nn.ReLU,
-        features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
-        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
-        normalize_images: bool = True,
-        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
-        optimizer_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        super(CnnPolicy, self).__init__(
-            observation_space,
-            action_space,
-            lr_schedule,
-            net_arch,
-            activation_fn,
-            features_extractor_class,
-            features_extractor_kwargs,
-            normalize_images,
-            optimizer_class,
-            optimizer_kwargs,
-        )
-
-
-register_policy("MlpPolicy", MlpPolicy)
-register_policy("CnnPolicy", CnnPolicy)
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/models/experimental.py b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/models/experimental.py
deleted file mode 100644
index 11f75e2254b3054fc8c3b5be6e3fa0a994912a97..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/models/experimental.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Experimental modules
-"""
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from utils.downloads import attempt_download
-
-
-class Sum(nn.Module):
-    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
-    def __init__(self, n, weight=False):  # n: number of inputs
-        super().__init__()
-        self.weight = weight  # apply weights boolean
-        self.iter = range(n - 1)  # iter object
-        if weight:
-            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights
-
-    def forward(self, x):
-        y = x[0]  # no weight
-        if self.weight:
-            w = torch.sigmoid(self.w) * 2
-            for i in self.iter:
-                y = y + x[i + 1] * w[i]
-        else:
-            for i in self.iter:
-                y = y + x[i + 1]
-        return y
-
-
-class MixConv2d(nn.Module):
-    # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch_out, kernel, stride, ch_strategy
-        super().__init__()
-        n = len(k)  # number of convolutions
-        if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, n - 1E-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(n)]  # intermediate channels
-        else:  # equal weight.numel() per group
-            b = [c2] + [0] * n
-            a = np.eye(n + 1, n, k=-1)
-            a -= np.roll(a, 1, axis=1)
-            a *= np.array(k) ** 2
-            a[0] = 1
-            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
-
-        self.m = nn.ModuleList([
-            nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.SiLU()
-
-    def forward(self, x):
-        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
-
-
-class Ensemble(nn.ModuleList):
-    # Ensemble of models
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x, augment=False, profile=False, visualize=False):
-        y = [module(x, augment, profile, visualize)[0] for module in self]
-        # y = torch.stack(y).max(0)[0]  # max ensemble
-        # y = torch.stack(y).mean(0)  # mean ensemble
-        y = torch.cat(y, 1)  # nms ensemble
-        return y, None  # inference, train output
-
-
-def attempt_load(weights, device=None, inplace=True, fuse=True):
-    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
-    from models.yolo import Detect, Model
-
-    model = Ensemble()
-    for w in weights if isinstance(weights, list) else [weights]:
-        ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
-        ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
-
-        # Model compatibility updates
-        if not hasattr(ckpt, 'stride'):
-            ckpt.stride = torch.tensor([32.])
-        if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
-            ckpt.names = dict(enumerate(ckpt.names))  # convert to dict
-
-        model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode
-
-    # Module updates
-    for m in model.modules():
-        t = type(m)
-        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
-            m.inplace = inplace
-            if t is Detect and not isinstance(m.anchor_grid, list):
-                delattr(m, 'anchor_grid')
-                setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
-        elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
-            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
-
-    # Return model
-    if len(model) == 1:
-        return model[-1]
-
-    # Return detection ensemble
-    print(f'Ensemble created with {weights}\n')
-    for k in 'names', 'nc', 'yaml':
-        setattr(model, k, getattr(model[0], k))
-    model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
-    assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
-    return model
diff --git a/spaces/hdhzk/bingo/README.md b/spaces/hdhzk/bingo/README.md
deleted file mode 100644
index 218767d1d7debd26932ffddca2ec0f421c0171a9..0000000000000000000000000000000000000000
--- a/spaces/hdhzk/bingo/README.md
+++ /dev/null
@@ -1,195 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: true
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-</div>
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[![img](./docs/images/demo.png)](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[![Deploy to HuggingFace](https://img.shields.io/badge/%E7%82%B9%E5%87%BB%E9%83%A8%E7%BD%B2-%F0%9F%A4%97-fff)](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-<details>
-<summary>
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-</summary>
-
-#### 部署到 Netlify
-[![Deploy to Netlify Button](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-</details>
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge，通过人机校验，然后
-
-![BING HEADER](./docs/images/curl.png)
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-<details>
-<summary>正常格式/网页端保存的格式(格式仅供参考)</summary>
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
-  -H 'authority: www.bing.com' \
-  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
-  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
-  -H 'cache-control: max-age=0' \
-  -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
-  -H 'dnt: 1' \
-  -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
-  -H 'sec-ch-ua-arch: "x86"' \
-  -H 'sec-ch-ua-bitness: "64"' \
-  -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
-  -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
-  -H 'sec-ch-ua-mobile: ?0' \
-  -H 'sec-ch-ua-model: ""' \
-  -H 'sec-ch-ua-platform: "Windows"' \
-  -H 'sec-ch-ua-platform-version: "15.0.0"' \
-  -H 'sec-fetch-dest: document' \
-  -H 'sec-fetch-mode: navigate' \
-  -H 'sec-fetch-site: none' \
-  -H 'sec-fetch-user: ?1' \
-  -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
-  -H 'sec-ms-gec-version: 1-116.0.1938.29' \
-  -H 'upgrade-insecure-requests: 1' \
-  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \
-  -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
-  -H 'x-edge-shopping-flag: 1' \
-  --compressed
-```
-</details>
-
-<details>
-<summary>转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)</summary>
-
-```
-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
-```
-</details>
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。 
-
-
-## 答疑及交流
-
-<image src="./docs/images/wechat.png" width=240 />
-
-## License
-
-MIT © [LICENSE](https://github.com/weaigc/bingo/blob/main/LICENSE).
-
-
diff --git a/spaces/hekbobo/bingo/src/lib/isomorphic/browser.ts b/spaces/hekbobo/bingo/src/lib/isomorphic/browser.ts
deleted file mode 100644
index de125b1f1786d1618cb1ff47f403d76c6784f4ce..0000000000000000000000000000000000000000
--- a/spaces/hekbobo/bingo/src/lib/isomorphic/browser.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-'use client'
-
-const debug = console.info.bind(console)
-
-class WebSocketAlias extends WebSocket {
-  constructor(address: string | URL, ...args: any) {
-    super(address)
-  }
-}
-
-export default { fetch, WebSocket: WebSocketAlias, debug }
diff --git a/spaces/hekbobo/bingo/src/pages/api/sydney.ts b/spaces/hekbobo/bingo/src/pages/api/sydney.ts
deleted file mode 100644
index a5b99574289f532e6ef7c5e70a6360a556db9643..0000000000000000000000000000000000000000
--- a/spaces/hekbobo/bingo/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,61 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const conversationContext = req.body
-  const headers = createHeaders(req.cookies)
-  debug(headers)
-  res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
-  const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
-    headers: {
-      ...headers,
-      'accept-language': 'zh-CN,zh;q=0.9',
-      'cache-control': 'no-cache',
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      pragma: 'no-cache',
-    }
-  })
-
-  const closeDog = new WatchDog()
-  const timeoutDog = new WatchDog()
-  ws.onmessage = (event) => {
-    timeoutDog.watch(() => {
-      ws.send(websocketUtils.packMessage({ type: 6 }))
-    }, 1500)
-    closeDog.watch(() => {
-      ws.close()
-    }, 10000)
-    res.write(event.data)
-    if (/\{"type":([367])\}/.test(String(event.data))) {
-      const type = parseInt(RegExp.$1, 10)
-      debug('connection type', type)
-      if (type === 3) {
-        ws.close()
-      } else {
-        ws.send(websocketUtils.packMessage({ type }))
-      }
-    }
-  }
-
-  ws.onclose = () => {
-    timeoutDog.reset()
-    closeDog.reset()
-    debug('connection close')
-    res.end()
-  }
-
-  await new Promise((resolve) => ws.onopen = resolve)
-  ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
-  ws.send(websocketUtils.packMessage({ type: 6 }))
-  ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
-  req.socket.once('close', () => {
-    ws.close()
-    if (!res.closed) {
-      res.end()
-    }
-  })
-}
diff --git a/spaces/hfl/VQA_VLE_LLM/models/VLE/configuration_vle.py b/spaces/hfl/VQA_VLE_LLM/models/VLE/configuration_vle.py
deleted file mode 100644
index 8ea906f633defa3280f77aa47dcdb908b2620806..0000000000000000000000000000000000000000
--- a/spaces/hfl/VQA_VLE_LLM/models/VLE/configuration_vle.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# coding=utf-8
-# Copyright The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" VLE model configuration"""
-
-import copy
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-from transformers.models.auto.configuration_auto import AutoConfig
-from transformers.models.clip.configuration_clip import CLIPVisionConfig
-from typing import Union, Dict
-
-logger = logging.get_logger(__name__)
-
-
-class VLEConfig(PretrainedConfig):
-    r"""
-    [`VLEConfig`] is the configuration class to store the configuration of a
-    [`VLEModel`]. It is used to instantiate [`VLEModel`] model according to the
-    specified arguments, defining the text model and vision model configs.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        text_config (`dict`):
-            Dictionary of configuration options that defines text model config.
-        vision_config (`dict`):
-            Dictionary of configuration options that defines vison model config.
-        #TODO
-        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
-            The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation.
-        kwargs (*optional*):
-            Dictionary of keyword arguments.
-
-    Examples:
-
-    ```python
-    >>> from transformers import ViTConfig, BertConfig
-    >>> from configuration_vle import VLEconfig
-    >>> from modeling_vle import VLEModel
-    >>> # Initializing a BERT and ViT configuration
-    >>> config_vision = ViTConfig()
-    >>> config_text = BertConfig()
-
-    >>> config = VLEConfig.from_vision_text_configs(config_vision, config_text) #TODO
-
-    >>> # Initializing a BERT and ViT model (with random weights)
-    >>> model = VLEModel(config=config)
-
-    >>> # Accessing the model configuration
-    >>> config_vision = model.config.vision_config
-    >>> config_text = model.config.text_config
-
-    >>> # Saving the model, including its configuration
-    >>> model.save_pretrained("vit-bert")
-
-    >>> # loading model and config from pretrained folder
-    >>> vision_text_config = VLEConfig.from_pretrained("vit-bert")
-    >>> model = VLEModel.from_pretrained("vit-bert", config=vision_text_config)
-    ```"""
-
-    model_type = "vle"
-    is_composition = True
-
-    def __init__(
-        self, 
-        text_config: Union[PretrainedConfig, Dict],
-        vision_config: Union[PretrainedConfig, Dict],
-        num_token_types=2,
-        hidden_size=768,
-        num_hidden_layers=6,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        classifier_dropout=None,
-        **kwargs):
-        super().__init__(**kwargs)
-
-        if not isinstance(text_config,PretrainedConfig):
-            text_model_type = text_config.pop('model_type')
-            text_config = AutoConfig.for_model(text_model_type, **text_config)
-        self.text_config = text_config
-
-        if not isinstance(vision_config, PretrainedConfig):
-            vision_model_type = vision_config.pop('model_type')
-            if vision_model_type == "clip":
-                vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config
-            elif vision_model_type == "clip_vision_model":
-                vision_config = CLIPVisionConfig(**vision_config)
-            else:
-                vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
-            self.vision_config = vision_config
-        else:
-            vision_model_type = vision_config.model_type
-            if vision_model_type== "clip":
-                vision_config = vision_config.vision_config
-            self.vision_config = vision_config
-
-
-
-        # co-attention
-        self.num_token_types=num_token_types
-        self.hidden_size=hidden_size
-        self.num_hidden_layers=num_hidden_layers
-        self.num_attention_heads=num_attention_heads
-        self.intermediate_size=intermediate_size
-        self.hidden_act=hidden_act
-        self.hidden_dropout_prob=hidden_dropout_prob
-        self.attention_probs_dropout_prob=attention_probs_dropout_prob
-        self.initializer_range=initializer_range
-        self.layer_norm_eps=layer_norm_eps
-        self.classifier_dropout=classifier_dropout
-
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = copy.deepcopy(self.__dict__)
-        output["vision_config"] = self.vision_config.to_dict()
-        output["text_config"] = self.text_config.to_dict()
-        output["model_type"] = self.__class__.model_type
-        return output
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/experiment_planner_baseline_2DUNet.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/experiment_planner_baseline_2DUNet.py
deleted file mode 100644
index 31283041ba39ef3b1f2030576e4fe36229307378..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/experiment_planner_baseline_2DUNet.py
+++ /dev/null
@@ -1,159 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-
-import shutil
-
-import nnunet
-import numpy as np
-from batchgenerators.utilities.file_and_folder_operations import load_pickle, subfiles
-from multiprocessing.pool import Pool
-from nnunet.configuration import default_num_threads
-from nnunet.experiment_planning.common_utils import get_pool_and_conv_props
-from nnunet.experiment_planning.experiment_planner_baseline_3DUNet import ExperimentPlanner
-from nnunet.experiment_planning.utils import add_classes_in_slice_info
-from nnunet.network_architecture.generic_UNet import Generic_UNet
-from nnunet.paths import *
-from nnunet.preprocessing.preprocessing import PreprocessorFor2D
-from nnunet.training.model_restore import recursive_find_python_class
-
-
-class ExperimentPlanner2D(ExperimentPlanner):
-    def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
-        super(ExperimentPlanner2D, self).__init__(folder_with_cropped_data,
-                                                  preprocessed_output_folder)
-        self.data_identifier = default_data_identifier + "_2D"
-        self.plans_fname = join(self.preprocessed_output_folder, "nnUNetPlans" + "_plans_2D.pkl")
-
-        self.unet_base_num_features = 30
-        self.unet_max_num_filters = 512
-        self.unet_max_numpool = 999
-
-        self.preprocessor_name = "PreprocessorFor2D"
-
-    def get_properties_for_stage(self, current_spacing, original_spacing, original_shape, num_cases,
-                                 num_modalities, num_classes):
-
-        new_median_shape = np.round(original_spacing / current_spacing * original_shape).astype(int)
-
-        dataset_num_voxels = np.prod(new_median_shape, dtype=np.int64) * num_cases
-        input_patch_size = new_median_shape[1:]
-
-        network_numpool, net_pool_kernel_sizes, net_conv_kernel_sizes, input_patch_size, \
-        shape_must_be_divisible_by = get_pool_and_conv_props(current_spacing[1:], input_patch_size,
-                                                             self.unet_featuremap_min_edge_length,
-                                                             self.unet_max_numpool)
-
-        estimated_gpu_ram_consumption = Generic_UNet.compute_approx_vram_consumption(input_patch_size,
-                                                                                     network_numpool,
-                                                                                     self.unet_base_num_features,
-                                                                                     self.unet_max_num_filters,
-                                                                                     num_modalities, num_classes,
-                                                                                     net_pool_kernel_sizes,
-                                                                                     conv_per_stage=self.conv_per_stage)
-
-        batch_size = int(np.floor(Generic_UNet.use_this_for_batch_size_computation_2D /
-                                  estimated_gpu_ram_consumption * Generic_UNet.DEFAULT_BATCH_SIZE_2D))
-        if batch_size < self.unet_min_batch_size:
-            raise RuntimeError("This framework is not made to process patches this large. We will add patch-based "
-                               "2D networks later. Sorry for the inconvenience")
-
-        # check if batch size is too large (more than 5 % of dataset)
-        max_batch_size = np.round(self.batch_size_covers_max_percent_of_dataset * dataset_num_voxels /
-                                  np.prod(input_patch_size, dtype=np.int64)).astype(int)
-        batch_size = max(1, min(batch_size, max_batch_size))
-
-        plan = {
-            'batch_size': batch_size,
-            'num_pool_per_axis': network_numpool,
-            'patch_size': input_patch_size,
-            'median_patient_size_in_voxels': new_median_shape,
-            'current_spacing': current_spacing,
-            'original_spacing': original_spacing,
-            'pool_op_kernel_sizes': net_pool_kernel_sizes,
-            'conv_kernel_sizes': net_conv_kernel_sizes,
-            'do_dummy_2D_data_aug': False
-        }
-        return plan
-
-    def plan_experiment(self):
-        use_nonzero_mask_for_normalization = self.determine_whether_to_use_mask_for_norm()
-        print("Are we using the nonzero maks for normalizaion?", use_nonzero_mask_for_normalization)
-
-        spacings = self.dataset_properties['all_spacings']
-        sizes = self.dataset_properties['all_sizes']
-        all_classes = self.dataset_properties['all_classes']
-        modalities = self.dataset_properties['modalities']
-        num_modalities = len(list(modalities.keys()))
-
-        target_spacing = self.get_target_spacing()
-        new_shapes = np.array([np.array(i) / target_spacing * np.array(j) for i, j in zip(spacings, sizes)])
-
-        max_spacing_axis = np.argmax(target_spacing)
-        remaining_axes = [i for i in list(range(3)) if i != max_spacing_axis]
-        self.transpose_forward = [max_spacing_axis] + remaining_axes
-        self.transpose_backward = [np.argwhere(np.array(self.transpose_forward) == i)[0][0] for i in range(3)]
-
-        # we base our calculations on the median shape of the datasets
-        median_shape = np.median(np.vstack(new_shapes), 0)
-        print("the median shape of the dataset is ", median_shape)
-
-        max_shape = np.max(np.vstack(new_shapes), 0)
-        print("the max shape in the dataset is ", max_shape)
-        min_shape = np.min(np.vstack(new_shapes), 0)
-        print("the min shape in the dataset is ", min_shape)
-
-        print("we don't want feature maps smaller than ", self.unet_featuremap_min_edge_length, " in the bottleneck")
-
-        # how many stages will the image pyramid have?
-        self.plans_per_stage = []
-
-        target_spacing_transposed = np.array(target_spacing)[self.transpose_forward]
-        median_shape_transposed = np.array(median_shape)[self.transpose_forward]
-        print("the transposed median shape of the dataset is ", median_shape_transposed)
-
-        self.plans_per_stage.append(
-            self.get_properties_for_stage(target_spacing_transposed, target_spacing_transposed, median_shape_transposed,
-                                          num_cases=len(self.list_of_cropped_npz_files),
-                                          num_modalities=num_modalities,
-                                          num_classes=len(all_classes) + 1),
-            )
-
-        print(self.plans_per_stage)
-
-        self.plans_per_stage = self.plans_per_stage[::-1]
-        self.plans_per_stage = {i: self.plans_per_stage[i] for i in range(len(self.plans_per_stage))}  # convert to dict
-
-        normalization_schemes = self.determine_normalization_scheme()
-        # deprecated
-        only_keep_largest_connected_component, min_size_per_class, min_region_size_per_class = None, None, None
-
-        # these are independent of the stage
-        plans = {'num_stages': len(list(self.plans_per_stage.keys())), 'num_modalities': num_modalities,
-                 'modalities': modalities, 'normalization_schemes': normalization_schemes,
-                 'dataset_properties': self.dataset_properties, 'list_of_npz_files': self.list_of_cropped_npz_files,
-                 'original_spacings': spacings, 'original_sizes': sizes,
-                 'preprocessed_data_folder': self.preprocessed_output_folder,
-                 'num_classes': [len(label) for label in all_classes],
-                 'all_classes': all_classes, 'base_num_features': self.unet_base_num_features,
-                 'use_mask_for_norm': use_nonzero_mask_for_normalization,
-                 'keep_only_largest_region': only_keep_largest_connected_component,
-                 'min_region_size_per_class': min_region_size_per_class, 'min_size_per_class': min_size_per_class,
-                 'transpose_forward': self.transpose_forward, 'transpose_backward': self.transpose_backward,
-                 'data_identifier': self.data_identifier, 'plans_per_stage': self.plans_per_stage,
-                 'preprocessor_name': self.preprocessor_name,
-                 }
-
-        self.plans = plans
-        self.save_my_plans()
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/run/run_training.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/run/run_training.py
deleted file mode 100644
index 3629c8e2b8bb89aafd85879cf7fa4c7f4ced2757..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/run/run_training.py
+++ /dev/null
@@ -1,203 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-
-import argparse
-from batchgenerators.utilities.file_and_folder_operations import *
-from nnunet.run.default_configuration import get_default_configuration
-from nnunet.paths import default_plans_identifier
-from nnunet.run.load_pretrained_weights import load_pretrained_weights
-from nnunet.training.cascade_stuff.predict_next_stage import predict_next_stage
-from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer
-from nnunet.training.network_training.nnUNetTrainerCascadeFullRes import nnUNetTrainerCascadeFullRes
-from nnunet.training.network_training.nnUNetTrainerV2_CascadeFullRes import nnUNetTrainerV2CascadeFullRes
-from nnunet.utilities.task_name_id_conversion import convert_id_to_task_name
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("network")
-    parser.add_argument("network_trainer")
-    parser.add_argument("task", help="can be task name or task id")
-    parser.add_argument("fold", help='0, 1, ..., 5 or \'all\'')
-    parser.add_argument("-val", "--validation_only", help="use this if you want to only run the validation",
-                        action="store_true")
-    parser.add_argument("-c", "--continue_training", help="use this if you want to continue a training",
-                        action="store_true")
-    parser.add_argument("-p", help="plans identifier. Only change this if you created a custom experiment planner",
-                        default=default_plans_identifier, required=False)
-    parser.add_argument("--use_compressed_data", default=False, action="store_true",
-                        help="If you set use_compressed_data, the training cases will not be decompressed. Reading compressed data "
-                             "is much more CPU and RAM intensive and should only be used if you know what you are "
-                             "doing", required=False)
-    parser.add_argument("--deterministic",
-                        help="Makes training deterministic, but reduces training speed substantially. I (Fabian) think "
-                             "this is not necessary. Deterministic training will make you overfit to some random seed. "
-                             "Don't use that.",
-                        required=False, default=False, action="store_true")
-    parser.add_argument("--npz", required=False, default=False, action="store_true", help="if set then nnUNet will "
-                                                                                          "export npz files of "
-                                                                                          "predicted segmentations "
-                                                                                          "in the validation as well. "
-                                                                                          "This is needed to run the "
-                                                                                          "ensembling step so unless "
-                                                                                          "you are developing nnUNet "
-                                                                                          "you should enable this")
-    parser.add_argument("--find_lr", required=False, default=False, action="store_true",
-                        help="not used here, just for fun")
-    parser.add_argument("--valbest", required=False, default=False, action="store_true",
-                        help="hands off. This is not intended to be used")
-    parser.add_argument("--fp32", required=False, default=False, action="store_true",
-                        help="disable mixed precision training and run old school fp32")
-    parser.add_argument("--val_folder", required=False, default="validation_raw",
-                        help="name of the validation folder. No need to use this for most people")
-    parser.add_argument("--disable_saving", required=False, action='store_true',
-                        help="If set nnU-Net will not save any parameter files (except a temporary checkpoint that "
-                             "will be removed at the end of the training). Useful for development when you are "
-                             "only interested in the results and want to save some disk space")
-    parser.add_argument("--disable_postprocessing_on_folds", required=False, action='store_true',
-                        help="Running postprocessing on each fold only makes sense when developing with nnU-Net and "
-                             "closely observing the model performance on specific configurations. You do not need it "
-                             "when applying nnU-Net because the postprocessing for this will be determined only once "
-                             "all five folds have been trained and nnUNet_find_best_configuration is called. Usually "
-                             "running postprocessing on each fold is computationally cheap, but some users have "
-                             "reported issues with very large images. If your images are large (>600x600x600 voxels) "
-                             "you should consider setting this flag.")
-    # parser.add_argument("--interp_order", required=False, default=3, type=int,
-    #                     help="order of interpolation for segmentations. Testing purpose only. Hands off")
-    # parser.add_argument("--interp_order_z", required=False, default=0, type=int,
-    #                     help="order of interpolation along z if z is resampled separately. Testing purpose only. "
-    #                          "Hands off")
-    # parser.add_argument("--force_separate_z", required=False, default="None", type=str,
-    #                     help="force_separate_z resampling. Can be None, True or False. Testing purpose only. Hands off")
-    parser.add_argument('--val_disable_overwrite', action='store_false', default=True,
-                        help='Validation does not overwrite existing segmentations')
-    parser.add_argument('--disable_next_stage_pred', action='store_true', default=False,
-                        help='do not predict next stage')
-    parser.add_argument('-pretrained_weights', type=str, required=False, default=None,
-                        help='path to nnU-Net checkpoint file to be used as pretrained model (use .model '
-                             'file, for example model_final_checkpoint.model). Will only be used when actually training. '
-                             'Optional. Beta. Use with caution.')
-    parser.add_argument('--disable_deepsupervision', action='store_true', default=False)
-
-    args = parser.parse_args()
-
-    task = args.task
-    fold = args.fold
-    network = args.network
-    network_trainer = args.network_trainer
-    validation_only = args.validation_only
-    plans_identifier = args.p
-    find_lr = args.find_lr
-    disable_postprocessing_on_folds = args.disable_postprocessing_on_folds
-    disable_deepsupervision = args.disable_deepsupervision
-
-    use_compressed_data = args.use_compressed_data
-    decompress_data = not use_compressed_data
-
-    deterministic = args.deterministic
-    valbest = args.valbest
-
-    fp32 = args.fp32
-    run_mixed_precision = not fp32
-
-    val_folder = args.val_folder
-    # interp_order = args.interp_order
-    # interp_order_z = args.interp_order_z
-    # force_separate_z = args.force_separate_z
-
-    if not task.startswith("Task"):
-        task_id = int(task)
-        task = convert_id_to_task_name(task_id)
-
-    if fold == 'all':
-        pass
-    else:
-        fold = int(fold)
-
-    # if force_separate_z == "None":
-    #     force_separate_z = None
-    # elif force_separate_z == "False":
-    #     force_separate_z = False
-    # elif force_separate_z == "True":
-    #     force_separate_z = True
-    # else:
-    #     raise ValueError("force_separate_z must be None, True or False. Given: %s" % force_separate_z)
-
-    plans_file, output_folder_name, dataset_directory, batch_dice, stage, \
-    trainer_class = get_default_configuration(network, task, network_trainer, plans_identifier)
-
-    if trainer_class is None:
-        raise RuntimeError("Could not find trainer class in nnunet.training.network_training")
-
-    if network == "3d_cascade_fullres":
-        assert issubclass(trainer_class, (nnUNetTrainerCascadeFullRes, nnUNetTrainerV2CascadeFullRes)), \
-            "If running 3d_cascade_fullres then your " \
-            "trainer class must be derived from " \
-            "nnUNetTrainerCascadeFullRes"
-    else:
-        assert issubclass(trainer_class,
-                          nnUNetTrainer), "network_trainer was found but is not derived from nnUNetTrainer"
-
-    trainer = trainer_class(plans_file, fold, output_folder=output_folder_name, dataset_directory=dataset_directory,
-                            batch_dice=batch_dice, stage=stage, unpack_data=decompress_data,
-                            deterministic=deterministic,
-                            fp16=run_mixed_precision)
-    if args.disable_saving:
-        trainer.save_final_checkpoint = False  # whether or not to save the final checkpoint
-        trainer.save_best_checkpoint = False  # whether or not to save the best checkpoint according to
-        # self.best_val_eval_criterion_MA
-        trainer.save_intermediate_checkpoints = True  # whether or not to save checkpoint_latest. We need that in case
-        # the training chashes
-        trainer.save_latest_only = True  # if false it will not store/overwrite _latest but separate files each
-
-    trainer.initialize(not validation_only, disable_deepsupervision=disable_deepsupervision)
-
-    if find_lr:
-        trainer.find_lr()
-    else:
-        if not validation_only:
-            if args.continue_training:
-                # -c was set, continue a previous training and ignore pretrained weights
-                trainer.load_latest_checkpoint()
-            elif (not args.continue_training) and (args.pretrained_weights is not None):
-                # we start a new training. If pretrained_weights are set, use them
-                load_pretrained_weights(trainer.network, args.pretrained_weights)
-            else:
-                # new training without pretraine weights, do nothing
-                pass
-
-            # TRAINING
-            trainer.run_training()
-
-        else:
-            if valbest:
-                trainer.load_best_checkpoint(train=False)
-            else:
-                trainer.load_final_checkpoint(train=False)
-
-        trainer.network.eval()
-
-        # predict validation
-        trainer.validate(save_softmax=args.npz, validation_folder_name=val_folder,
-                         run_postprocessing_on_folds=not disable_postprocessing_on_folds,
-                         overwrite=args.val_disable_overwrite)
-
-        if network == '3d_lowres' and not args.disable_next_stage_pred:
-            print("predicting segmentations for the next stage of the cascade")
-            predict_next_stage(trainer, join(dataset_directory, trainer.plans['data_identifier'] + "_stage%d" % 1))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/dataloading/dataset_loading.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/dataloading/dataset_loading.py
deleted file mode 100644
index 1edbee5b761309cd1bd6c6d635fec643c44c201f..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/dataloading/dataset_loading.py
+++ /dev/null
@@ -1,615 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    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 collections import OrderedDict
-import numpy as np
-from multiprocessing import Pool
-
-from batchgenerators.dataloading.data_loader import SlimDataLoaderBase
-
-from nnunet.configuration import default_num_threads
-from nnunet.paths import preprocessing_output_dir
-from batchgenerators.utilities.file_and_folder_operations import *
-
-
-def get_case_identifiers(folder):
-    case_identifiers = [i[:-4] for i in os.listdir(folder) if i.endswith("npz") and (i.find("segFromPrevStage") == -1)]
-    return case_identifiers
-
-
-def get_case_identifiers_from_raw_folder(folder):
-    case_identifiers = np.unique(
-        [i[:-12] for i in os.listdir(folder) if i.endswith(".nii.gz") and (i.find("segFromPrevStage") == -1)])
-    return case_identifiers
-
-
-def convert_to_npy(args):
-    if not isinstance(args, tuple):
-        key = "data"
-        npz_file = args
-    else:
-        npz_file, key = args
-    if not isfile(npz_file[:-3] + "npy"):
-        a = np.load(npz_file)[key]
-        np.save(npz_file[:-3] + "npy", a)
-
-
-def save_as_npz(args):
-    if not isinstance(args, tuple):
-        key = "data"
-        npy_file = args
-    else:
-        npy_file, key = args
-    d = np.load(npy_file)
-    np.savez_compressed(npy_file[:-3] + "npz", **{key: d})
-
-
-def unpack_dataset(folder, threads=default_num_threads, key="data"):
-    """
-    unpacks all npz files in a folder to npy (whatever you want to have unpacked must be saved unter key)
-    :param folder:
-    :param threads:
-    :param key:
-    :return:
-    """
-    p = Pool(threads)
-    npz_files = subfiles(folder, True, None, ".npz", True)
-    p.map(convert_to_npy, zip(npz_files, [key] * len(npz_files)))
-    p.close()
-    p.join()
-
-
-
-def pack_dataset(folder, threads=default_num_threads, key="data"):
-    p = Pool(threads)
-    npy_files = subfiles(folder, True, None, ".npy", True)
-    p.map(save_as_npz, zip(npy_files, [key] * len(npy_files)))
-    p.close()
-    p.join()
-
-
-def delete_npy(folder):
-    case_identifiers = get_case_identifiers(folder)
-    npy_files = [join(folder, i + ".npy") for i in case_identifiers]
-    npy_files = [i for i in npy_files if isfile(i)]
-    for n in npy_files:
-        os.remove(n)
-
-
-def load_dataset(folder, num_cases_properties_loading_threshold=1000):
-    # we don't load the actual data but instead return the filename to the np file.
-    print('loading dataset')
-    case_identifiers = get_case_identifiers(folder)
-    case_identifiers.sort()
-    dataset = OrderedDict()
-    for c in case_identifiers:
-        dataset[c] = OrderedDict()
-        dataset[c]['data_file'] = join(folder, "%s.npz" % c)
-
-        # dataset[c]['properties'] = load_pickle(join(folder, "%s.pkl" % c))
-        dataset[c]['properties_file'] = join(folder, "%s.pkl" % c)
-
-        if dataset[c].get('seg_from_prev_stage_file') is not None:
-            dataset[c]['seg_from_prev_stage_file'] = join(folder, "%s_segs.npz" % c)
-
-    if len(case_identifiers) <= num_cases_properties_loading_threshold:
-        print('loading all case properties')
-        for i in dataset.keys():
-            dataset[i]['properties'] = load_pickle(dataset[i]['properties_file'])
-
-    return dataset
-
-
-def crop_2D_image_force_fg(img, crop_size, valid_voxels):
-    """
-    img must be [c, x, y]
-    img[-1] must be the segmentation with segmentation>0 being foreground
-    :param img:
-    :param crop_size:
-    :param valid_voxels: voxels belonging to the selected class
-    :return:
-    """
-    assert len(valid_voxels.shape) == 2
-
-    if type(crop_size) not in (tuple, list):
-        crop_size = [crop_size] * (len(img.shape) - 1)
-    else:
-        assert len(crop_size) == (len(
-            img.shape) - 1), "If you provide a list/tuple as center crop make sure it has the same len as your data has dims (3d)"
-
-    # we need to find the center coords that we can crop to without exceeding the image border
-    lb_x = crop_size[0] // 2
-    ub_x = img.shape[1] - crop_size[0] // 2 - crop_size[0] % 2
-    lb_y = crop_size[1] // 2
-    ub_y = img.shape[2] - crop_size[1] // 2 - crop_size[1] % 2
-
-    if len(valid_voxels) == 0:
-        selected_center_voxel = (np.random.random_integers(lb_x, ub_x),
-                                 np.random.random_integers(lb_y, ub_y))
-    else:
-        selected_center_voxel = valid_voxels[np.random.choice(valid_voxels.shape[1]), :]
-
-    selected_center_voxel = np.array(selected_center_voxel)
-    for i in range(2):
-        selected_center_voxel[i] = max(crop_size[i] // 2, selected_center_voxel[i])
-        selected_center_voxel[i] = min(img.shape[i + 1] - crop_size[i] // 2 - crop_size[i] % 2,
-                                       selected_center_voxel[i])
-
-    result = img[:, (selected_center_voxel[0] - crop_size[0] // 2):(
-            selected_center_voxel[0] + crop_size[0] // 2 + crop_size[0] % 2),
-             (selected_center_voxel[1] - crop_size[1] // 2):(
-                     selected_center_voxel[1] + crop_size[1] // 2 + crop_size[1] % 2)]
-    return result
-
-
-class DataLoader3D(SlimDataLoaderBase):
-    def __init__(self, data, patch_size, final_patch_size, batch_size, has_prev_stage=False,
-                 oversample_foreground_percent=0.0, memmap_mode="r", pad_mode="edge", pad_kwargs_data=None,
-                 pad_sides=None):
-        """
-        This is the basic data loader for 3D networks. It uses preprocessed data as produced by my (Fabian) preprocessing.
-        You can load the data with load_dataset(folder) where folder is the folder where the npz files are located. If there
-        are only npz files present in that folder, the data loader will unpack them on the fly. This may take a while
-        and increase CPU usage. Therefore, I advise you to call unpack_dataset(folder) first, which will unpack all npz
-        to npy. Don't forget to call delete_npy(folder) after you are done with training?
-        Why all the hassle? Well the decathlon dataset is huge. Using npy for everything will consume >1 TB and that is uncool
-        given that I (Fabian) will have to store that permanently on /datasets and my local computer. With this strategy all
-        data is stored in a compressed format (factor 10 smaller) and only unpacked when needed.
-        :param data: get this with load_dataset(folder, stage=0). Plug the return value in here and you are g2g (good to go)
-        :param patch_size: what patch size will this data loader return? it is common practice to first load larger
-        patches so that a central crop after data augmentation can be done to reduce border artifacts. If unsure, use
-        get_patch_size() from data_augmentation.default_data_augmentation
-        :param final_patch_size: what will the patch finally be cropped to (after data augmentation)? this is the patch
-        size that goes into your network. We need this here because we will pad patients in here so that patches at the
-        border of patients are sampled properly
-        :param batch_size:
-        :param num_batches: how many batches will the data loader produce before stopping? None=endless
-        :param seed:
-        :param stage: ignore this (Fabian only)
-        :param random: Sample keys randomly; CAREFUL! non-random sampling requires batch_size=1, otherwise you will iterate batch_size times over the dataset
-        :param oversample_foreground: half the batch will be forced to contain at least some foreground (equal prob for each of the foreground classes)
-        """
-        super(DataLoader3D, self).__init__(data, batch_size, None)
-        if pad_kwargs_data is None:
-            pad_kwargs_data = OrderedDict()
-        self.pad_kwargs_data = pad_kwargs_data
-        self.pad_mode = pad_mode
-        self.oversample_foreground_percent = oversample_foreground_percent
-        self.final_patch_size = final_patch_size
-        self.has_prev_stage = has_prev_stage
-        self.patch_size = patch_size
-        self.list_of_keys = list(self._data.keys())
-        # need_to_pad denotes by how much we need to pad the data so that if we sample a patch of size final_patch_size
-        # (which is what the network will get) these patches will also cover the border of the patients
-        self.need_to_pad = (np.array(patch_size) - np.array(final_patch_size)).astype(int)
-        if pad_sides is not None:
-            if not isinstance(pad_sides, np.ndarray):
-                pad_sides = np.array(pad_sides)
-            self.need_to_pad += pad_sides
-        self.memmap_mode = memmap_mode
-        self.num_channels = None
-        self.pad_sides = pad_sides
-        self.data_shape, self.seg_shape = self.determine_shapes()
-
-    def get_do_oversample(self, batch_idx):
-        return not batch_idx < round(self.batch_size * (1 - self.oversample_foreground_percent))
-
-    def determine_shapes(self):
-        if self.has_prev_stage:
-            num_seg = 3
-        else:
-            num_seg = 1
-
-        k = list(self._data.keys())[0]
-        if isfile(self._data[k]['data_file'][:-4] + ".npy"):
-            case_all_data = np.load(self._data[k]['data_file'][:-4] + ".npy", self.memmap_mode)
-        else:
-            case_all_data = np.load(self._data[k]['data_file'])['data']
-        num_color_channels = case_all_data.shape[0] - 1
-        data_shape = (self.batch_size, num_color_channels, *self.patch_size)
-        seg_shape = (self.batch_size, num_seg, *self.patch_size)
-        return data_shape, seg_shape
-
-    def generate_train_batch(self):
-        selected_keys = np.random.choice(self.list_of_keys, self.batch_size, True, None)
-        data = np.zeros(self.data_shape, dtype=np.float32)
-        seg = np.zeros(self.seg_shape, dtype=np.float32)
-        case_properties = []
-        for j, i in enumerate(selected_keys):
-            # oversampling foreground will improve stability of model training, especially if many patches are empty
-            # (Lung for example)
-            if self.get_do_oversample(j):
-                force_fg = True
-            else:
-                force_fg = False
-
-            if 'properties' in self._data[i].keys():
-                properties = self._data[i]['properties']
-            else:
-                properties = load_pickle(self._data[i]['properties_file'])
-            case_properties.append(properties)
-
-            # cases are stored as npz, but we require unpack_dataset to be run. This will decompress them into npy
-            # which is much faster to access
-            if isfile(self._data[i]['data_file'][:-4] + ".npy"):
-                case_all_data = np.load(self._data[i]['data_file'][:-4] + ".npy", self.memmap_mode)
-            else:
-                case_all_data = np.load(self._data[i]['data_file'])['data']
-
-            # If we are doing the cascade then we will also need to load the segmentation of the previous stage and
-            # concatenate it. Here it will be concatenates to the segmentation because the augmentations need to be
-            # applied to it in segmentation mode. Later in the data augmentation we move it from the segmentations to
-            # the last channel of the data
-            if self.has_prev_stage:
-                if isfile(self._data[i]['seg_from_prev_stage_file'][:-4] + ".npy"):
-                    segs_from_previous_stage = np.load(self._data[i]['seg_from_prev_stage_file'][:-4] + ".npy",
-                                                       mmap_mode=self.memmap_mode)[None]
-                else:
-                    segs_from_previous_stage = np.load(self._data[i]['seg_from_prev_stage_file'])['data'][None]
-                # we theoretically support several possible previsous segmentations from which only one is sampled. But
-                # in practice this feature was never used so it's always only one segmentation
-                seg_key = np.random.choice(segs_from_previous_stage.shape[0])
-                seg_from_previous_stage = segs_from_previous_stage[seg_key:seg_key + 1]
-                assert all([i == j for i, j in zip(seg_from_previous_stage.shape[1:], case_all_data.shape[1:])]), \
-                    "seg_from_previous_stage does not match the shape of case_all_data: %s vs %s" % \
-                    (str(seg_from_previous_stage.shape[1:]), str(case_all_data.shape[1:]))
-            else:
-                seg_from_previous_stage = None
-
-            # do you trust me? You better do. Otherwise you'll have to go through this mess and honestly there are
-            # better things you could do right now
-
-            # (above) documentation of the day. Nice. Even myself coming back 1 months later I have not friggin idea
-            # what's going on. I keep the above documentation just for fun but attempt to make things clearer now
-
-            need_to_pad = self.need_to_pad.copy()
-            for d in range(3):
-                # if case_all_data.shape + need_to_pad is still < patch size we need to pad more! We pad on both sides
-                # always
-                if need_to_pad[d] + case_all_data.shape[d + 1] < self.patch_size[d]:
-                    need_to_pad[d] = self.patch_size[d] - case_all_data.shape[d + 1]
-
-            # we can now choose the bbox from -need_to_pad // 2 to shape - patch_size + need_to_pad // 2. Here we
-            # define what the upper and lower bound can be to then sample form them with np.random.randint
-            shape = case_all_data.shape[1:]
-            lb_x = - need_to_pad[0] // 2
-            ub_x = shape[0] + need_to_pad[0] // 2 + need_to_pad[0] % 2 - self.patch_size[0]
-            lb_y = - need_to_pad[1] // 2
-            ub_y = shape[1] + need_to_pad[1] // 2 + need_to_pad[1] % 2 - self.patch_size[1]
-            lb_z = - need_to_pad[2] // 2
-            ub_z = shape[2] + need_to_pad[2] // 2 + need_to_pad[2] % 2 - self.patch_size[2]
-
-            # if not force_fg then we can just sample the bbox randomly from lb and ub. Else we need to make sure we get
-            # at least one of the foreground classes in the patch
-            if not force_fg:
-                bbox_x_lb = np.random.randint(lb_x, ub_x + 1)
-                bbox_y_lb = np.random.randint(lb_y, ub_y + 1)
-                bbox_z_lb = np.random.randint(lb_z, ub_z + 1)
-            else:
-                # these values should have been precomputed
-                if 'class_locations' not in properties.keys():
-                    raise RuntimeError("Please rerun the preprocessing with the newest version of nnU-Net!")
-
-                # this saves us a np.unique. Preprocessing already did that for all cases. Neat.
-                foreground_classes = np.array(
-                    [i for i in properties['class_locations'].keys() if len(properties['class_locations'][i]) != 0])
-                foreground_classes = foreground_classes[foreground_classes > 0]
-
-                if len(foreground_classes) == 0:
-                    # this only happens if some image does not contain foreground voxels at all
-                    selected_class = None
-                    voxels_of_that_class = None
-                    print('case does not contain any foreground classes', i)
-                else:
-                    selected_class = np.random.choice(foreground_classes)
-
-                    voxels_of_that_class = properties['class_locations'][selected_class]
-
-                if voxels_of_that_class is not None:
-                    selected_voxel = voxels_of_that_class[np.random.choice(len(voxels_of_that_class))]
-                    # selected voxel is center voxel. Subtract half the patch size to get lower bbox voxel.
-                    # Make sure it is within the bounds of lb and ub
-                    bbox_x_lb = max(lb_x, selected_voxel[0] - self.patch_size[0] // 2)
-                    bbox_y_lb = max(lb_y, selected_voxel[1] - self.patch_size[1] // 2)
-                    bbox_z_lb = max(lb_z, selected_voxel[2] - self.patch_size[2] // 2)
-                else:
-                    # If the image does not contain any foreground classes, we fall back to random cropping
-                    bbox_x_lb = np.random.randint(lb_x, ub_x + 1)
-                    bbox_y_lb = np.random.randint(lb_y, ub_y + 1)
-                    bbox_z_lb = np.random.randint(lb_z, ub_z + 1)
-
-            bbox_x_ub = bbox_x_lb + self.patch_size[0]
-            bbox_y_ub = bbox_y_lb + self.patch_size[1]
-            bbox_z_ub = bbox_z_lb + self.patch_size[2]
-
-            # whoever wrote this knew what he was doing (hint: it was me). We first crop the data to the region of the
-            # bbox that actually lies within the data. This will result in a smaller array which is then faster to pad.
-            # valid_bbox is just the coord that lied within the data cube. It will be padded to match the patch size
-            # later
-            valid_bbox_x_lb = max(0, bbox_x_lb)
-            valid_bbox_x_ub = min(shape[0], bbox_x_ub)
-            valid_bbox_y_lb = max(0, bbox_y_lb)
-            valid_bbox_y_ub = min(shape[1], bbox_y_ub)
-            valid_bbox_z_lb = max(0, bbox_z_lb)
-            valid_bbox_z_ub = min(shape[2], bbox_z_ub)
-
-            # At this point you might ask yourself why we would treat seg differently from seg_from_previous_stage.
-            # Why not just concatenate them here and forget about the if statements? Well that's because segneeds to
-            # be padded with -1 constant whereas seg_from_previous_stage needs to be padded with 0s (we could also
-            # remove label -1 in the data augmentation but this way it is less error prone)
-            case_all_data = np.copy(case_all_data[:, valid_bbox_x_lb:valid_bbox_x_ub,
-                                    valid_bbox_y_lb:valid_bbox_y_ub,
-                                    valid_bbox_z_lb:valid_bbox_z_ub])
-            if seg_from_previous_stage is not None:
-                seg_from_previous_stage = seg_from_previous_stage[:, valid_bbox_x_lb:valid_bbox_x_ub,
-                                          valid_bbox_y_lb:valid_bbox_y_ub,
-                                          valid_bbox_z_lb:valid_bbox_z_ub]
-
-            data[j] = np.pad(case_all_data[:-1], ((0, 0),
-                                                  (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
-                                                  (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0)),
-                                                  (-min(0, bbox_z_lb), max(bbox_z_ub - shape[2], 0))),
-                             self.pad_mode, **self.pad_kwargs_data)
-
-            seg[j, 0] = np.pad(case_all_data[-1:], ((0, 0),
-                                                    (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
-                                                    (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0)),
-                                                    (-min(0, bbox_z_lb), max(bbox_z_ub - shape[2], 0))),
-                               'constant', **{'constant_values': -1})
-            if seg_from_previous_stage is not None:
-                seg[j, 1] = np.pad(seg_from_previous_stage, ((0, 0),
-                                                             (-min(0, bbox_x_lb),
-                                                              max(bbox_x_ub - shape[0], 0)),
-                                                             (-min(0, bbox_y_lb),
-                                                              max(bbox_y_ub - shape[1], 0)),
-                                                             (-min(0, bbox_z_lb),
-                                                              max(bbox_z_ub - shape[2], 0))),
-                                   'constant', **{'constant_values': 0})
-
-        return {'data': data, 'seg': seg, 'properties': case_properties, 'keys': selected_keys}
-
-
-class DataLoader2D(SlimDataLoaderBase):
-    def __init__(self, data, patch_size, final_patch_size, batch_size, oversample_foreground_percent=0.0,
-                 memmap_mode="r", pseudo_3d_slices=1, pad_mode="edge",
-                 pad_kwargs_data=None, pad_sides=None):
-        """
-        This is the basic data loader for 2D networks. It uses preprocessed data as produced by my (Fabian) preprocessing.
-        You can load the data with load_dataset(folder) where folder is the folder where the npz files are located. If there
-        are only npz files present in that folder, the data loader will unpack them on the fly. This may take a while
-        and increase CPU usage. Therefore, I advise you to call unpack_dataset(folder) first, which will unpack all npz
-        to npy. Don't forget to call delete_npy(folder) after you are done with training?
-        Why all the hassle? Well the decathlon dataset is huge. Using npy for everything will consume >1 TB and that is uncool
-        given that I (Fabian) will have to store that permanently on /datasets and my local computer. With htis strategy all
-        data is stored in a compressed format (factor 10 smaller) and only unpacked when needed.
-        :param data: get this with load_dataset(folder, stage=0). Plug the return value in here and you are g2g (good to go)
-        :param patch_size: what patch size will this data loader return? it is common practice to first load larger
-        patches so that a central crop after data augmentation can be done to reduce border artifacts. If unsure, use
-        get_patch_size() from data_augmentation.default_data_augmentation
-        :param final_patch_size: what will the patch finally be cropped to (after data augmentation)? this is the patch
-        size that goes into your network. We need this here because we will pad patients in here so that patches at the
-        border of patients are sampled properly
-        :param batch_size:
-        :param num_batches: how many batches will the data loader produce before stopping? None=endless
-        :param seed:
-        :param stage: ignore this (Fabian only)
-        :param transpose: ignore this
-        :param random: sample randomly; CAREFUL! non-random sampling requires batch_size=1, otherwise you will iterate batch_size times over the dataset
-        :param pseudo_3d_slices: 7 = 3 below and 3 above the center slice
-        """
-        super(DataLoader2D, self).__init__(data, batch_size, None)
-        if pad_kwargs_data is None:
-            pad_kwargs_data = OrderedDict()
-        self.pad_kwargs_data = pad_kwargs_data
-        self.pad_mode = pad_mode
-        self.pseudo_3d_slices = pseudo_3d_slices
-        self.oversample_foreground_percent = oversample_foreground_percent
-        self.final_patch_size = final_patch_size
-        self.patch_size = patch_size
-        self.list_of_keys = list(self._data.keys())
-        self.need_to_pad = np.array(patch_size) - np.array(final_patch_size)
-        self.memmap_mode = memmap_mode
-        if pad_sides is not None:
-            if not isinstance(pad_sides, np.ndarray):
-                pad_sides = np.array(pad_sides)
-            self.need_to_pad += pad_sides
-        self.pad_sides = pad_sides
-        self.data_shape, self.seg_shape = self.determine_shapes()
-
-    def determine_shapes(self):
-        # TODO make this dynamic (Done)
-        num_color_channels = 1
-
-        k = list(self._data.keys())[0]
-        if isfile(self._data[k]['data_file'][:-4] + ".npy"):
-            case_all_data = np.load(self._data[k]['data_file'][:-4] + ".npy", self.memmap_mode)
-        else:
-            case_all_data = np.load(self._data[k]['data_file'])['data']
-
-        num_seg = case_all_data.shape[0] - num_color_channels
-        data_shape = (self.batch_size, num_color_channels, *self.patch_size)
-        seg_shape = (self.batch_size, num_seg, *self.patch_size)
-        return data_shape, seg_shape
-
-    def get_do_oversample(self, batch_idx):
-        return not batch_idx < round(self.batch_size * (1 - self.oversample_foreground_percent))
-
-    def generate_train_batch(self):
-        selected_keys = np.random.choice(self.list_of_keys, self.batch_size, True, None)
-
-        data = np.zeros(self.data_shape, dtype=np.float32)
-        seg = np.zeros(self.seg_shape, dtype=np.float32)
-
-        case_properties = []
-        for j, i in enumerate(selected_keys):
-            if 'properties' in self._data[i].keys():
-                properties = self._data[i]['properties']
-            else:
-                properties = load_pickle(self._data[i]['properties_file'])
-            case_properties.append(properties)
-
-            if self.get_do_oversample(j):
-                force_fg = True
-            else:
-                force_fg = False
-
-            if not isfile(self._data[i]['data_file'][:-4] + ".npy"):
-                # lets hope you know what you're doing
-                case_all_data = np.load(self._data[i]['data_file'][:-4] + ".npz")['data']
-            else:
-                case_all_data = np.load(self._data[i]['data_file'][:-4] + ".npy", self.memmap_mode)
-
-            # this is for when there is just a 2d slice in case_all_data (2d support)
-            if len(case_all_data.shape) == 3:
-                case_all_data = case_all_data[:, None]
-
-            # first select a slice. This can be either random (no force fg) or guaranteed to contain some class
-            if not force_fg:
-                random_slice = np.random.choice(case_all_data.shape[1])
-                selected_class = None
-            else:
-                # these values should have been precomputed
-                if 'class_locations' not in properties.keys():
-                    raise RuntimeError("Please rerun the preprocessing with the newest version of nnU-Net!")
-
-                foreground_classeses = []
-                for label in properties['class_locations'].keys():
-                    foreground_classes = np.array(
-                        [i for i in properties['class_locations'][label].keys() if len(properties['class_locations'][label][i]) != 0])
-                    foreground_classes = foreground_classes[foreground_classes > 0]
-                    foreground_classeses.append(foreground_classes)
-
-                if len(foreground_classeses) == 0:
-                    selected_class = None
-                    random_slice = np.random.choice(case_all_data.shape[1])
-                    print('case does not contain any foreground classes', i)
-                else:
-                    selected_label = np.random.choice(len(properties['class_locations']))
-                    selected_class = np.random.choice(foreground_classeses[selected_label])
-
-                    voxels_of_that_class = properties['class_locations'][selected_label][selected_class]
-                    valid_slices = np.unique(voxels_of_that_class[:, 0])
-                    random_slice = np.random.choice(valid_slices)
-                    voxels_of_that_class = voxels_of_that_class[voxels_of_that_class[:, 0] == random_slice]
-                    voxels_of_that_class = voxels_of_that_class[:, 1:]
-
-            # now crop case_all_data to contain just the slice of interest. If we want additional slice above and
-            # below the current slice, here is where we get them. We stack those as additional color channels
-            if self.pseudo_3d_slices == 1:
-                case_all_data = case_all_data[:, random_slice]
-            else:
-                # this is very deprecated and will probably not work anymore. If you intend to use this you need to
-                # check this!
-                mn = random_slice - (self.pseudo_3d_slices - 1) // 2
-                mx = random_slice + (self.pseudo_3d_slices - 1) // 2 + 1
-                valid_mn = max(mn, 0)
-                valid_mx = min(mx, case_all_data.shape[1])
-                case_all_seg = case_all_data[-1:]
-                case_all_data = case_all_data[:-1]
-                case_all_data = case_all_data[:, valid_mn:valid_mx]
-                case_all_seg = case_all_seg[:, random_slice]
-                need_to_pad_below = valid_mn - mn
-                need_to_pad_above = mx - valid_mx
-                if need_to_pad_below > 0:
-                    shp_for_pad = np.array(case_all_data.shape)
-                    shp_for_pad[1] = need_to_pad_below
-                    case_all_data = np.concatenate((np.zeros(shp_for_pad), case_all_data), 1)
-                if need_to_pad_above > 0:
-                    shp_for_pad = np.array(case_all_data.shape)
-                    shp_for_pad[1] = need_to_pad_above
-                    case_all_data = np.concatenate((case_all_data, np.zeros(shp_for_pad)), 1)
-                case_all_data = case_all_data.reshape((-1, case_all_data.shape[-2], case_all_data.shape[-1]))
-                case_all_data = np.concatenate((case_all_data, case_all_seg), 0)
-
-            # case all data should now be (c, x, y)
-            assert len(case_all_data.shape) == 3
-
-            # we can now choose the bbox from -need_to_pad // 2 to shape - patch_size + need_to_pad // 2. Here we
-            # define what the upper and lower bound can be to then sample form them with np.random.randint
-
-            need_to_pad = self.need_to_pad.copy()
-            for d in range(2):
-                # if case_all_data.shape + need_to_pad is still < patch size we need to pad more! We pad on both sides
-                # always
-                if need_to_pad[d] + case_all_data.shape[d + 1] < self.patch_size[d]:
-                    need_to_pad[d] = self.patch_size[d] - case_all_data.shape[d + 1]
-
-            shape = case_all_data.shape[1:]
-            lb_x = - need_to_pad[0] // 2
-            ub_x = shape[0] + need_to_pad[0] // 2 + need_to_pad[0] % 2 - self.patch_size[0]
-            lb_y = - need_to_pad[1] // 2
-            ub_y = shape[1] + need_to_pad[1] // 2 + need_to_pad[1] % 2 - self.patch_size[1]
-
-            # if not force_fg then we can just sample the bbox randomly from lb and ub. Else we need to make sure we get
-            # at least one of the foreground classes in the patch
-            if not force_fg or selected_class is None:
-                bbox_x_lb = np.random.randint(lb_x, ub_x + 1)
-                bbox_y_lb = np.random.randint(lb_y, ub_y + 1)
-            else:
-                # this saves us a np.unique. Preprocessing already did that for all cases. Neat.
-                selected_voxel = voxels_of_that_class[np.random.choice(len(voxels_of_that_class))]
-                # selected voxel is center voxel. Subtract half the patch size to get lower bbox voxel.
-                # Make sure it is within the bounds of lb and ub
-                bbox_x_lb = max(lb_x, selected_voxel[0] - self.patch_size[0] // 2)
-                bbox_y_lb = max(lb_y, selected_voxel[1] - self.patch_size[1] // 2)
-
-            bbox_x_ub = bbox_x_lb + self.patch_size[0]
-            bbox_y_ub = bbox_y_lb + self.patch_size[1]
-
-            # whoever wrote this knew what he was doing (hint: it was me). We first crop the data to the region of the
-            # bbox that actually lies within the data. This will result in a smaller array which is then faster to pad.
-            # valid_bbox is just the coord that lied within the data cube. It will be padded to match the patch size
-            # later
-            valid_bbox_x_lb = max(0, bbox_x_lb)
-            valid_bbox_x_ub = min(shape[0], bbox_x_ub)
-            valid_bbox_y_lb = max(0, bbox_y_lb)
-            valid_bbox_y_ub = min(shape[1], bbox_y_ub)
-
-            # At this point you might ask yourself why we would treat seg differently from seg_from_previous_stage.
-            # Why not just concatenate them here and forget about the if statements? Well that's because segneeds to
-            # be padded with -1 constant whereas seg_from_previous_stage needs to be padded with 0s (we could also
-            # remove label -1 in the data augmentation but this way it is less error prone)
-
-            case_all_data = case_all_data[:, valid_bbox_x_lb:valid_bbox_x_ub,
-                            valid_bbox_y_lb:valid_bbox_y_ub]
-
-            case_all_data_donly = np.pad(case_all_data[:1], ((0, 0),
-                                                              (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
-                                                              (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0))),
-                                         self.pad_mode, **self.pad_kwargs_data)
-
-            case_all_data_segonly = np.pad(case_all_data[1:], ((0, 0),
-                                                                (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
-                                                                (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0))),
-                                           'constant', **{'constant_values': -1})
-
-            data[j] = case_all_data_donly
-            seg[j] = case_all_data_segonly
-
-        keys = selected_keys
-        return {'data': data, 'seg': seg, 'properties': case_properties, "keys": keys}
-
-
-if __name__ == "__main__":
-    t = "Task002_Heart"
-    p = join(preprocessing_output_dir, t, "stage1")
-    dataset = load_dataset(p)
-    with open(join(join(preprocessing_output_dir, t), "plans_stage1.pkl"), 'rb') as f:
-        plans = pickle.load(f)
-    unpack_dataset(p)
-    dl = DataLoader3D(dataset, (32, 32, 32), (32, 32, 32), 2, oversample_foreground_percent=0.33)
-    dl = DataLoader3D(dataset, np.array(plans['patch_size']).astype(int), np.array(plans['patch_size']).astype(int), 2,
-                      oversample_foreground_percent=0.33)
-    dl2d = DataLoader2D(dataset, (64, 64), np.array(plans['patch_size']).astype(int)[1:], 12,
-                        oversample_foreground_percent=0.33)
diff --git a/spaces/hrnph/rvc-models/infer_pack/modules.py b/spaces/hrnph/rvc-models/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/hrnph/rvc-models/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/hu-po/speech2speech/src/src/openailib.py b/spaces/hu-po/speech2speech/src/src/openailib.py
deleted file mode 100644
index 75acdf1eca37f2813c94799d865671c9a7cd81c2..0000000000000000000000000000000000000000
--- a/spaces/hu-po/speech2speech/src/src/openailib.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import logging
-import os
-
-from .utils import timeit
-
-import openai
-
-logging.basicConfig(level=logging.INFO)
-log = logging.getLogger(__name__)
-
-
-def set_openai_key(openai_api_key_textbox = None):
-    log.info(f"Setting OpenAI key.")
-    if openai_api_key_textbox is not None:
-        os.environ["OPENAI_API_KEY"] = openai_api_key_textbox
-    try:
-        openai.api_key = os.getenv("OPENAI_API_KEY")
-    except KeyError as e:
-        log.warning("OPENAI_API_KEY not found in environment variables.")
-        pass
-
-set_openai_key()
-
-@timeit
-def speech_to_text(audio_path):
-    log.info("Transcribing audio...")
-    transcript = openai.Audio.transcribe("whisper-1", open(audio_path, "rb"))
-    text = transcript["text"]
-    log.info(f"Transcript: \n\t{text}")
-    return text
-
-
-@timeit
-def top_response(prompt, system=None, model="gpt-3.5-turbo", max_tokens=20, temperature=0.8):
-    _prompt = [
-        {
-            "role": "user",
-            "content": prompt,
-        },
-    ]
-    if system:
-        _prompt = [
-            {
-                "role": "system",
-                "content": system,
-            },
-        ] + _prompt
-    log.info(f"API call to {model} with prompt: \n\n\t{_prompt}\n\n")
-    _response = openai.ChatCompletion.create(
-        model=model,
-        messages=_prompt,
-        temperature=temperature,
-        n=1,
-        max_tokens=max_tokens,
-    )
-    log.info(f"API reponse: \n\t{_response}")
-    response: str = _response['choices'][0]['message']['content']
-    return response
diff --git a/spaces/huggingchat/chat-ui/PRIVACY.md b/spaces/huggingchat/chat-ui/PRIVACY.md
deleted file mode 100644
index 234346feed69424298303a0ce6ae6fb0b895c4ec..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/PRIVACY.md
+++ /dev/null
@@ -1,36 +0,0 @@
-## Privacy
-
-> Last updated: October 4, 2023
-
-Users of HuggingChat are authenticated through their HF user account.
-
-By default, your conversations may be shared with the respective models' authors to improve their training data and model over time. Model authors are the custodians of the data collected by their model, even if it's hosted on our platform.
-
-If you disable data sharing in your settings, your conversations will not be used for any downstream usage (including for research or model training purposes), and they will only be stored to let you access past conversations. You can click on the Delete icon to delete any past conversation at any moment.
-
-🗓 Please also consult huggingface.co's main privacy policy at <https://huggingface.co/privacy>. To exercise any of your legal privacy rights, please send an email to <privacy@huggingface.co>.
-
-## About available LLMs
-
-The goal of this app is to showcase that it is now possible to build an open source alternative to ChatGPT. 💪
-
-For now (October 2023), it's running:
-
-- [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
-- [CodeLlama 35B](https://about.fb.com/news/2023/08/code-llama-ai-for-coding/)
-- [Falcon 180B](https://www.tii.ae/news/technology-innovation-institute-introduces-worlds-most-powerful-open-llm-falcon-180b)
-- [Mistral 7B](https://mistral.ai/news/announcing-mistral-7b/)
-
-## Technical details
-
-This app is running in a [Space](https://huggingface.co/docs/hub/spaces-overview), which entails that the code for this UI is publicly visible [inside the Space repo](https://huggingface.co/spaces/huggingchat/chat-ui/tree/main).
-
-**Further development takes place on the [huggingface/chat-ui GitHub repo](https://github.com/huggingface/chat-ui).**
-
-The inference backend is running the optimized [text-generation-inference](https://github.com/huggingface/text-generation-inference) on HuggingFace's Inference API infrastructure.
-
-It is therefore possible to deploy a copy of this app to a Space and customize it (swap model, add some UI elements, or store user messages according to your own Terms and conditions). You can also 1-click deploy your own instance using the [Chat UI Spaces Docker template](https://huggingface.co/new-space?template=huggingchat/chat-ui-template).
-
-We welcome any feedback on this app: please participate to the public discussion at <https://huggingface.co/spaces/huggingchat/chat-ui/discussions>
-
-<a target="_blank" href="https://huggingface.co/spaces/huggingchat/chat-ui/discussions"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-a-discussion-xl.svg" title="open a discussion"></a>
diff --git a/spaces/huggingchat/chat-ui/svelte.config.js b/spaces/huggingchat/chat-ui/svelte.config.js
deleted file mode 100644
index e93decaf872ef153bf12ba1a5aaad6e4937a2c87..0000000000000000000000000000000000000000
--- a/spaces/huggingchat/chat-ui/svelte.config.js
+++ /dev/null
@@ -1,29 +0,0 @@
-import adapter from "@sveltejs/adapter-node";
-import { vitePreprocess } from "@sveltejs/kit/vite";
-import dotenv from "dotenv";
-
-dotenv.config({ path: "./.env.local" });
-dotenv.config({ path: "./.env" });
-
-process.env.PUBLIC_VERSION = process.env.npm_package_version;
-
-/** @type {import('@sveltejs/kit').Config} */
-const config = {
-	// Consult https://kit.svelte.dev/docs/integrations#preprocessors
-	// for more information about preprocessors
-	preprocess: vitePreprocess(),
-
-	kit: {
-		adapter: adapter(),
-
-		paths: {
-			base: process.env.APP_BASE || "",
-		},
-		csrf: {
-			// handled in hooks.server.ts, because we can have multiple valid origins
-			checkOrigin: false,
-		},
-	},
-};
-
-export default config;
diff --git a/spaces/huggingface-projects/huggingbots/falcon.py b/spaces/huggingface-projects/huggingbots/falcon.py
deleted file mode 100644
index 04204bcfb6658ffb5af9b93b706837171380ef6a..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/huggingbots/falcon.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import asyncio
-import os
-from typing import Optional
-
-import gradio_client as grc
-from gradio_client.utils import QueueError
-
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-
-BOT_USER_ID = 1086256910572986469 if os.getenv("TEST_ENV", False) else 1102236653545861151
-FALCON_CHANNEL_ID = 1079459939405279232 if os.getenv("TEST_ENV", False) else 1119313248056004729
-
-
-thread_to_client = {}
-thread_to_user = {}
-
-
-async def wait(job):
-    while not job.done():
-        await asyncio.sleep(0.2)
-
-
-def get_client(session: Optional[str] = None) -> grc.Client:
-    client = grc.Client("huggingface-projects/falcon-180b-discord", hf_token=os.getenv("HF_TOKEN"))
-    if session:
-        client.session_hash = session
-    return client
-
-
-async def falcon_chat(ctx, prompt):
-    """Generates text based on a given prompt"""
-    try:
-        if ctx.author.id != BOT_USER_ID:
-            if ctx.channel.id == FALCON_CHANNEL_ID:
-                if os.environ.get("TEST_ENV") == "True":
-                    print("Safetychecks passed for try_falcon")
-                message = await ctx.send(f"**{prompt}** - {ctx.author.mention}")
-                if len(prompt) > 99:
-                    small_prompt = prompt[:99]
-                else:
-                    small_prompt = prompt
-                thread = await message.create_thread(name=small_prompt, auto_archive_duration=60)  # interaction.user
-
-                if os.environ.get("TEST_ENV") == "True":
-                    print("Running falcon_initial_generation...")
-                loop = asyncio.get_running_loop()
-                client = await loop.run_in_executor(None, get_client, None)
-                job = client.submit(prompt, api_name="/chat")
-                await wait(job)
-                try:
-                    job.result()
-                    response = job.outputs()[-1]
-                    await thread.send(response)
-                    thread_to_client[thread.id] = client
-                    thread_to_user[thread.id] = ctx.author.id
-                except QueueError:
-                    await thread.send("The gradio space powering this bot is really busy! Please try again later!")
-    except Exception as e:
-        print(f"chat (180B) Error: {e}")
-
-
-async def continue_chat(message):
-    """Continues a given conversation based on chathistory"""
-    try:
-        if message.channel.id in thread_to_user:
-            if thread_to_user[message.channel.id] == message.author.id:
-                client = thread_to_client[message.channel.id]
-                job = client.submit(message.content, api_name="/chat")
-                await wait(job)
-                try:
-                    job.result()
-                    response = job.outputs()[-1]
-                    await message.reply(response)
-                except QueueError:
-                    await message.reply("The gradio space powering this bot is really busy! Please try again later!")
-    except Exception as e:
-        print(f"continue_falcon Error: {e}")
-        await message.reply(f"Error: {e} <@811235357663297546> (continue_falcon error)")
diff --git a/spaces/hungln1102/emotion_classification_surreynlp_2023/README.md b/spaces/hungln1102/emotion_classification_surreynlp_2023/README.md
deleted file mode 100644
index 2f5a3b339287efafe1c2c4e450f634ce759126c0..0000000000000000000000000000000000000000
--- a/spaces/hungln1102/emotion_classification_surreynlp_2023/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Emotion Classification Surreynlp 2023
-emoji: ⚡
-colorFrom: pink
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hysts/StyleGAN-Human/app.py b/spaces/hysts/StyleGAN-Human/app.py
deleted file mode 100644
index b5b0fea51bc244b161b243993274d90dbe8c967c..0000000000000000000000000000000000000000
--- a/spaces/hysts/StyleGAN-Human/app.py
+++ /dev/null
@@ -1,69 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import functools
-import pickle
-import sys
-
-import gradio as gr
-import numpy as np
-import torch
-import torch.nn as nn
-from huggingface_hub import hf_hub_download
-
-sys.path.insert(0, 'StyleGAN-Human')
-
-TITLE = 'StyleGAN-Human'
-DESCRIPTION = 'https://github.com/stylegan-human/StyleGAN-Human'
-
-
-def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor:
-    return torch.from_numpy(np.random.RandomState(seed).randn(
-        1, z_dim)).to(device).float()
-
-
-@torch.inference_mode()
-def generate_image(seed: int, truncation_psi: float, model: nn.Module,
-                   device: torch.device) -> np.ndarray:
-    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
-
-    z = generate_z(model.z_dim, seed, device)
-    label = torch.zeros([1, model.c_dim], device=device)
-
-    out = model(z, label, truncation_psi=truncation_psi, force_fp32=True)
-    out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-    return out[0].cpu().numpy()
-
-
-def load_model(file_name: str, device: torch.device) -> nn.Module:
-    path = hf_hub_download('public-data/StyleGAN-Human', f'models/{file_name}')
-    with open(path, 'rb') as f:
-        model = pickle.load(f)['G_ema']
-    model.eval()
-    model.to(device)
-    with torch.inference_mode():
-        z = torch.zeros((1, model.z_dim)).to(device)
-        label = torch.zeros([1, model.c_dim], device=device)
-        model(z, label, force_fp32=True)
-    return model
-
-
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-model = load_model('stylegan_human_v2_1024.pkl', device)
-fn = functools.partial(generate_image, model=model, device=device)
-
-gr.Interface(
-    fn=fn,
-    inputs=[
-        gr.Slider(label='Seed', minimum=0, maximum=100000, step=1, value=0),
-        gr.Slider(label='Truncation psi',
-                  minimum=0,
-                  maximum=2,
-                  step=0.05,
-                  value=0.7),
-    ],
-    outputs=gr.Image(label='Output', type='numpy'),
-    title=TITLE,
-    description=DESCRIPTION,
-).queue(max_size=10).launch()
diff --git a/spaces/hysts/age-estimation-APPA-REAL/style.css b/spaces/hysts/age-estimation-APPA-REAL/style.css
deleted file mode 100644
index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000
--- a/spaces/hysts/age-estimation-APPA-REAL/style.css
+++ /dev/null
@@ -1,3 +0,0 @@
-h1 {
-  text-align: center;
-}
diff --git a/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/README.md b/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/README.md
deleted file mode 100644
index e120242f3504d45c3264166ec9d9f276164c8cbc..0000000000000000000000000000000000000000
--- a/spaces/hysts/cv_diffusion_text-to-image-synthesis_tiny/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ModelScope Chinese text2image (tiny)
-emoji: 👀
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py b/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py
deleted file mode 100644
index b8a00d6305eeda5a94788017afc1cda0d4a4cd2a..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "mbf"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 2e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 20, 25]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/igashov/DiffLinker/src/delinker_utils/frag_utils.py b/spaces/igashov/DiffLinker/src/delinker_utils/frag_utils.py
deleted file mode 100644
index 0c4d4634d39e5419bd3c0ebc3df755eceec6278f..0000000000000000000000000000000000000000
--- a/spaces/igashov/DiffLinker/src/delinker_utils/frag_utils.py
+++ /dev/null
@@ -1,413 +0,0 @@
-import csv
-import networkx as nx
-
-from joblib import Parallel, delayed
-from rdkit import Chem
-from rdkit.Chem import AllChem
-from src.delinker_utils import sascorer
-
-
-def read_triples_file(filename):
-    '''Reads .smi file '''
-    '''Returns array containing smiles strings of molecules'''
-    smiles, names = [], []
-    with open(filename, 'r') as f:
-        for line in f:
-            if line:
-                smiles.append(line.strip().split(' ')[0:3])
-    return smiles
-
-
-def remove_dummys(smi_string):
-    return Chem.MolToSmiles(Chem.RemoveHs(AllChem.ReplaceSubstructs(Chem.MolFromSmiles(smi_string),Chem.MolFromSmiles('*'),Chem.MolFromSmiles('[H]'),True)[0]))
-
-
-def sa_filter(results, verbose=True):
-    count = 0
-    total = 0
-    for processed, res in enumerate(results):
-        total += len(res)
-        for m in res:
-            # Check SA score has improved
-            if calc_mol_props(m[1])[1] < calc_mol_props(m[0])[1]:
-                count += 1
-        # Progress
-        if verbose:
-            if processed % 10 == 0:
-                print("\rProcessed %d" % processed, end="")
-    print("\r",end="")
-    return count/total
-
-
-def ring_check_res(res, clean_frag):
-    check = True
-    gen_mol = Chem.MolFromSmiles(res[1])
-    linker = Chem.DeleteSubstructs(gen_mol, clean_frag)
-
-    # Get linker rings
-    ssr = Chem.GetSymmSSSR(linker)
-    # Check rings
-    for ring in ssr:
-        for atom_idx in ring:
-            for bond in linker.GetAtomWithIdx(atom_idx).GetBonds():
-                if bond.GetBondType() == 2 and bond.GetBeginAtomIdx() in ring and bond.GetEndAtomIdx() in ring:
-                    check = False
-    return check
-
-
-def ring_filter(results, verbose=True):
-    count = 0
-    total = 0
-    du = Chem.MolFromSmiles('*')
-    for processed, res in enumerate(results):
-        total += len(res)
-        for m in res:
-            # Clean frags
-            clean_frag = Chem.RemoveHs(AllChem.ReplaceSubstructs(Chem.MolFromSmiles(m[0]),du,Chem.MolFromSmiles('[H]'),True)[0])
-            if ring_check_res(m, clean_frag):
-                count += 1
-        # Progress
-        if verbose:
-            if processed % 10 == 0:
-                print("\rProcessed %d" % processed, end="")
-    print("\r",end="")
-    return count/total
-
-
-def check_ring_filter(linker):
-    check = True
-    # Get linker rings
-    ssr = Chem.GetSymmSSSR(linker)
-    # Check rings
-    for ring in ssr:
-        for atom_idx in ring:
-            for bond in linker.GetAtomWithIdx(atom_idx).GetBonds():
-                if bond.GetBondType() == 2 and bond.GetBeginAtomIdx() in ring and bond.GetEndAtomIdx() in ring:
-                    check = False
-    return check
-
-
-def check_pains(mol, pains_smarts):
-    for pain in pains_smarts:
-        if mol.HasSubstructMatch(pain):
-            return False
-    return True
-
-
-def calc_2d_filters(toks, pains_smarts):
-    try:
-        # Input format: (Full Molecule (SMILES), Linker (SMILES), Unlinked Fragments (SMILES))
-        frags = Chem.MolFromSmiles(toks[2])
-        linker = Chem.MolFromSmiles(toks[1])
-        full_mol = Chem.MolFromSmiles(toks[0])
-        # Remove dummy atoms from unlinked fragments
-        du = Chem.MolFromSmiles('*')
-        clean_frag = Chem.RemoveHs(AllChem.ReplaceSubstructs(frags, du, Chem.MolFromSmiles('[H]'), True)[0])
-
-        res = []
-        # Check: Unlinked fragments in full molecule
-        if len(full_mol.GetSubstructMatch(clean_frag)) > 0:
-            # Check: SA score improved from unlinked fragments to full molecule
-            if calc_sa_score_mol(full_mol) < calc_sa_score_mol(frags):
-                res.append(True)
-            else:
-                res.append(False)
-            # Check: No non-aromatic rings with double bonds
-            if check_ring_filter(linker):
-                res.append(True)
-            else:
-                res.append(False)
-            # Check: Pass pains filters
-            if check_pains(full_mol, pains_smarts):
-                res.append(True)
-            else:
-                res.append(False)
-        return res
-    except:
-        return [False, False, False]
-
-
-def calc_filters_2d_dataset(results, pains_smarts_loc, n_cores=1):
-    # Load pains filters
-    with open(pains_smarts_loc, 'r') as f:
-        pains_smarts = [Chem.MolFromSmarts(line[0], mergeHs=True) for line in csv.reader(f)]
-    # calc_2d_filters([results[0][2], results[0][4], results[0][1]], pains_smarts)
-    with Parallel(n_jobs=n_cores, backend='multiprocessing') as parallel:
-        filters_2d = parallel(delayed(calc_2d_filters)([toks[2], toks[4], toks[1]], pains_smarts) for toks in results)
-
-    return filters_2d
-
-
-def calc_mol_props(smiles):
-    # Create RDKit mol
-    mol = Chem.MolFromSmiles(smiles)
-    if mol is None:
-        print("Error passing: %s" % smiles)
-        return None
-
-    # QED
-    qed = Chem.QED.qed(mol)
-    # Synthetic accessibility score - number of cycles (rings with > 6 atoms)
-    sas = sascorer.calculateScore(mol)
-    # Cyles with >6 atoms
-    ri = mol.GetRingInfo()
-    nMacrocycles = 0
-    for x in ri.AtomRings():
-        if len(x) > 6:
-            nMacrocycles += 1
-
-    prop_array = [qed, sas]
-
-    return prop_array
-
-
-def calc_sa_score_mol(mol, verbose=False):
-    if mol is None:
-        if verbose:
-            print("Error passing: %s" % mol)
-        return None
-    # Synthetic accessibility score
-    return sascorer.calculateScore(mol)
-
-
-def get_linker(full_mol, clean_frag, starting_point):
-    # INPUT FORMAT: molecule (RDKit mol object), clean fragments (RDKit mol object), starting fragments (SMILES)
-
-    # Get matches of fragments
-    matches = list(full_mol.GetSubstructMatches(clean_frag))
-
-    # If no matches, terminate
-    if len(matches) == 0:
-        print("No matches")
-        return ""
-
-    # Get number of atoms in linker
-    linker_len = full_mol.GetNumHeavyAtoms() - clean_frag.GetNumHeavyAtoms()
-    if linker_len == 0:
-        return ""
-
-    # Setup
-    mol_to_break = Chem.Mol(full_mol)
-    Chem.Kekulize(full_mol, clearAromaticFlags=True)
-
-    poss_linker = []
-
-    if len(matches) > 0:
-        # Loop over matches
-        for match in matches:
-            mol_rw = Chem.RWMol(full_mol)
-            # Get linker atoms
-            linker_atoms = list(set(list(range(full_mol.GetNumHeavyAtoms()))).difference(match))
-            linker_bonds = []
-            atoms_joined_to_linker = []
-            # Loop over starting fragments atoms
-            # Get (i) bonds between starting fragments and linker, (ii) atoms joined to linker
-            for idx_to_delete in sorted(match, reverse=True):
-                nei = [x.GetIdx() for x in mol_rw.GetAtomWithIdx(idx_to_delete).GetNeighbors()]
-                intersect = set(nei).intersection(set(linker_atoms))
-                if len(intersect) == 1:
-                    linker_bonds.append(mol_rw.GetBondBetweenAtoms(idx_to_delete, list(intersect)[0]).GetIdx())
-                    atoms_joined_to_linker.append(idx_to_delete)
-                elif len(intersect) > 1:
-                    for idx_nei in list(intersect):
-                        linker_bonds.append(mol_rw.GetBondBetweenAtoms(idx_to_delete, idx_nei).GetIdx())
-                        atoms_joined_to_linker.append(idx_to_delete)
-
-            # Check number of atoms joined to linker
-            # If not == 2, check next match
-            if len(set(atoms_joined_to_linker)) != 2:
-                continue
-
-            # Delete starting fragments atoms
-            for idx_to_delete in sorted(match, reverse=True):
-                mol_rw.RemoveAtom(idx_to_delete)
-
-            linker = Chem.Mol(mol_rw)
-            # Check linker required num atoms
-            if linker.GetNumHeavyAtoms() == linker_len:
-                mol_rw = Chem.RWMol(full_mol)
-                # Delete linker atoms
-                for idx_to_delete in sorted(linker_atoms, reverse=True):
-                    mol_rw.RemoveAtom(idx_to_delete)
-                frags = Chem.Mol(mol_rw)
-                # Check there are two disconnected fragments
-                if len(Chem.rdmolops.GetMolFrags(frags)) == 2:
-                    # Fragment molecule into starting fragments and linker
-                    fragmented_mol = Chem.FragmentOnBonds(mol_to_break, linker_bonds)
-                    # Remove starting fragments from fragmentation
-                    linker_to_return = Chem.Mol(fragmented_mol)
-                    qp = Chem.AdjustQueryParameters()
-                    qp.makeDummiesQueries = True
-                    for f in starting_point.split('.'):
-                        qfrag = Chem.AdjustQueryProperties(Chem.MolFromSmiles(f), qp)
-                        linker_to_return = AllChem.DeleteSubstructs(linker_to_return, qfrag, onlyFrags=True)
-
-                    # Check linker is connected and two bonds to outside molecule
-                    if len(Chem.rdmolops.GetMolFrags(linker)) == 1 and len(linker_bonds) == 2:
-                        Chem.Kekulize(linker_to_return, clearAromaticFlags=True)
-                        # If for some reason a starting fragment isn't removed (and it's larger than the linker), remove (happens v. occassionally)
-                        if len(Chem.rdmolops.GetMolFrags(linker_to_return)) > 1:
-                            for frag in Chem.MolToSmiles(linker_to_return).split('.'):
-                                if Chem.MolFromSmiles(frag).GetNumHeavyAtoms() == linker_len:
-                                    return frag
-                        return Chem.MolToSmiles(Chem.MolFromSmiles(Chem.MolToSmiles(linker_to_return)))
-
-                    # If not, add to possible linkers (above doesn't capture some complex cases)
-                    else:
-                        fragmented_mol = Chem.MolFromSmiles(Chem.MolToSmiles(fragmented_mol), sanitize=False)
-                        linker_to_return = AllChem.DeleteSubstructs(fragmented_mol, Chem.MolFromSmiles(starting_point))
-                        poss_linker.append(Chem.MolToSmiles(linker_to_return))
-
-    # If only one possibility, return linker
-    if len(poss_linker) == 1:
-        return poss_linker[0]
-    # If no possibilities, process failed
-    elif len(poss_linker) == 0:
-        print("FAIL:", Chem.MolToSmiles(full_mol), Chem.MolToSmiles(clean_frag), starting_point)
-        return ""
-    # If multiple possibilities, process probably failed
-    else:
-        print("More than one poss linker. ", poss_linker)
-        return poss_linker[0]
-
-
-def get_linker_v2(full_mol, clean_frag):
-    # INPUT FORMAT: molecule (RDKit mol object), clean fragments (RDKit mol object), starting fragments (SMILES)
-
-    # Get matches of fragments
-    matches = list(full_mol.GetSubstructMatches(clean_frag))
-
-    # If no matches, terminate
-    if len(matches) == 0:
-        print("No matches")
-        return ""
-
-    # Get number of atoms in linker
-    linker_len = full_mol.GetNumHeavyAtoms() - clean_frag.GetNumHeavyAtoms()
-    if linker_len == 0:
-        return ""
-
-    # Setup
-    mol_to_break = Chem.Mol(full_mol)
-    Chem.Kekulize(full_mol, clearAromaticFlags=True)
-
-    poss_linker = []
-
-    if len(matches) > 0:
-        # Loop over matches
-        for match in matches:
-            mol_rw = Chem.RWMol(full_mol)
-            # Get linker atoms
-            linker_atoms = list(set(list(range(full_mol.GetNumHeavyAtoms()))).difference(match))
-            linker_bonds = []
-            atoms_joined_to_linker = []
-            # Loop over starting fragments atoms
-            # Get (i) bonds between starting fragments and linker, (ii) atoms joined to linker
-            for idx_to_delete in sorted(match, reverse=True):
-                nei = [x.GetIdx() for x in mol_rw.GetAtomWithIdx(idx_to_delete).GetNeighbors()]
-                intersect = set(nei).intersection(set(linker_atoms))
-                if len(intersect) == 1:
-                    linker_bonds.append(mol_rw.GetBondBetweenAtoms(idx_to_delete, list(intersect)[0]).GetIdx())
-                    atoms_joined_to_linker.append(idx_to_delete)
-                elif len(intersect) > 1:
-                    for idx_nei in list(intersect):
-                        linker_bonds.append(mol_rw.GetBondBetweenAtoms(idx_to_delete, idx_nei).GetIdx())
-                        atoms_joined_to_linker.append(idx_to_delete)
-
-            # Check number of atoms joined to linker
-            # If not == 2, check next match
-            if len(set(atoms_joined_to_linker)) != 2:
-                continue
-
-            # Delete starting fragments atoms
-            for idx_to_delete in sorted(match, reverse=True):
-                mol_rw.RemoveAtom(idx_to_delete)
-
-            linker = Chem.Mol(mol_rw)
-            # Check linker required num atoms
-            if linker.GetNumHeavyAtoms() == linker_len:
-                mol_rw = Chem.RWMol(full_mol)
-                # Delete linker atoms
-                for idx_to_delete in sorted(linker_atoms, reverse=True):
-                    mol_rw.RemoveAtom(idx_to_delete)
-                frags = Chem.Mol(mol_rw)
-
-                # Check linker is connected and two bonds to outside molecule
-                if len(Chem.rdmolops.GetMolFrags(linker)) == 1 and len(linker_bonds) == 2:
-                    Chem.Kekulize(linker, clearAromaticFlags=True)
-                    # If for some reason a starting fragment isn't removed (and it's larger than the linker), remove (happens v. occassionally)
-                    if len(Chem.rdmolops.GetMolFrags(linker)) > 1:
-                        for frag in Chem.MolToSmiles(linker).split('.'):
-                            if Chem.MolFromSmiles(frag).GetNumHeavyAtoms() == linker_len:
-                                return frag
-                    return Chem.MolToSmiles(Chem.MolFromSmiles(Chem.MolToSmiles(linker)))
-
-                # If not, add to possible linkers (above doesn't capture some complex cases)
-                else:
-                    poss_linker.append(Chem.MolToSmiles(linker))
-
-    # If only one possibility, return linker
-    if len(poss_linker) == 1:
-        return poss_linker[0]
-    # If no possibilities, process failed
-    elif len(poss_linker) == 0:
-        print("FAIL:", Chem.MolToSmiles(full_mol), Chem.MolToSmiles(clean_frag))
-        return ""
-    # If multiple possibilities, process probably failed
-    else:
-        print("More than one poss linker. ", poss_linker)
-        return poss_linker[0]
-
-
-def unique(results):
-    total_dupes = 0
-    total = 0
-    for res in results:
-        original_num = len(res)
-        test_data = set(res)
-        new_num = len(test_data)
-        total_dupes += original_num - new_num
-        total += original_num
-    return 1 - total_dupes/float(total)
-
-
-def check_recovered_original_mol_with_idx(results):
-    outcomes = []
-    rec_idx = []
-    for res in results:
-        success = False
-        # Load original mol and canonicalise
-        orig_mol = Chem.MolFromSmiles(res[0][0][0])
-        Chem.RemoveStereochemistry(orig_mol)
-        orig_mol = Chem.MolToSmiles(Chem.RemoveHs(orig_mol))
-        #orig_mol = MolStandardize.canonicalize_tautomer_smiles(orig_mol)
-        # Check generated mols
-        for m in res:
-            # print(1)
-            gen_mol = Chem.MolFromSmiles(m[0][2])
-            Chem.RemoveStereochemistry(gen_mol)
-            gen_mol = Chem.MolToSmiles(Chem.RemoveHs(gen_mol))
-            #gen_mol = MolStandardize.canonicalize_tautomer_smiles(gen_mol)
-            if gen_mol == orig_mol:
-                # outcomes.append(True)
-                success = True
-                rec_idx.append(m[1])
-                # break
-        if not success:
-            outcomes.append(False)
-        else:
-            outcomes.append(True)
-    return outcomes, rec_idx
-
-
-def topology_from_rdkit(rdkit_molecule):
-    topology = nx.Graph()
-    for atom in rdkit_molecule.GetAtoms():
-        # Add the atoms as nodes
-        topology.add_node(atom.GetIdx(), atom_type=atom.GetAtomicNum())
-
-        # Add the bonds as edges
-    for bond in rdkit_molecule.GetBonds():
-        topology.add_edge(bond.GetBeginAtomIdx(), bond.GetEndAtomIdx(), bond_type=bond.GetBondType())
-
-    return topology
diff --git a/spaces/imkaushalpatel/GoogleNet/README.md b/spaces/imkaushalpatel/GoogleNet/README.md
deleted file mode 100644
index df2519b98833be095d810dff5e8374d8f314106a..0000000000000000000000000000000000000000
--- a/spaces/imkaushalpatel/GoogleNet/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GoogleNet
-emoji: 👀
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/imperialwool/funapi/routes/holidays/__init__.py b/spaces/imperialwool/funapi/routes/holidays/__init__.py
deleted file mode 100644
index dc9bf64150abd2bd148fab643b4346d947749f75..0000000000000000000000000000000000000000
--- a/spaces/imperialwool/funapi/routes/holidays/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .getHolidays import *
\ No newline at end of file
diff --git a/spaces/inflaton/learn-ai/app_modules/llm_loader.py b/spaces/inflaton/learn-ai/app_modules/llm_loader.py
deleted file mode 100644
index 7773cbaa83b5269f46e5d29e8ce2dbfe8269cec3..0000000000000000000000000000000000000000
--- a/spaces/inflaton/learn-ai/app_modules/llm_loader.py
+++ /dev/null
@@ -1,659 +0,0 @@
-import os
-import sys
-import threading
-from queue import Queue
-from typing import Any, Optional
-
-import torch
-from langchain import HuggingFaceTextGenInference
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import (
-    CTransformers,
-    GPT4All,
-    HuggingFacePipeline,
-    LlamaCpp,
-    OpenLLM,
-)
-from langchain.schema import LLMResult
-from transformers import (
-    AutoConfig,
-    AutoModelForCausalLM,
-    AutoModelForSeq2SeqLM,
-    AutoTokenizer,
-    BitsAndBytesConfig,
-    StoppingCriteria,
-    StoppingCriteriaList,
-    T5Tokenizer,
-    TextStreamer,
-    pipeline,
-)
-
-from app_modules.instruct_pipeline import InstructionTextGenerationPipeline
-from app_modules.utils import ensure_model_is_downloaded
-
-
-class TextIteratorStreamer(TextStreamer, StreamingStdOutCallbackHandler):
-    def __init__(
-        self,
-        tokenizer: "AutoTokenizer",
-        skip_prompt: bool = False,
-        timeout: Optional[float] = None,
-        for_huggingface: bool = False,
-        **decode_kwargs,
-    ):
-        super().__init__(tokenizer, skip_prompt, **decode_kwargs)
-        self.text_queue = Queue()
-        self.stop_signal = None
-        self.timeout = timeout
-        self.total_tokens = 0
-        self.for_huggingface = for_huggingface
-
-    def on_finalized_text(self, text: str, stream_end: bool = False):
-        super().on_finalized_text(text, stream_end=stream_end)
-
-        """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue."""
-        self.text_queue.put(text, timeout=self.timeout)
-        self.total_tokens = self.total_tokens + 1
-        if stream_end:
-            print("\n")
-            self.text_queue.put("\n", timeout=self.timeout)
-            self.text_queue.put(self.stop_signal, timeout=self.timeout)
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        sys.stdout.write(token)
-        sys.stdout.flush()
-        self.text_queue.put(token, timeout=self.timeout)
-        self.total_tokens = self.total_tokens + 1
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        print("\n")
-        self.text_queue.put("\n", timeout=self.timeout)
-        self.text_queue.put(self.stop_signal, timeout=self.timeout)
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        value = self.text_queue.get(timeout=self.timeout)
-        if value == self.stop_signal:
-            raise StopIteration()
-        else:
-            return value
-
-    def reset(self, q: Queue = None):
-        # print("resetting TextIteratorStreamer")
-        self.text_queue = q if q is not None else Queue()
-
-    def empty(self):
-        return self.text_queue.empty()
-
-
-class LLMLoader:
-    llm_model_type: str
-    llm: any
-    streamer: any
-    max_tokens_limit: int
-    lock: any
-
-    def __init__(self, llm_model_type):
-        self.llm_model_type = llm_model_type
-        self.llm = None
-        self.streamer = TextIteratorStreamer(
-            "",
-            for_huggingface=True,
-        )
-        self.max_tokens_limit = 2048
-        self.search_kwargs = {"k": 4}
-        self.lock = threading.Lock()
-
-    def _init_hf_streamer(self, tokenizer):
-        self.streamer = TextIteratorStreamer(
-            tokenizer,
-            timeout=10.0,
-            skip_prompt=True,
-            skip_special_tokens=True,
-            for_huggingface=True,
-        )
-
-    def init(
-        self,
-        custom_handler: Optional[BaseCallbackHandler] = None,
-        n_threds: int = 4,
-        hf_pipeline_device_type: str = None,
-    ):
-        print("initializing LLM: " + self.llm_model_type)
-
-        if hf_pipeline_device_type is None:
-            hf_pipeline_device_type = "cpu"
-
-        using_cuda = hf_pipeline_device_type.startswith("cuda")
-        torch_dtype = torch.float16 if using_cuda else torch.float32
-        if os.environ.get("USING_TORCH_BFLOAT16") == "true":
-            torch_dtype = torch.bfloat16
-        load_quantized_model = os.environ.get("LOAD_QUANTIZED_MODEL")
-
-        print(f"  hf_pipeline_device_type: {hf_pipeline_device_type}")
-        print(f"     load_quantized_model: {load_quantized_model}")
-        print(f"              torch_dtype: {torch_dtype}")
-        print(f"                 n_threds: {n_threds}")
-
-        double_quant_config = BitsAndBytesConfig(
-            load_in_4bit=load_quantized_model == "4bit",
-            bnb_4bit_use_double_quant=load_quantized_model == "4bit",
-            load_in_8bit=load_quantized_model == "8bit",
-            bnb_8bit_use_double_quant=load_quantized_model == "8bit",
-        )
-
-        callbacks = []
-        if self.streamer is not None:
-            callbacks.append(self.streamer)
-        if custom_handler is not None:
-            callbacks.append(custom_handler)
-
-        if self.llm is None:
-            if self.llm_model_type == "openai":
-                MODEL_NAME = os.environ.get("OPENAI_MODEL_NAME") or "gpt-3.5-turbo"
-                print(f"              using model: {MODEL_NAME}")
-                self.llm = ChatOpenAI(
-                    model_name=MODEL_NAME,
-                    streaming=True,
-                    callbacks=callbacks,
-                    verbose=True,
-                    temperature=0,
-                )
-            elif self.llm_model_type == "openllm":
-                server_url = os.environ.get("OPENLLM_SERVER_URL")
-                print(f"               server url: {server_url}")
-                self.llm = OpenLLM(
-                    server_url=server_url,
-                    # callbacks=callbacks,
-                    verbose=True,
-                )
-            elif self.llm_model_type.startswith("gpt4all"):
-                MODEL_PATH = ensure_model_is_downloaded(self.llm_model_type)
-                self.llm = GPT4All(
-                    model=MODEL_PATH,
-                    max_tokens=2048,
-                    n_threads=n_threds,
-                    backend="gptj" if self.llm_model_type == "gpt4all-j" else "llama",
-                    callbacks=callbacks,
-                    verbose=True,
-                    use_mlock=True,
-                )
-            elif self.llm_model_type == "llamacpp":
-                MODEL_PATH = ensure_model_is_downloaded(self.llm_model_type)
-                self.llm = LlamaCpp(
-                    model_path=MODEL_PATH,
-                    n_ctx=8192,
-                    n_threads=n_threds,
-                    seed=0,
-                    temperature=0,
-                    max_tokens=2048,
-                    callbacks=callbacks,
-                    verbose=True,
-                    use_mlock=True,
-                )
-            elif self.llm_model_type == "ctransformers":
-                MODEL_PATH = ensure_model_is_downloaded(self.llm_model_type)
-                config = {
-                    "max_new_tokens": self.max_tokens_limit,
-                    "repetition_penalty": 1.1,
-                }
-                self.llm = CTransformers(
-                    model=MODEL_PATH,
-                    model_type="llama",
-                    config=config,
-                    callbacks=callbacks,
-                )
-            elif self.llm_model_type == "hftgi":
-                HFTGI_SERVER_URL = os.environ.get("HFTGI_SERVER_URL")
-                self.max_tokens_limit = 4096
-                self.llm = HuggingFaceTextGenInference(
-                    inference_server_url=HFTGI_SERVER_URL,
-                    max_new_tokens=self.max_tokens_limit / 2,
-                    # top_k=0,
-                    top_p=0.95,
-                    # typical_p=0.95,
-                    temperature=0.01,
-                    repetition_penalty=1.12,
-                    callbacks=callbacks,
-                    timeout=600,
-                    streaming=True,
-                )
-            elif self.llm_model_type.startswith("huggingface"):
-                MODEL_NAME_OR_PATH = os.environ.get("HUGGINGFACE_MODEL_NAME_OR_PATH")
-                print(f"            loading model: {MODEL_NAME_OR_PATH}")
-
-                hf_auth_token = (
-                    os.environ.get("HUGGINGFACE_AUTH_TOKEN")
-                    if "Llama-2" in MODEL_NAME_OR_PATH
-                    else None
-                )
-                transformers_offline = os.environ.get("TRANSFORMERS_OFFLINE") == "1"
-                token = (
-                    hf_auth_token
-                    if hf_auth_token is not None
-                    and len(hf_auth_token) > 0
-                    and not transformers_offline
-                    else None
-                )
-                print(f"            HF auth token: {str(token)[-5:]}")
-
-                if "Llama-2" in MODEL_NAME_OR_PATH:
-                    self.max_tokens_limit = 4096
-
-                is_t5 = "t5" in MODEL_NAME_OR_PATH
-                temperature = (
-                    0.01
-                    if "gpt4all-j" in MODEL_NAME_OR_PATH
-                    or "dolly" in MODEL_NAME_OR_PATH
-                    or "Qwen" in MODEL_NAME_OR_PATH
-                    or "Llama-2" in MODEL_NAME_OR_PATH
-                    else 0
-                )
-                use_fast = (
-                    "stable" in MODEL_NAME_OR_PATH
-                    or "RedPajama" in MODEL_NAME_OR_PATH
-                    or "dolly" in MODEL_NAME_OR_PATH
-                )
-                padding_side = "left"  # if "dolly" in MODEL_NAME_OR_PATH else None
-
-                config = (
-                    AutoConfig.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        trust_remote_code=True,
-                        token=token,
-                        fp32=hf_pipeline_device_type == "cpu",
-                        bf16=(
-                            hf_pipeline_device_type != "cpu"
-                            and torch_dtype == torch.bfloat16
-                        ),
-                        fp16=(
-                            hf_pipeline_device_type != "cpu"
-                            and torch_dtype != torch.bfloat16
-                        ),
-                    )
-                    if "Qwen" in MODEL_NAME_OR_PATH
-                    else AutoConfig.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        trust_remote_code=True,
-                        token=token,
-                    )
-                )
-
-                # config.attn_config["attn_impl"] = "triton"
-                # config.max_seq_len = 4096
-                config.init_device = hf_pipeline_device_type
-
-                tokenizer = (
-                    T5Tokenizer.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        token=token,
-                    )
-                    if is_t5
-                    else AutoTokenizer.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        use_fast=use_fast,
-                        trust_remote_code=True,
-                        padding_side=padding_side,
-                        token=token,
-                    )
-                )
-
-                self._init_hf_streamer(tokenizer)
-
-                task = "text2text-generation" if is_t5 else "text-generation"
-
-                return_full_text = True if "dolly" in MODEL_NAME_OR_PATH else None
-
-                repetition_penalty = (
-                    1.15
-                    if "falcon" in MODEL_NAME_OR_PATH
-                    else (1.25 if "dolly" in MODEL_NAME_OR_PATH else 1.1)
-                )
-
-                if load_quantized_model is not None:
-                    model = (
-                        AutoModelForSeq2SeqLM.from_pretrained(
-                            MODEL_NAME_OR_PATH,
-                            config=config,
-                            quantization_config=double_quant_config,
-                            trust_remote_code=True,
-                            use_auth_token=token,
-                        )
-                        if is_t5
-                        else AutoModelForCausalLM.from_pretrained(
-                            MODEL_NAME_OR_PATH,
-                            config=config,
-                            quantization_config=double_quant_config,
-                            trust_remote_code=True,
-                            use_auth_token=token,
-                        )
-                    )
-
-                    print(f"Model memory footprint: {model.get_memory_footprint()}")
-
-                    eos_token_id = -1
-                    # starchat-beta uses a special <|end|> token with ID 49155 to denote ends of a turn
-                    if "starchat" in MODEL_NAME_OR_PATH:
-                        eos_token_id = 49155
-                    pad_token_id = eos_token_id
-
-                    pipe = (
-                        InstructionTextGenerationPipeline(
-                            task=task,
-                            model=model,
-                            tokenizer=tokenizer,
-                            streamer=self.streamer,
-                            max_new_tokens=2048,
-                            temperature=temperature,
-                            return_full_text=return_full_text,  # langchain expects the full text
-                            repetition_penalty=repetition_penalty,
-                        )
-                        if "dolly" in MODEL_NAME_OR_PATH
-                        else (
-                            pipeline(
-                                task,
-                                model=model,
-                                tokenizer=tokenizer,
-                                eos_token_id=eos_token_id,
-                                pad_token_id=pad_token_id,
-                                streamer=self.streamer,
-                                return_full_text=return_full_text,  # langchain expects the full text
-                                device_map="auto",
-                                trust_remote_code=True,
-                                max_new_tokens=2048,
-                                do_sample=True,
-                                temperature=0.01,
-                                top_p=0.95,
-                                top_k=50,
-                                repetition_penalty=repetition_penalty,
-                            )
-                            if eos_token_id != -1
-                            else pipeline(
-                                task,
-                                model=model,
-                                tokenizer=tokenizer,
-                                streamer=self.streamer,
-                                return_full_text=return_full_text,  # langchain expects the full text
-                                device_map="auto",
-                                trust_remote_code=True,
-                                max_new_tokens=2048,
-                                # verbose=True,
-                                temperature=temperature,
-                                top_p=0.95,
-                                top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-                                repetition_penalty=repetition_penalty,
-                            )
-                        )
-                    )
-                elif "dolly" in MODEL_NAME_OR_PATH:
-                    model = AutoModelForCausalLM.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        device_map=hf_pipeline_device_type,
-                        torch_dtype=torch_dtype,
-                    )
-
-                    pipe = InstructionTextGenerationPipeline(
-                        task=task,
-                        model=model,
-                        tokenizer=tokenizer,
-                        streamer=self.streamer,
-                        max_new_tokens=2048,
-                        temperature=temperature,
-                        return_full_text=True,
-                        repetition_penalty=repetition_penalty,
-                        token=token,
-                    )
-                else:
-                    if os.environ.get("DISABLE_MODEL_PRELOADING") != "true":
-                        model = (
-                            AutoModelForSeq2SeqLM.from_pretrained(
-                                MODEL_NAME_OR_PATH,
-                                config=config,
-                                trust_remote_code=True,
-                            )
-                            if is_t5
-                            else (
-                                AutoModelForCausalLM.from_pretrained(
-                                    MODEL_NAME_OR_PATH,
-                                    config=config,
-                                    trust_remote_code=True,
-                                )
-                                if "Qwen" in MODEL_NAME_OR_PATH
-                                else (
-                                    AutoModelForCausalLM.from_pretrained(
-                                        MODEL_NAME_OR_PATH,
-                                        config=config,
-                                        trust_remote_code=True,
-                                    )
-                                    if token is None
-                                    else AutoModelForCausalLM.from_pretrained(
-                                        MODEL_NAME_OR_PATH,
-                                        config=config,
-                                        trust_remote_code=True,
-                                        token=token,
-                                    )
-                                )
-                            )
-                        )
-                        print(f"Model memory footprint: {model.get_memory_footprint()}")
-                        model = model.eval()
-                        # print(f"Model memory footprint: {model.get_memory_footprint()}")
-                    else:
-                        model = MODEL_NAME_OR_PATH
-
-                    pipe = (
-                        pipeline(
-                            task,
-                            model=model,
-                            tokenizer=tokenizer,
-                            streamer=self.streamer,
-                            return_full_text=return_full_text,  # langchain expects the full text
-                            device=hf_pipeline_device_type,
-                            torch_dtype=torch_dtype,
-                            max_new_tokens=2048,
-                            trust_remote_code=True,
-                            temperature=temperature,
-                            top_p=0.95,
-                            top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-                            repetition_penalty=1.115,
-                        )
-                        if token is None
-                        else pipeline(
-                            task,
-                            model=model,
-                            tokenizer=tokenizer,
-                            streamer=self.streamer,
-                            return_full_text=return_full_text,  # langchain expects the full text
-                            device=hf_pipeline_device_type,
-                            torch_dtype=torch_dtype,
-                            max_new_tokens=2048,
-                            trust_remote_code=True,
-                            temperature=temperature,
-                            top_p=0.95,
-                            top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-                            repetition_penalty=1.115,
-                            token=token,
-                        )
-                    )
-
-                self.llm = HuggingFacePipeline(pipeline=pipe, callbacks=callbacks)
-            elif self.llm_model_type == "mosaicml":
-                MODEL_NAME_OR_PATH = os.environ.get("MOSAICML_MODEL_NAME_OR_PATH")
-                print(f"            loading model: {MODEL_NAME_OR_PATH}")
-
-                config = AutoConfig.from_pretrained(
-                    MODEL_NAME_OR_PATH, trust_remote_code=True
-                )
-                # config.attn_config["attn_impl"] = "triton"
-                config.max_seq_len = 16384 if "30b" in MODEL_NAME_OR_PATH else 4096
-                config.init_device = hf_pipeline_device_type
-
-                model = (
-                    AutoModelForCausalLM.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        config=config,
-                        quantization_config=double_quant_config,
-                        trust_remote_code=True,
-                    )
-                    if load_quantized_model is not None
-                    else AutoModelForCausalLM.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        config=config,
-                        torch_dtype=torch_dtype,
-                        trust_remote_code=True,
-                    )
-                )
-
-                print(f"Model loaded on {config.init_device}")
-                print(f"Model memory footprint: {model.get_memory_footprint()}")
-
-                tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
-                self._init_hf_streamer(tokenizer)
-
-                # mtp-7b is trained to add "<|endoftext|>" at the end of generations
-                stop_token_ids = tokenizer.convert_tokens_to_ids(["<|endoftext|>"])
-
-                # define custom stopping criteria object
-                class StopOnTokens(StoppingCriteria):
-                    def __call__(
-                        self,
-                        input_ids: torch.LongTensor,
-                        scores: torch.FloatTensor,
-                        **kwargs,
-                    ) -> bool:
-                        for stop_id in stop_token_ids:
-                            if input_ids[0][-1] == stop_id:
-                                return True
-                        return False
-
-                stopping_criteria = StoppingCriteriaList([StopOnTokens()])
-
-                max_new_tokens = 8192 if "30b" in MODEL_NAME_OR_PATH else 2048
-                self.max_tokens_limit = max_new_tokens
-                self.search_kwargs = (
-                    {"k": 8} if "30b" in MODEL_NAME_OR_PATH else self.search_kwargs
-                )
-                repetition_penalty = 1.05 if "30b" in MODEL_NAME_OR_PATH else 1.02
-
-                pipe = (
-                    pipeline(
-                        model=model,
-                        tokenizer=tokenizer,
-                        streamer=self.streamer,
-                        return_full_text=True,  # langchain expects the full text
-                        task="text-generation",
-                        device_map="auto",
-                        # we pass model parameters here too
-                        stopping_criteria=stopping_criteria,  # without this model will ramble
-                        temperature=0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
-                        top_p=0.95,  # select from top tokens whose probability add up to 15%
-                        top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-                        max_new_tokens=max_new_tokens,  # mex number of tokens to generate in the output
-                        repetition_penalty=repetition_penalty,  # without this output begins repeating
-                    )
-                    if load_quantized_model is not None
-                    else pipeline(
-                        model=model,
-                        tokenizer=tokenizer,
-                        streamer=self.streamer,
-                        return_full_text=True,  # langchain expects the full text
-                        task="text-generation",
-                        device=config.init_device,
-                        # we pass model parameters here too
-                        stopping_criteria=stopping_criteria,  # without this model will ramble
-                        temperature=0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
-                        top_p=0.95,  # select from top tokens whose probability add up to 15%
-                        top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-                        max_new_tokens=max_new_tokens,  # mex number of tokens to generate in the output
-                        repetition_penalty=repetition_penalty,  # without this output begins repeating
-                    )
-                )
-                self.llm = HuggingFacePipeline(pipeline=pipe, callbacks=callbacks)
-            elif self.llm_model_type == "stablelm":
-                MODEL_NAME_OR_PATH = os.environ.get("STABLELM_MODEL_NAME_OR_PATH")
-                print(f"            loading model: {MODEL_NAME_OR_PATH}")
-
-                config = AutoConfig.from_pretrained(
-                    MODEL_NAME_OR_PATH, trust_remote_code=True
-                )
-                # config.attn_config["attn_impl"] = "triton"
-                # config.max_seq_len = 4096
-                config.init_device = hf_pipeline_device_type
-
-                model = (
-                    AutoModelForCausalLM.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        config=config,
-                        quantization_config=double_quant_config,
-                        trust_remote_code=True,
-                    )
-                    if load_quantized_model is not None
-                    else AutoModelForCausalLM.from_pretrained(
-                        MODEL_NAME_OR_PATH,
-                        config=config,
-                        torch_dtype=torch_dtype,
-                        trust_remote_code=True,
-                    )
-                )
-
-                print(f"Model loaded on {config.init_device}")
-                print(f"Model memory footprint: {model.get_memory_footprint()}")
-
-                tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH)
-                self._init_hf_streamer(tokenizer)
-
-                class StopOnTokens(StoppingCriteria):
-                    def __call__(
-                        self,
-                        input_ids: torch.LongTensor,
-                        scores: torch.FloatTensor,
-                        **kwargs,
-                    ) -> bool:
-                        stop_ids = [50278, 50279, 50277, 1, 0]
-                        for stop_id in stop_ids:
-                            if input_ids[0][-1] == stop_id:
-                                return True
-                        return False
-
-                stopping_criteria = StoppingCriteriaList([StopOnTokens()])
-
-                pipe = (
-                    pipeline(
-                        model=model,
-                        tokenizer=tokenizer,
-                        streamer=self.streamer,
-                        return_full_text=True,  # langchain expects the full text
-                        task="text-generation",
-                        device_map="auto",
-                        # we pass model parameters here too
-                        stopping_criteria=stopping_criteria,  # without this model will ramble
-                        temperature=0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
-                        top_p=0.95,  # select from top tokens whose probability add up to 15%
-                        top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-                        max_new_tokens=2048,  # mex number of tokens to generate in the output
-                        repetition_penalty=1.25,  # without this output begins repeating
-                    )
-                    if load_quantized_model is not None
-                    else pipeline(
-                        model=model,
-                        tokenizer=tokenizer,
-                        streamer=self.streamer,
-                        return_full_text=True,  # langchain expects the full text
-                        task="text-generation",
-                        device=config.init_device,
-                        # we pass model parameters here too
-                        stopping_criteria=stopping_criteria,  # without this model will ramble
-                        temperature=0,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
-                        top_p=0.95,  # select from top tokens whose probability add up to 15%
-                        top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
-                        max_new_tokens=2048,  # mex number of tokens to generate in the output
-                        repetition_penalty=1.05,  # without this output begins repeating
-                    )
-                )
-                self.llm = HuggingFacePipeline(pipeline=pipe, callbacks=callbacks)
-
-        print("initialization complete")
diff --git a/spaces/innnky/nyaru4.0/preprocess_flist_config.py b/spaces/innnky/nyaru4.0/preprocess_flist_config.py
deleted file mode 100644
index 6e3dd0bd9390a509c282bbde4ff2631ac94404e4..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru4.0/preprocess_flist_config.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import os
-import argparse
-import re
-
-from tqdm import tqdm
-from random import shuffle
-import json
-
-config_template = json.load(open("configs/config.json"))
-
-pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
-    parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
-    parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
-    parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
-    args = parser.parse_args()
-    
-    train = []
-    val = []
-    test = []
-    idx = 0
-    spk_dict = {}
-    spk_id = 0
-    for speaker in tqdm(os.listdir(args.source_dir)):
-        spk_dict[speaker] = spk_id
-        spk_id += 1
-        wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
-        for wavpath in wavs:
-            if not pattern.match(wavpath):
-                print(f"warning：文件名{wavpath}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
-        if len(wavs) < 10:
-            print(f"warning：{speaker}数据集数量小于10条，请补充数据")
-        wavs = [i for i in wavs if i.endswith("wav")]
-        shuffle(wavs)
-        train += wavs[2:-2]
-        val += wavs[:2]
-        test += wavs[-2:]
-
-    shuffle(train)
-    shuffle(val)
-    shuffle(test)
-            
-    print("Writing", args.train_list)
-    with open(args.train_list, "w") as f:
-        for fname in tqdm(train):
-            wavpath = fname
-            f.write(wavpath + "\n")
-        
-    print("Writing", args.val_list)
-    with open(args.val_list, "w") as f:
-        for fname in tqdm(val):
-            wavpath = fname
-            f.write(wavpath + "\n")
-            
-    print("Writing", args.test_list)
-    with open(args.test_list, "w") as f:
-        for fname in tqdm(test):
-            wavpath = fname
-            f.write(wavpath + "\n")
-
-    config_template["spk"] = spk_dict
-    print("Writing configs/config.json")
-    with open("configs/config.json", "w") as f:
-        json.dump(config_template, f, indent=2)
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/720p Full Movie Download Qissa Hindi.md b/spaces/inplisQlawa/anything-midjourney-v4-1/720p Full Movie Download Qissa Hindi.md
deleted file mode 100644
index 59fa097a84752643e591a1c6c0cdec42c1911b63..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/720p Full Movie Download Qissa Hindi.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>720p full movie download Qissa hindi</h2><br /><p><b><b>DOWNLOAD</b> &#10042;&#10042;&#10042; <a href="https://urlin.us/2uEx4y">https://urlin.us/2uEx4y</a></b></p><br /><br />
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-Qissa: The Tale of a Lonely Ghost (2015) Hindi 720p WEB-DL x264 AAC 5.1CH ESubs – 900 MB. By Happy2Hub. Posted on. How To Download. 1fdad05405<br />
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-<p></p>
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Denise Milani - 2315 Pictures - 81 Complete Sets ((EXCLUSIVE)).md b/spaces/inplisQlawa/anything-midjourney-v4-1/Denise Milani - 2315 Pictures - 81 Complete Sets ((EXCLUSIVE)).md
deleted file mode 100644
index adf3fe83104e65be84c3e56eb60d10a725b3344a..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Denise Milani - 2315 Pictures - 81 Complete Sets ((EXCLUSIVE)).md	
+++ /dev/null
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-<h2>Denise Milani - 2,315 Pictures - 81 complete sets</h2><br /><p><b><b>Download</b> &#10004; <a href="https://urlin.us/2uEwfK">https://urlin.us/2uEwfK</a></b></p><br /><br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Fin De Fiesta En Los Pinos Pdf Download HOT.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Fin De Fiesta En Los Pinos Pdf Download HOT.md
deleted file mode 100644
index 40addf7611f073b72a7b2a1f04562cc044129fac..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Fin De Fiesta En Los Pinos Pdf Download HOT.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-
-<p>Fiesta (Spanish: fiesta, celebration) is a Mexican social phenomenon, the result of a mixture of Celtic tradition, a combination of popular cultural elements, and religious traditions. Fiesta is a short form of fiestă and festin, both of which mean "feast". Some festivals are religious celebrations, especially for Hispanic Catholic and Protestant immigrants. </p>
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-<p></p> 899543212b<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/Car Parking Multiplayer V4.4.4 (MOD Unlimited Money).md b/spaces/inreVtussa/clothingai/Examples/Car Parking Multiplayer V4.4.4 (MOD Unlimited Money).md
deleted file mode 100644
index f58228a1d989b795813371f89a07da39dbfb3a23..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Car Parking Multiplayer V4.4.4 (MOD Unlimited Money).md	
+++ /dev/null
@@ -1,108 +0,0 @@
-
-<h1>Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money): A Complete Guide</h1>
-<p>If you are a fan of car games and want to experience a realistic and fun parking simulation, you should try Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money). This is a modded version of the original game that offers you unlimited money and access to all the features and vehicles in the game. In this article, we will tell you what this game is, how to download and install it, and what benefits it can bring to you.</p>
-<h2>Car Parking Multiplayer v4.4.4 (MOD, Unlimited money)</h2><br /><p><b><b>Download File</b> &#9889; <a href="https://tiurll.com/2uCjDR">https://tiurll.com/2uCjDR</a></b></p><br /><br />
-<h2>What is Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money) is a modified version of Car Parking Multiplayer, a popular parking simulation game developed by olzhass. This game allows you to drive and park various cars in different scenarios and locations, such as city streets, airports, deserts, and more. You can also explore the open world with your car and interact with other players online.</p>
-<p>However, this is not an official version of the game, but a hacked one that gives you unlimited money and unlocks all the features and vehicles in the game. This means that you can enjoy the game without any limitations or restrictions. However, this also means that you might face some risks and disadvantages, such as malware infection, legal issues, lack of updates and support, and reduced functionality and quality.</p>
-<h2>How to download and install Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>If you want to download and install Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money), you can follow these steps:</p>
-<ol>
-<li>Download the APK file from one of the links provided by the search results. For example, you can use this link: https://apkdone.com/car-parking-multiplayer/.</li>
-<li>Allow your device to install apps from unknown sources by going to Settings > Security > Unknown Sources.</li>
-<li>Locate the downloaded APK file on your device and tap on it to install it.</li>
-<li>Wait for the installation process to finish and launch the game.</li>
-<li>Enjoy playing Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money) with unlimited money and all features unlocked.</li>
-</ol>
-<h2>What are the benefits of using Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>Using Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money) can bring you some benefits, such as:</p>
-<ul>
-<li>You can save money by not paying for the original game or any in-app purchases.</li>
-<li>You can access all the features and vehicles in the game without any limitations or restrictions.</li>
-<li>You can enjoy a realistic and fun parking simulation with various cars and scenarios.</li>
-<li>You can explore the open world with your car and interact with other players online.</li>
-</ul></p>
-<p></p>
-<h2>How to use Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>Once you have downloaded and installed Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money), you can use it for your parking simulation game. Here are some steps to follow:</p>
-<ol>
-<li>Launch the game and select the mode you want to play from the main menu. You can choose from Single Player, Online, Friends, or Free Mode.</li>
-<li>Select the car you want to drive from the garage. You can choose from various models and brands, such as BMW, Mercedes, Audi, Toyota, and more.</li>
-<li>Select the location you want to park your car in from the map. You can choose from different scenarios and environments, such as city streets, airports, deserts, and more.</li>
-<li>Drive your car to the designated parking spot and park it correctly without hitting any obstacles or other cars.</li>
-<li>Earn money and rewards for completing the parking tasks and challenges.</li>
-</ol>
-<h2>What are some tips and tricks for using Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>Here are some tips and tricks for using Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money) effectively:</p>
-<ul>
-<li>Use the unlimited money to upgrade and customize your car with various parts and accessories, such as body kits, spoilers, rims, tires, paint, stickers, and more.</li>
-<li>Use the camera button to switch between different views of your car and the parking spot.</li>
-<li>Use the steering wheel, pedals, gear shift, and handbrake to control your car smoothly and accurately.</li>
-<li>Use the map button to see your location and the parking spot on the map.</li>
-<li>Use the chat button to communicate and interact with other players online.</li>
-</ul>
-<h2>What are some alternatives to Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>If you are looking for some alternatives to Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money), you can try these games:</p>
-<ul>
-<li>Parking Master 3D: A realistic 3D parking simulation game that offers various cars and locations to park in.</li>
-<li>Parking Jam 3D: A casual and fun parking puzzle game that challenges you to clear the traffic jam by moving the cars out of the way.</li>
-<li>Parking King: A simple and addictive parking game that tests your skills and reflexes in parking different cars in various situations.</li>
-<li>Parking Frenzy 2.0: A popular parking game that features realistic graphics, smooth controls, and challenging levels.</li>
-</ul>
-<h2>How to uninstall Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>If you want to uninstall Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money), you can follow these steps:</p>
-<ol>
-<li>Close the game if it is running.</li>
-<li>Go to the Settings app on your device and select Apps or Applications.</li>
-<li>Find and select Car Parking Multiplayer from the list of installed apps and tap on Uninstall.</li>
-<li>Confirm your action and wait for the uninstallation process to finish.</li>
-<li>Delete the APK file from your device if you still have it.</li>
-</ol>
-<h2>How to get help and support for Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>If you need help and support for Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money), you can try these options:</p>
-<ul>
-<li>Visit the official website of olzhass at https://olzhass.com/ and check the FAQ, tutorials, manuals and guides for the game.</li>
-<li>Contact the original developers of Car Parking Multiplayer at support@olzhass.com and ask for assistance or feedback.</li>
-<li>Join the online community of Car Parking Multiplayer players at https://www.facebook.com/CarParkingMultiplayer/ and share your questions, tips, ideas and experiences with other players.</li>
-<li>Search for online forums, blogs, videos and articles that discuss Car Parking Multiplayer and learn from other players' opinions and suggestions.</li>
-</ul>
-<h2>How to troubleshoot Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>If you encounter any problems or errors while using Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money), you can try these solutions:</p>
-<ul>
-<li>Make sure that your device meets the minimum system requirements for running the game. You can check them at https://apkdone.com/car-parking-multiplayer/.</li>
-<li>Make sure that you have installed the game correctly and allowed it to access your storage and network.</li>
-<li>Make sure that you have a stable and fast internet connection to play the online mode.</li>
-<li>Make sure that you have not overloaded your device's memory or storage by running too many apps or games at the same time.</li>
-<li>Make sure that you have not corrupted or deleted any files or folders related to the game.</li>
-<li>If none of the above solutions work, you can try to clear the game's cache and data, reinstall the game, or contact the original developers of Car Parking Multiplayer at support@olzhass.com for further assistance.</li>
-</ul>
-<h2>How to optimize your parking skills with Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>If you want to optimize your parking skills with Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money), you can follow these tips:</p>
-<ul>
-<li>Use the unlimited money to buy and try different cars with different sizes and shapes, so you can learn how to park them in various situations.</li>
-<li>Use the camera button to switch between different views of your car and the parking spot, so you can see the best angles and directions to park.</li>
-<li>Use the steering wheel, pedals, gear shift, and handbrake to control your car smoothly and accurately, so you can avoid hitting any obstacles or other cars.</li>
-<li>Use the map button to see your location and the parking spot on the map, so you can plan your route and strategy to park.</li>
-<li>Use the chat button to communicate and interact with other players online, so you can ask for help, advice, or feedback on your parking skills.</li>
-</ul>
-<h2>What are some drawbacks of using Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money)?</h2>
-<p>While using Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money) can have some benefits, it also has some drawbacks that you should be aware of before using it. Here are some of them:</p>
-<ul>
-<li>You might be violating the intellectual property rights of olzhass by using a modded version of their game. This could lead to legal consequences and penalties if you are caught.</li>
-<li>You might be exposing your device to malware and viruses by downloading and installing a modded version of the game from an untrusted source. This could compromise your security and privacy and damage your device.</li>
-<li>You might be missing out on the latest updates and features of the game by using an outdated version of the game. This could affect your performance and quality and limit your options and capabilities.</li>
-<li>You might be losing the opportunity to get technical support and customer service from olzhass by using a modded version of their game. This could make it harder for you to solve any issues or problems that you might encounter while using the game.</li>
-</ul>
-<h2>How to get the original version of Car Parking Multiplayer v4.8.9.4.4?</h2>
-<p>If you want to get the original version of Car Parking Multiplayer v4.8.9.4.4, you can follow these steps:</p>
-<ol>
-<li>Visit the official website of olzhass at https://olzhass.com/ and check the information and details about the game.</li>
-<li>Visit the Google Play Store or App Store on your device and search for Car Parking Multiplayer.</li>
-<li>Select the game from the search results and tap on Install or Get.</li>
-<li>Wait for the installation process to finish and launch the game.</li>
-<li>Enjoy playing Car Parking Multiplayer v4.8.9.4.4 with its original features and content.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money) is a modded version of a popular parking simulation game that offers you unlimited money and access to all the features and vehicles in the game. In this article, we have told you what this game is, how to download and install it, and what benefits and drawbacks it can bring to you. If you want to use this game safely and legally, we recommend that you get the original product from the official website of olzhass. Alternatively, you can try some other similar games that might suit your needs and budget. We hope that this article has helped you to understand more about Car Parking Multiplayer v4.8.9.4.4 (MOD, Unlimited Money) and how to use it effectively.</p> 3cee63e6c2<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/Clave Para Activar Convertxtodvd 5 !FULL!.md b/spaces/inreVtussa/clothingai/Examples/Clave Para Activar Convertxtodvd 5 !FULL!.md
deleted file mode 100644
index 519c907a9b71280179018116372390968c114b47..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Clave Para Activar Convertxtodvd 5 !FULL!.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p>its main advantage is that it allows batch conversion and can convert multiple videos at the same time. it supports all major video, audio and subtitle formats and all most popular container files of dvd, vcd, svcd, vob, avi, mpg, mp4, wmv, mov and many more. convertxtodvd crack is user friendly and provides user-friendly design. it supports both windows platform and mac platform based applications.</p>
-<h2>clave para activar convertxtodvd 5</h2><br /><p><b><b>Download</b> &gt;&gt;&gt; <a href="https://tiurll.com/2uClQO">https://tiurll.com/2uClQO</a></b></p><br /><br />
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-<p>in just two minutes, you can burn your favorite videos to dvd for you can watch them on your tv or dvd player more precisely and easily. with this program, you can convert any videos such as your home videos, movies, youtube videos, etc. to dvd movies using with best quality setting of high definition which can make it play for a longer time.</p>
-<p>vso convertxtodvd 5 full serial number<strong></strong>provides the best and simple to use interface to help you to convert your videos easily without any problem. no need any other third party application because it has all the features to convert your videos to dvd movies using with excellent high quality setting.</p>
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diff --git a/spaces/inreVtussa/clothingai/Examples/Clearview Rc Flight Simulator Keygen.md b/spaces/inreVtussa/clothingai/Examples/Clearview Rc Flight Simulator Keygen.md
deleted file mode 100644
index 41845c2637f72886a2e0c9e3c951c80086d85077..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Clearview Rc Flight Simulator Keygen.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>clearview flight simulator rc license key code mac. clearview personal flying simulator is a windows based free fpv rc helicopter simulator for all windows operating systems. this simulator lets you fly rc helicopters in realistic flying environments with realistic flying conditions. clearview flight simulator rc license code generator. free download clearview rc flight simulator v.1.0.0 crack.clearview rc flight simulator v.2.1 crack. clearview rc flight simulator activation serial number. clearview rc flight simulator v.4 crack. clearview rc flight simulator code generator. to downioad clearview rc flight simulator account activation code, click on the download button the internet browser version you are using will be no longer supported. click on double on the registry key document to apply the repair, then click on on 'yes' install the 32bit version if you have an 32bit operation system x86dc install the 64bit patch if you have a 64bit operation system.</p>
-<p>your browser does not accept cookies. create an account at clearview to continue. clearview is a participant in the amazon services llc associates program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to amazon.com. by making any purchase through the amazon links provided, you will be supporting our efforts to create an educational, family and gender neutral virtual world.</p>
-<h2>clearview rc flight simulator keygen</h2><br /><p><b><b>Download File</b> > <a href="https://tiurll.com/2uCiRF">https://tiurll.com/2uCiRF</a></b></p><br /><br />
-<p>that is the first step to download clearview rc flight simulator crack the instructions are given for the clearview simulator, which is a virtual simulator, not a real flight simulator. download clearview rc flight simulator crack clearview rc flight simulator crack download. phoenix flight simulator 1999 (microsoft. buy clearview rc flight simulator, flight simulation, airplane control software at pc store please note the following request: _the instructions are given for the clearview simulator, which is a virtual simulator, not a real flight simulator.clean that fuses together multiple per-word cleanings into a single pass. from __future__ import absolute_import from __future__ import unicode_literals from.. import names import re import sys def _add_clean_module(mod): adds one or more words (or other symbols) to the cleaning list. names.add_to_clean_module(mod) def _do_fuse(words): reusable function to prepose arguments for cleaning. _add_clean_module(words) return words def _ensure_clean_request(words): prefixes a list of words to ensure that its cleaned correctly. _add_clean_module(words) return words def _ensure_clean_present(words): prefixes a list of words to ensure that its cleaned. # todo: clean multiline comments as well. pass _add_clean_module(words) return words def _ensure_clean_tag(words): prefixes a list of words to ensure that they are cleaned. the list of words may contain words, because whole-word syntax is supported. _add_clean_module(words) return words def _ensure_clean_string(words): prefixes a list of words to ensure that they are cleaned. words = [w for w in words] # type:</p> 899543212b<br />
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diff --git a/spaces/isabel/pug-or-cat-image-classifier/app.py b/spaces/isabel/pug-or-cat-image-classifier/app.py
deleted file mode 100644
index 9071bf46476411daecc1b0066eb99ea306f61ae1..0000000000000000000000000000000000000000
--- a/spaces/isabel/pug-or-cat-image-classifier/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-### -------------------------------- ###
-###            libraries             ###
-### -------------------------------- ###
-import gradio as gr
-import numpy as np
-import os
-from tensorflow.keras.models import load_model
-from reader import get_article
-
-
-### -------------------------------- ###
-###           model loading          ###
-### -------------------------------- ###
-model = load_model('model.h5') # single file model from colab
-
-
-## --------------------------------- ###
-###     reading: categories.txt      ###
-### -------------------------------- ###
-labels = ['please upload categories.txt' for i in range(10)] # placeholder
-
-if os.path.isfile("categories.txt"):
-    # open categories.txt in read mode
-    categories = open("categories.txt", "r")
-    labels = categories.readline().split()
-
-
-## --------------------------------- ###
-###        reading: info.txt         ###
-### -------------------------------- ###
-# borrow file reading functionality from reader.py
-info = get_article()
-
-
-### -------------------------------- ###
-###        interface creation        ###
-### -------------------------------- ###
-samples = ['pug.jpeg', 'cheetah.jpeg']
-
-def preprocess(image):
-  image = np.array(image) / 255
-  image = np.expand_dims(image, axis=0)
-  return image
-  
-def predict_image(image):
-  pred = model.predict(preprocess(image))
-  results = {}
-  for row in pred:
-    for idx, item in enumerate(row):
-      results[labels[idx]] = float(item)
-  return results
-
-# generate img input and text label output
-image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
-label = gr.outputs.Label(num_top_classes=len(labels))
-
-# generate and launch interface
-interface = gr.Interface(fn=predict_image, inputs=image, outputs=label, article=info['article'], css=info['css'], theme='default', title=info['title'], allow_flagging='never', description=info['description'], examples=samples)
-interface.launch()
\ No newline at end of file
diff --git a/spaces/jayesh95/Voice-QA/LICENSE.md b/spaces/jayesh95/Voice-QA/LICENSE.md
deleted file mode 100644
index 5b9197fc51f675f9973c2f33c5b53c46f2a43804..0000000000000000000000000000000000000000
--- a/spaces/jayesh95/Voice-QA/LICENSE.md
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Jayesh Mahapatra
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/tests/test_consistency.py b/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/tests/test_consistency.py
deleted file mode 100644
index 27d49eaae8721b7ad82d4949f2ab2606c8875d9f..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/third_party/CLIP/tests/test_consistency.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import numpy as np
-import pytest
-import torch
-from PIL import Image
-
-import clip
-
-
-@pytest.mark.parametrize("model_name", clip.available_models())
-def test_consistency(model_name):
-    device = "cpu"
-    jit_model, transform = clip.load(model_name, device=device, jit=True)
-    py_model, _ = clip.load(model_name, device=device, jit=False)
-
-    image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device)
-    text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
-
-    with torch.no_grad():
-        logits_per_image, _ = jit_model(image, text)
-        jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-
-        logits_per_image, _ = py_model(image, text)
-        py_probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-
-    assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)
diff --git a/spaces/jelly21/claudfuen-photorealistic-fuen-v1/app.py b/spaces/jelly21/claudfuen-photorealistic-fuen-v1/app.py
deleted file mode 100644
index 5d4d2195259b2df60a162907e36701a36f9420f2..0000000000000000000000000000000000000000
--- a/spaces/jelly21/claudfuen-photorealistic-fuen-v1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/claudfuen/photorealistic-fuen-v1").launch()
\ No newline at end of file
diff --git a/spaces/jhj0517/Whisper-WebUI-Easy-Subtitle-Generator/app.py b/spaces/jhj0517/Whisper-WebUI-Easy-Subtitle-Generator/app.py
deleted file mode 100644
index 362ba07370fa6dfb3181dc7c6029aec2e5ee1cc6..0000000000000000000000000000000000000000
--- a/spaces/jhj0517/Whisper-WebUI-Easy-Subtitle-Generator/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import gradio as gr
-from modules.model_Inference import WhisperInference
-from ui.htmls import CSS,MARKDOWN_TITLE,MARKDOWN_NOTE
-from modules.youtube_manager import get_ytmetas
-
-def on_change_models(model_size):
-    translatable_model = ["large","large-v1","large-v2"]
-    if model_size not in translatable_model:
-        return gr.Checkbox.update(visible=False,value=False,interactive=False)
-    else:
-        return gr.Checkbox.update(visible=True,value=False,label="Translate to English?",interactive=True) 
-
-whisper_inf = WhisperInference()
-block = gr.Blocks(css=CSS).queue(api_open=False)
-
-with block:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(MARKDOWN_TITLE,elem_id="md_project")
-            gr.Markdown(MARKDOWN_NOTE,elem_id="md_note")
-    with gr.Tabs():
-        with gr.TabItem("File"): # tab1    
-            with gr.Row():
-                input_file = gr.Files(type="file", label="Upload File here",file_types=["audio","video"])
-            with gr.Row():
-                dd_model = gr.Dropdown(choices=whisper_inf.available_models,value="tiny",label="Model")
-                dd_lang = gr.Dropdown(choices=["Automatic Detection"]+whisper_inf.available_langs,value="Automatic Detection",label="Language")
-                dd_subformat = gr.Dropdown(["SRT","WebVTT"],value="SRT",label="Subtitle Format")
-            with gr.Row():
-                cb_translate = gr.Checkbox(value=False,visible=False,label="Translate to English?",interactive=True) 
-            with gr.Row():
-                btn_run = gr.Button("GENERATE SUBTITLE FILE",variant="primary")
-            with gr.Row():
-                tb_indicator = gr.Textbox(label="Output")
-
-            btn_run.click(fn=whisper_inf.transcribe_file,inputs=[input_file,dd_model,dd_lang,dd_subformat,cb_translate],outputs=[tb_indicator])    
-            dd_model.change(fn=on_change_models,inputs=[dd_model],outputs=[cb_translate])
-        
-        with gr.TabItem("Youtube"): # tab2
-            with gr.Row():
-                tb_youtubelink = gr.Textbox(label="Youtube Link" ) 
-            with gr.Row().style(equal_height=True):
-                with gr.Column():
-                    img_thumbnail = gr.Image(label="Youtube Thumbnail")
-                with gr.Column():
-                    tb_title = gr.Label(label="Youtube Title")
-                    tb_description = gr.Textbox(label="Youtube Description",max_lines=15)
-            with gr.Row():
-                dd_model = gr.Dropdown(choices=whisper_inf.available_models,value="tiny",label="Model")
-                dd_lang = gr.Dropdown(choices=["Automatic Detection"]+whisper_inf.available_langs,value="Automatic Detection",label="Language")
-                dd_subformat = gr.Dropdown(choices=["SRT","WebVTT"],value="SRT",label="Subtitle Format")
-            with gr.Row():
-                cb_translate = gr.Checkbox(value=False,visible=False,label="Translate to English?",interactive=True) 
-            with gr.Row():
-                btn_run = gr.Button("GENERATE SUBTITLE FILE",variant="primary")
-            with gr.Row():
-                tb_indicator = gr.Textbox(label="Output")
-
-            btn_run.click(fn=whisper_inf.transcribe_youtube,inputs=[tb_youtubelink,dd_model,dd_lang,dd_subformat,cb_translate],outputs=[tb_indicator])    
-            tb_youtubelink.change(get_ytmetas,inputs=[tb_youtubelink],outputs=[img_thumbnail,tb_title,tb_description])
-            dd_model.change(fn=on_change_models,inputs=[dd_model],outputs=[cb_translate])
-
-        with gr.TabItem("Mic"): # tab3
-            with gr.Row():
-                mic_input = gr.Microphone(label="Record with Mic",type="filepath",interactive=True)
-            with gr.Row():
-                dd_model = gr.Dropdown(choices=whisper_inf.available_models,value="tiny",label="Model")
-                dd_lang = gr.Dropdown(choices=["Automatic Detection"]+whisper_inf.available_langs,value="Automatic Detection",label="Language")
-                dd_subformat = gr.Dropdown(["SRT","WebVTT"],value="SRT",label="Subtitle Format")
-            with gr.Row():
-                cb_translate = gr.Checkbox(value=False,visible=False,label="Translate to English?",interactive=True) 
-            with gr.Row():
-                btn_run = gr.Button("GENERATE SUBTITLE FILE",variant="primary")
-            with gr.Row():
-                tb_indicator = gr.Textbox(label="Output")
-
-            btn_run.click(fn=whisper_inf.transcribe_mic,inputs=[mic_input,dd_model,dd_lang,dd_subformat,cb_translate],outputs=[tb_indicator])    
-            dd_model.change(fn=on_change_models,inputs=[dd_model],outputs=[cb_translate])   
-    
-block.launch()
-
diff --git a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/transforms.py b/spaces/jimschat/VITS-Umamusume-voice-synthesizer/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/_version.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/_version.py
deleted file mode 100644
index f3455f1f1f75d1ed2b0473db9574dc9eb435f18b..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/_version.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Master version for Pillow
-__version__ = "10.0.1"
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_codecs/adobe_glyphs.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_codecs/adobe_glyphs.py
deleted file mode 100644
index 6d8f7fbdf863a5e5bb9cc2cd1940b223ad24df71..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/_codecs/adobe_glyphs.py
+++ /dev/null
@@ -1,13437 +0,0 @@
-# https://raw.githubusercontent.com/adobe-type-tools/agl-aglfn/master/glyphlist.txt
-
-# converted manually to python
-# Extended with data from GlyphNameFormatter:
-#    https://github.com/LettError/glyphNameFormatter
-
-# -----------------------------------------------------------
-# Copyright 2002-2019 Adobe (http://www.adobe.com/).
-#
-# Redistribution and use in source and binary forms, with or
-# without modification, are permitted provided that the
-# following conditions are met:
-#
-# Redistributions of source code must retain the above
-# copyright notice, this list of conditions and the following
-# disclaimer.
-#
-# Redistributions in binary form must reproduce the above
-# copyright notice, this list of conditions and the following
-# disclaimer in the documentation and/or other materials
-# provided with the distribution.
-#
-# Neither the name of Adobe nor the names of its contributors
-# may be used to endorse or promote products derived from this
-# software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
-# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
-# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
-# NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# -----------------------------------------------------------
-# Name:          Adobe Glyph List
-# Table version: 2.0
-# Date:          September 20, 2002
-# URL:           https://github.com/adobe-type-tools/agl-aglfn
-#
-# Format: two semicolon-delimited fields:
-#   (1) glyph name--upper/lowercase letters and digits
-#   (2) Unicode scalar value--four uppercase hexadecimal digits
-#
-adobe_glyphs = {
-    "/.notdef": "\u0000",
-    "/A": "\u0041",
-    "/AA": "\uA732",
-    "/AE": "\u00C6",
-    "/AEacute": "\u01FC",
-    "/AEmacron": "\u01E2",
-    "/AEsmall": "\uF7E6",
-    "/AO": "\uA734",
-    "/AU": "\uA736",
-    "/AV": "\uA738",
-    "/AVhorizontalbar": "\uA73A",
-    "/AY": "\uA73C",
-    "/Aacute": "\u00C1",
-    "/Aacutesmall": "\uF7E1",
-    "/Abreve": "\u0102",
-    "/Abreveacute": "\u1EAE",
-    "/Abrevecyr": "\u04D0",
-    "/Abrevecyrillic": "\u04D0",
-    "/Abrevedotbelow": "\u1EB6",
-    "/Abrevegrave": "\u1EB0",
-    "/Abrevehoi": "\u1EB2",
-    "/Abrevehookabove": "\u1EB2",
-    "/Abrevetilde": "\u1EB4",
-    "/Acaron": "\u01CD",
-    "/Acircle": "\u24B6",
-    "/Acircleblack": "\u1F150",
-    "/Acircumflex": "\u00C2",
-    "/Acircumflexacute": "\u1EA4",
-    "/Acircumflexdotbelow": "\u1EAC",
-    "/Acircumflexgrave": "\u1EA6",
-    "/Acircumflexhoi": "\u1EA8",
-    "/Acircumflexhookabove": "\u1EA8",
-    "/Acircumflexsmall": "\uF7E2",
-    "/Acircumflextilde": "\u1EAA",
-    "/Acute": "\uF6C9",
-    "/Acutesmall": "\uF7B4",
-    "/Acyr": "\u0410",
-    "/Acyrillic": "\u0410",
-    "/Adblgrave": "\u0200",
-    "/Adieresis": "\u00C4",
-    "/Adieresiscyr": "\u04D2",
-    "/Adieresiscyrillic": "\u04D2",
-    "/Adieresismacron": "\u01DE",
-    "/Adieresissmall": "\uF7E4",
-    "/Adot": "\u0226",
-    "/Adotbelow": "\u1EA0",
-    "/Adotmacron": "\u01E0",
-    "/Agrave": "\u00C0",
-    "/Agravedbl": "\u0200",
-    "/Agravesmall": "\uF7E0",
-    "/Ahoi": "\u1EA2",
-    "/Ahookabove": "\u1EA2",
-    "/Aiecyr": "\u04D4",
-    "/Aiecyrillic": "\u04D4",
-    "/Ainvertedbreve": "\u0202",
-    "/Akbar": "\uFDF3",
-    "/Alayhe": "\uFDF7",
-    "/Allah": "\uFDF2",
-    "/Alpha": "\u0391",
-    "/Alphaacute": "\u1FBB",
-    "/Alphaasper": "\u1F09",
-    "/Alphaasperacute": "\u1F0D",
-    "/Alphaasperacuteiotasub": "\u1F8D",
-    "/Alphaaspergrave": "\u1F0B",
-    "/Alphaaspergraveiotasub": "\u1F8B",
-    "/Alphaasperiotasub": "\u1F89",
-    "/Alphaaspertilde": "\u1F0F",
-    "/Alphaaspertildeiotasub": "\u1F8F",
-    "/Alphabreve": "\u1FB8",
-    "/Alphagrave": "\u1FBA",
-    "/Alphaiotasub": "\u1FBC",
-    "/Alphalenis": "\u1F08",
-    "/Alphalenisacute": "\u1F0C",
-    "/Alphalenisacuteiotasub": "\u1F8C",
-    "/Alphalenisgrave": "\u1F0A",
-    "/Alphalenisgraveiotasub": "\u1F8A",
-    "/Alphalenisiotasub": "\u1F88",
-    "/Alphalenistilde": "\u1F0E",
-    "/Alphalenistildeiotasub": "\u1F8E",
-    "/Alphatonos": "\u0386",
-    "/Alphawithmacron": "\u1FB9",
-    "/Amacron": "\u0100",
-    "/Amonospace": "\uFF21",
-    "/Aogonek": "\u0104",
-    "/Aparens": "\u1F110",
-    "/Aring": "\u00C5",
-    "/Aringacute": "\u01FA",
-    "/Aringbelow": "\u1E00",
-    "/Aringsmall": "\uF7E5",
-    "/Asmall": "\uF761",
-    "/Asquare": "\u1F130",
-    "/Asquareblack": "\u1F170",
-    "/Astroke": "\u023A",
-    "/Atilde": "\u00C3",
-    "/Atildesmall": "\uF7E3",
-    "/Aturned": "\u2C6F",
-    "/Ayahend": "\u06DD",
-    "/Aybarmenian": "\u0531",
-    "/B": "\u0042",
-    "/Bcircle": "\u24B7",
-    "/Bcircleblack": "\u1F151",
-    "/Bdot": "\u1E02",
-    "/Bdotaccent": "\u1E02",
-    "/Bdotbelow": "\u1E04",
-    "/Becyr": "\u0411",
-    "/Becyrillic": "\u0411",
-    "/Benarmenian": "\u0532",
-    "/Beta": "\u0392",
-    "/Bflourish": "\uA796",
-    "/Bhook": "\u0181",
-    "/BismillahArRahmanArRaheem": "\uFDFD",
-    "/Blinebelow": "\u1E06",
-    "/Bmonospace": "\uFF22",
-    "/Bparens": "\u1F111",
-    "/Brevesmall": "\uF6F4",
-    "/Bscript": "\u212C",
-    "/Bsmall": "\uF762",
-    "/Bsquare": "\u1F131",
-    "/Bsquareblack": "\u1F171",
-    "/Bstroke": "\u0243",
-    "/Btopbar": "\u0182",
-    "/C": "\u0043",
-    "/CDcircle": "\u1F12D",
-    "/Caarmenian": "\u053E",
-    "/Cacute": "\u0106",
-    "/Caron": "\uF6CA",
-    "/Caronsmall": "\uF6F5",
-    "/Cbar": "\uA792",
-    "/Ccaron": "\u010C",
-    "/Ccedilla": "\u00C7",
-    "/Ccedillaacute": "\u1E08",
-    "/Ccedillasmall": "\uF7E7",
-    "/Ccircle": "\u24B8",
-    "/Ccircleblack": "\u1F152",
-    "/Ccircumflex": "\u0108",
-    "/Cdblstruck": "\u2102",
-    "/Cdot": "\u010A",
-    "/Cdotaccent": "\u010A",
-    "/Cdotreversed": "\uA73E",
-    "/Cedillasmall": "\uF7B8",
-    "/Cfraktur": "\u212D",
-    "/Chaarmenian": "\u0549",
-    "/Cheabkhasiancyrillic": "\u04BC",
-    "/Cheabkhcyr": "\u04BC",
-    "/Cheabkhtailcyr": "\u04BE",
-    "/Checyr": "\u0427",
-    "/Checyrillic": "\u0427",
-    "/Chedescenderabkhasiancyrillic": "\u04BE",
-    "/Chedescendercyrillic": "\u04B6",
-    "/Chedieresiscyr": "\u04F4",
-    "/Chedieresiscyrillic": "\u04F4",
-    "/Cheharmenian": "\u0543",
-    "/Chekhakascyr": "\u04CB",
-    "/Chekhakassiancyrillic": "\u04CB",
-    "/Chetailcyr": "\u04B6",
-    "/Chevertcyr": "\u04B8",
-    "/Cheverticalstrokecyrillic": "\u04B8",
-    "/Chi": "\u03A7",
-    "/Chook": "\u0187",
-    "/Circumflexsmall": "\uF6F6",
-    "/Citaliccircle": "\u1F12B",
-    "/Cmonospace": "\uFF23",
-    "/Coarmenian": "\u0551",
-    "/Con": "\uA76E",
-    "/Cparens": "\u1F112",
-    "/Csmall": "\uF763",
-    "/Csquare": "\u1F132",
-    "/Csquareblack": "\u1F172",
-    "/Cstretched": "\u0297",
-    "/Cstroke": "\u023B",
-    "/Cuatrillo": "\uA72C",
-    "/Cuatrillocomma": "\uA72E",
-    "/D": "\u0044",
-    "/DZ": "\u01F1",
-    "/DZcaron": "\u01C4",
-    "/Daarmenian": "\u0534",
-    "/Dafrican": "\u0189",
-    "/Dcaron": "\u010E",
-    "/Dcedilla": "\u1E10",
-    "/Dchecyr": "\u052C",
-    "/Dcircle": "\u24B9",
-    "/Dcircleblack": "\u1F153",
-    "/Dcircumflexbelow": "\u1E12",
-    "/Dcroat": "\u0110",
-    "/Ddblstruckitalic": "\u2145",
-    "/Ddot": "\u1E0A",
-    "/Ddotaccent": "\u1E0A",
-    "/Ddotbelow": "\u1E0C",
-    "/Decyr": "\u0414",
-    "/Decyrillic": "\u0414",
-    "/Deicoptic": "\u03EE",
-    "/Dekomicyr": "\u0500",
-    "/Delta": "\u2206",
-    "/Deltagreek": "\u0394",
-    "/Dhook": "\u018A",
-    "/Dieresis": "\uF6CB",
-    "/DieresisAcute": "\uF6CC",
-    "/DieresisGrave": "\uF6CD",
-    "/Dieresissmall": "\uF7A8",
-    "/Digamma": "\u03DC",
-    "/Digammagreek": "\u03DC",
-    "/Digammapamphylian": "\u0376",
-    "/Dinsular": "\uA779",
-    "/Djecyr": "\u0402",
-    "/Djecyrillic": "\u0402",
-    "/Djekomicyr": "\u0502",
-    "/Dlinebelow": "\u1E0E",
-    "/Dmonospace": "\uFF24",
-    "/Dotaccentsmall": "\uF6F7",
-    "/Dparens": "\u1F113",
-    "/Dslash": "\u0110",
-    "/Dsmall": "\uF764",
-    "/Dsquare": "\u1F133",
-    "/Dsquareblack": "\u1F173",
-    "/Dtopbar": "\u018B",
-    "/Dz": "\u01F2",
-    "/Dzcaron": "\u01C5",
-    "/Dzeabkhasiancyrillic": "\u04E0",
-    "/Dzeabkhcyr": "\u04E0",
-    "/Dzecyr": "\u0405",
-    "/Dzecyrillic": "\u0405",
-    "/Dzhecyr": "\u040F",
-    "/Dzhecyrillic": "\u040F",
-    "/Dzjekomicyr": "\u0506",
-    "/Dzzhecyr": "\u052A",
-    "/E": "\u0045",
-    "/Eacute": "\u00C9",
-    "/Eacutesmall": "\uF7E9",
-    "/Ebreve": "\u0114",
-    "/Ecaron": "\u011A",
-    "/Ecedilla": "\u0228",
-    "/Ecedillabreve": "\u1E1C",
-    "/Echarmenian": "\u0535",
-    "/Ecircle": "\u24BA",
-    "/Ecircleblack": "\u1F154",
-    "/Ecircumflex": "\u00CA",
-    "/Ecircumflexacute": "\u1EBE",
-    "/Ecircumflexbelow": "\u1E18",
-    "/Ecircumflexdotbelow": "\u1EC6",
-    "/Ecircumflexgrave": "\u1EC0",
-    "/Ecircumflexhoi": "\u1EC2",
-    "/Ecircumflexhookabove": "\u1EC2",
-    "/Ecircumflexsmall": "\uF7EA",
-    "/Ecircumflextilde": "\u1EC4",
-    "/Ecyrillic": "\u0404",
-    "/Edblgrave": "\u0204",
-    "/Edieresis": "\u00CB",
-    "/Edieresissmall": "\uF7EB",
-    "/Edot": "\u0116",
-    "/Edotaccent": "\u0116",
-    "/Edotbelow": "\u1EB8",
-    "/Efcyr": "\u0424",
-    "/Efcyrillic": "\u0424",
-    "/Egrave": "\u00C8",
-    "/Egravedbl": "\u0204",
-    "/Egravesmall": "\uF7E8",
-    "/Egyptain": "\uA724",
-    "/Egyptalef": "\uA722",
-    "/Eharmenian": "\u0537",
-    "/Ehoi": "\u1EBA",
-    "/Ehookabove": "\u1EBA",
-    "/Eightroman": "\u2167",
-    "/Einvertedbreve": "\u0206",
-    "/Eiotifiedcyr": "\u0464",
-    "/Eiotifiedcyrillic": "\u0464",
-    "/Elcyr": "\u041B",
-    "/Elcyrillic": "\u041B",
-    "/Elevenroman": "\u216A",
-    "/Elhookcyr": "\u0512",
-    "/Elmiddlehookcyr": "\u0520",
-    "/Elsharptailcyr": "\u04C5",
-    "/Eltailcyr": "\u052E",
-    "/Emacron": "\u0112",
-    "/Emacronacute": "\u1E16",
-    "/Emacrongrave": "\u1E14",
-    "/Emcyr": "\u041C",
-    "/Emcyrillic": "\u041C",
-    "/Emonospace": "\uFF25",
-    "/Emsharptailcyr": "\u04CD",
-    "/Encyr": "\u041D",
-    "/Encyrillic": "\u041D",
-    "/Endescendercyrillic": "\u04A2",
-    "/Eng": "\u014A",
-    "/Engecyr": "\u04A4",
-    "/Enghecyrillic": "\u04A4",
-    "/Enhookcyr": "\u04C7",
-    "/Enhookcyrillic": "\u04C7",
-    "/Enhookleftcyr": "\u0528",
-    "/Enmiddlehookcyr": "\u0522",
-    "/Ensharptailcyr": "\u04C9",
-    "/Entailcyr": "\u04A2",
-    "/Eogonek": "\u0118",
-    "/Eopen": "\u0190",
-    "/Eparens": "\u1F114",
-    "/Epsilon": "\u0395",
-    "/Epsilonacute": "\u1FC9",
-    "/Epsilonasper": "\u1F19",
-    "/Epsilonasperacute": "\u1F1D",
-    "/Epsilonaspergrave": "\u1F1B",
-    "/Epsilongrave": "\u1FC8",
-    "/Epsilonlenis": "\u1F18",
-    "/Epsilonlenisacute": "\u1F1C",
-    "/Epsilonlenisgrave": "\u1F1A",
-    "/Epsilontonos": "\u0388",
-    "/Ercyr": "\u0420",
-    "/Ercyrillic": "\u0420",
-    "/Ereversed": "\u018E",
-    "/Ereversedcyr": "\u042D",
-    "/Ereversedcyrillic": "\u042D",
-    "/Ereverseddieresiscyr": "\u04EC",
-    "/Ereversedopen": "\uA7AB",
-    "/Ertickcyr": "\u048E",
-    "/Escript": "\u2130",
-    "/Escyr": "\u0421",
-    "/Escyrillic": "\u0421",
-    "/Esdescendercyrillic": "\u04AA",
-    "/Esh": "\u01A9",
-    "/Esmall": "\uF765",
-    "/Esmallturned": "\u2C7B",
-    "/Esquare": "\u1F134",
-    "/Esquareblack": "\u1F174",
-    "/Estailcyr": "\u04AA",
-    "/Estroke": "\u0246",
-    "/Et": "\uA76A",
-    "/Eta": "\u0397",
-    "/Etaacute": "\u1FCB",
-    "/Etaasper": "\u1F29",
-    "/Etaasperacute": "\u1F2D",
-    "/Etaasperacuteiotasub": "\u1F9D",
-    "/Etaaspergrave": "\u1F2B",
-    "/Etaaspergraveiotasub": "\u1F9B",
-    "/Etaasperiotasub": "\u1F99",
-    "/Etaaspertilde": "\u1F2F",
-    "/Etaaspertildeiotasub": "\u1F9F",
-    "/Etagrave": "\u1FCA",
-    "/Etaiotasub": "\u1FCC",
-    "/Etalenis": "\u1F28",
-    "/Etalenisacute": "\u1F2C",
-    "/Etalenisacuteiotasub": "\u1F9C",
-    "/Etalenisgrave": "\u1F2A",
-    "/Etalenisgraveiotasub": "\u1F9A",
-    "/Etalenisiotasub": "\u1F98",
-    "/Etalenistilde": "\u1F2E",
-    "/Etalenistildeiotasub": "\u1F9E",
-    "/Etarmenian": "\u0538",
-    "/Etatonos": "\u0389",
-    "/Eth": "\u00D0",
-    "/Ethsmall": "\uF7F0",
-    "/Etilde": "\u1EBC",
-    "/Etildebelow": "\u1E1A",
-    "/Eukrcyr": "\u0404",
-    "/Euro": "\u20AC",
-    "/Ezh": "\u01B7",
-    "/Ezhcaron": "\u01EE",
-    "/Ezhreversed": "\u01B8",
-    "/F": "\u0046",
-    "/Fcircle": "\u24BB",
-    "/Fcircleblack": "\u1F155",
-    "/Fdot": "\u1E1E",
-    "/Fdotaccent": "\u1E1E",
-    "/Feharmenian": "\u0556",
-    "/Feicoptic": "\u03E4",
-    "/Fhook": "\u0191",
-    "/Finsular": "\uA77B",
-    "/Fitacyr": "\u0472",
-    "/Fitacyrillic": "\u0472",
-    "/Fiveroman": "\u2164",
-    "/Fmonospace": "\uFF26",
-    "/Fourroman": "\u2163",
-    "/Fparens": "\u1F115",
-    "/Fscript": "\u2131",
-    "/Fsmall": "\uF766",
-    "/Fsquare": "\u1F135",
-    "/Fsquareblack": "\u1F175",
-    "/Fstroke": "\uA798",
-    "/Fturned": "\u2132",
-    "/G": "\u0047",
-    "/GBsquare": "\u3387",
-    "/Gacute": "\u01F4",
-    "/Gamma": "\u0393",
-    "/Gammaafrican": "\u0194",
-    "/Gammadblstruck": "\u213E",
-    "/Gangiacoptic": "\u03EA",
-    "/Gbreve": "\u011E",
-    "/Gcaron": "\u01E6",
-    "/Gcedilla": "\u0122",
-    "/Gcircle": "\u24BC",
-    "/Gcircleblack": "\u1F156",
-    "/Gcircumflex": "\u011C",
-    "/Gcommaaccent": "\u0122",
-    "/Gdot": "\u0120",
-    "/Gdotaccent": "\u0120",
-    "/Gecyr": "\u0413",
-    "/Gecyrillic": "\u0413",
-    "/Gehookcyr": "\u0494",
-    "/Gehookstrokecyr": "\u04FA",
-    "/Germandbls": "\u1E9E",
-    "/Gestrokecyr": "\u0492",
-    "/Getailcyr": "\u04F6",
-    "/Geupcyr": "\u0490",
-    "/Ghadarmenian": "\u0542",
-    "/Ghemiddlehookcyrillic": "\u0494",
-    "/Ghestrokecyrillic": "\u0492",
-    "/Gheupturncyrillic": "\u0490",
-    "/Ghook": "\u0193",
-    "/Ghooksmall": "\u029B",
-    "/Gimarmenian": "\u0533",
-    "/Ginsular": "\uA77D",
-    "/Ginsularturned": "\uA77E",
-    "/Gjecyr": "\u0403",
-    "/Gjecyrillic": "\u0403",
-    "/Glottalstop": "\u0241",
-    "/Gmacron": "\u1E20",
-    "/Gmonospace": "\uFF27",
-    "/Gobliquestroke": "\uA7A0",
-    "/Gparens": "\u1F116",
-    "/Grave": "\uF6CE",
-    "/Gravesmall": "\uF760",
-    "/Gsmall": "\uF767",
-    "/Gsmallhook": "\u029B",
-    "/Gsquare": "\u1F136",
-    "/Gsquareblack": "\u1F176",
-    "/Gstroke": "\u01E4",
-    "/Gturnedsans": "\u2141",
-    "/H": "\u0048",
-    "/H18533": "\u25CF",
-    "/H18543": "\u25AA",
-    "/H18551": "\u25AB",
-    "/H22073": "\u25A1",
-    "/HPsquare": "\u33CB",
-    "/HVsquare": "\u1F14A",
-    "/Haabkhasiancyrillic": "\u04A8",
-    "/Haabkhcyr": "\u04A8",
-    "/Hacyr": "\u0425",
-    "/Hadescendercyrillic": "\u04B2",
-    "/Hahookcyr": "\u04FC",
-    "/Hardcyr": "\u042A",
-    "/Hardsigncyrillic": "\u042A",
-    "/Hastrokecyr": "\u04FE",
-    "/Hbar": "\u0126",
-    "/Hbrevebelow": "\u1E2A",
-    "/Hcaron": "\u021E",
-    "/Hcedilla": "\u1E28",
-    "/Hcircle": "\u24BD",
-    "/Hcircleblack": "\u1F157",
-    "/Hcircumflex": "\u0124",
-    "/Hdblstruck": "\u210D",
-    "/Hdescender": "\u2C67",
-    "/Hdieresis": "\u1E26",
-    "/Hdot": "\u1E22",
-    "/Hdotaccent": "\u1E22",
-    "/Hdotbelow": "\u1E24",
-    "/Heng": "\uA726",
-    "/Heta": "\u0370",
-    "/Hfraktur": "\u210C",
-    "/Hgfullwidth": "\u32CC",
-    "/Hhalf": "\u2C75",
-    "/Hhook": "\uA7AA",
-    "/Hmonospace": "\uFF28",
-    "/Hoarmenian": "\u0540",
-    "/HonAA": "\u0611",
-    "/HonRA": "\u0612",
-    "/HonSAW": "\u0610",
-    "/Horicoptic": "\u03E8",
-    "/Hparens": "\u1F117",
-    "/Hscript": "\u210B",
-    "/Hsmall": "\uF768",
-    "/Hsquare": "\u1F137",
-    "/Hsquareblack": "\u1F177",
-    "/Hstrokemod": "\uA7F8",
-    "/Hturned": "\uA78D",
-    "/Hungarumlaut": "\uF6CF",
-    "/Hungarumlautsmall": "\uF6F8",
-    "/Hwair": "\u01F6",
-    "/Hzsquare": "\u3390",
-    "/I": "\u0049",
-    "/IAcyrillic": "\u042F",
-    "/ICsquareblack": "\u1F18B",
-    "/IJ": "\u0132",
-    "/IUcyrillic": "\u042E",
-    "/Iacute": "\u00CD",
-    "/Iacutesmall": "\uF7ED",
-    "/Ibreve": "\u012C",
-    "/Icaron": "\u01CF",
-    "/Icircle": "\u24BE",
-    "/Icircleblack": "\u1F158",
-    "/Icircumflex": "\u00CE",
-    "/Icircumflexsmall": "\uF7EE",
-    "/Icyr": "\u0418",
-    "/Icyrillic": "\u0406",
-    "/Idblgrave": "\u0208",
-    "/Idieresis": "\u00CF",
-    "/Idieresisacute": "\u1E2E",
-    "/Idieresiscyr": "\u04E4",
-    "/Idieresiscyrillic": "\u04E4",
-    "/Idieresissmall": "\uF7EF",
-    "/Idot": "\u0130",
-    "/Idotaccent": "\u0130",
-    "/Idotbelow": "\u1ECA",
-    "/Iebrevecyr": "\u04D6",
-    "/Iebrevecyrillic": "\u04D6",
-    "/Iecyr": "\u0415",
-    "/Iecyrillic": "\u0415",
-    "/Iegravecyr": "\u0400",
-    "/Ifraktur": "\u2111",
-    "/Igrave": "\u00CC",
-    "/Igravecyr": "\u040D",
-    "/Igravedbl": "\u0208",
-    "/Igravesmall": "\uF7EC",
-    "/Ihoi": "\u1EC8",
-    "/Ihookabove": "\u1EC8",
-    "/Iicyrillic": "\u0418",
-    "/Iinvertedbreve": "\u020A",
-    "/Iishortcyrillic": "\u0419",
-    "/Imacron": "\u012A",
-    "/Imacroncyr": "\u04E2",
-    "/Imacroncyrillic": "\u04E2",
-    "/Imonospace": "\uFF29",
-    "/Iniarmenian": "\u053B",
-    "/Iocyr": "\u0401",
-    "/Iocyrillic": "\u0401",
-    "/Iogonek": "\u012E",
-    "/Iota": "\u0399",
-    "/Iotaacute": "\u1FDB",
-    "/Iotaafrican": "\u0196",
-    "/Iotaasper": "\u1F39",
-    "/Iotaasperacute": "\u1F3D",
-    "/Iotaaspergrave": "\u1F3B",
-    "/Iotaaspertilde": "\u1F3F",
-    "/Iotabreve": "\u1FD8",
-    "/Iotadieresis": "\u03AA",
-    "/Iotagrave": "\u1FDA",
-    "/Iotalenis": "\u1F38",
-    "/Iotalenisacute": "\u1F3C",
-    "/Iotalenisgrave": "\u1F3A",
-    "/Iotalenistilde": "\u1F3E",
-    "/Iotatonos": "\u038A",
-    "/Iotawithmacron": "\u1FD9",
-    "/Iparens": "\u1F118",
-    "/Is": "\uA76C",
-    "/Iscript": "\u2110",
-    "/Ishortcyr": "\u0419",
-    "/Ishortsharptailcyr": "\u048A",
-    "/Ismall": "\uF769",
-    "/Isquare": "\u1F138",
-    "/Isquareblack": "\u1F178",
-    "/Istroke": "\u0197",
-    "/Itilde": "\u0128",
-    "/Itildebelow": "\u1E2C",
-    "/Iukrcyr": "\u0406",
-    "/Izhitsacyr": "\u0474",
-    "/Izhitsacyrillic": "\u0474",
-    "/Izhitsadblgravecyrillic": "\u0476",
-    "/Izhitsagravedblcyr": "\u0476",
-    "/J": "\u004A",
-    "/Jaarmenian": "\u0541",
-    "/Jallajalalouhou": "\uFDFB",
-    "/Jcircle": "\u24BF",
-    "/Jcircleblack": "\u1F159",
-    "/Jcircumflex": "\u0134",
-    "/Jcrossed-tail": "\uA7B2",
-    "/Jecyr": "\u0408",
-    "/Jecyrillic": "\u0408",
-    "/Jheharmenian": "\u054B",
-    "/Jmonospace": "\uFF2A",
-    "/Jparens": "\u1F119",
-    "/Jsmall": "\uF76A",
-    "/Jsquare": "\u1F139",
-    "/Jsquareblack": "\u1F179",
-    "/Jstroke": "\u0248",
-    "/K": "\u004B",
-    "/KBsquare": "\u3385",
-    "/KKsquare": "\u33CD",
-    "/KORONIS": "\u1FBD",
-    "/Kaaleutcyr": "\u051E",
-    "/Kabashkcyr": "\u04A0",
-    "/Kabashkircyrillic": "\u04A0",
-    "/Kacute": "\u1E30",
-    "/Kacyr": "\u041A",
-    "/Kacyrillic": "\u041A",
-    "/Kadescendercyrillic": "\u049A",
-    "/Kahookcyr": "\u04C3",
-    "/Kahookcyrillic": "\u04C3",
-    "/Kaisymbol": "\u03CF",
-    "/Kappa": "\u039A",
-    "/Kastrokecyr": "\u049E",
-    "/Kastrokecyrillic": "\u049E",
-    "/Katailcyr": "\u049A",
-    "/Kaverticalstrokecyr": "\u049C",
-    "/Kaverticalstrokecyrillic": "\u049C",
-    "/Kcaron": "\u01E8",
-    "/Kcedilla": "\u0136",
-    "/Kcircle": "\u24C0",
-    "/Kcircleblack": "\u1F15A",
-    "/Kcommaaccent": "\u0136",
-    "/Kdescender": "\u2C69",
-    "/Kdiagonalstroke": "\uA742",
-    "/Kdotbelow": "\u1E32",
-    "/Keharmenian": "\u0554",
-    "/Kenarmenian": "\u053F",
-    "/Khacyrillic": "\u0425",
-    "/Kheicoptic": "\u03E6",
-    "/Khook": "\u0198",
-    "/Kjecyr": "\u040C",
-    "/Kjecyrillic": "\u040C",
-    "/Klinebelow": "\u1E34",
-    "/Kmonospace": "\uFF2B",
-    "/Kobliquestroke": "\uA7A2",
-    "/Koppa": "\u03DE",
-    "/Koppaarchaic": "\u03D8",
-    "/Koppacyr": "\u0480",
-    "/Koppacyrillic": "\u0480",
-    "/Koppagreek": "\u03DE",
-    "/Kparens": "\u1F11A",
-    "/Ksicyr": "\u046E",
-    "/Ksicyrillic": "\u046E",
-    "/Ksmall": "\uF76B",
-    "/Ksquare": "\u1F13A",
-    "/Ksquareblack": "\u1F17A",
-    "/Kstroke": "\uA740",
-    "/Kstrokediagonalstroke": "\uA744",
-    "/Kturned": "\uA7B0",
-    "/L": "\u004C",
-    "/LJ": "\u01C7",
-    "/LL": "\uF6BF",
-    "/LLwelsh": "\u1EFA",
-    "/LTDfullwidth": "\u32CF",
-    "/Lacute": "\u0139",
-    "/Lambda": "\u039B",
-    "/Lbar": "\u023D",
-    "/Lbelt": "\uA7AD",
-    "/Lbroken": "\uA746",
-    "/Lcaron": "\u013D",
-    "/Lcedilla": "\u013B",
-    "/Lcircle": "\u24C1",
-    "/Lcircleblack": "\u1F15B",
-    "/Lcircumflexbelow": "\u1E3C",
-    "/Lcommaaccent": "\u013B",
-    "/Ldblbar": "\u2C60",
-    "/Ldot": "\u013F",
-    "/Ldotaccent": "\u013F",
-    "/Ldotbelow": "\u1E36",
-    "/Ldotbelowmacron": "\u1E38",
-    "/Lhacyr": "\u0514",
-    "/Liwnarmenian": "\u053C",
-    "/Lj": "\u01C8",
-    "/Ljecyr": "\u0409",
-    "/Ljecyrillic": "\u0409",
-    "/Ljekomicyr": "\u0508",
-    "/Llinebelow": "\u1E3A",
-    "/Lmacrondot": "\u1E38",
-    "/Lmiddletilde": "\u2C62",
-    "/Lmonospace": "\uFF2C",
-    "/Lparens": "\u1F11B",
-    "/Lreversedsans": "\u2143",
-    "/Lscript": "\u2112",
-    "/Lslash": "\u0141",
-    "/Lslashsmall": "\uF6F9",
-    "/Lsmall": "\uF76C",
-    "/Lsquare": "\u1F13B",
-    "/Lsquareblack": "\u1F17B",
-    "/Lstroke": "\uA748",
-    "/Lturned": "\uA780",
-    "/Lturnedsans": "\u2142",
-    "/M": "\u004D",
-    "/MBsquare": "\u3386",
-    "/MVsquare": "\u1F14B",
-    "/Macron": "\uF6D0",
-    "/Macronsmall": "\uF7AF",
-    "/Macute": "\u1E3E",
-    "/Mcircle": "\u24C2",
-    "/Mcircleblack": "\u1F15C",
-    "/Mdot": "\u1E40",
-    "/Mdotaccent": "\u1E40",
-    "/Mdotbelow": "\u1E42",
-    "/Menarmenian": "\u0544",
-    "/Mhook": "\u2C6E",
-    "/Mmonospace": "\uFF2D",
-    "/Mohammad": "\uFDF4",
-    "/Mparens": "\u1F11C",
-    "/Mscript": "\u2133",
-    "/Msmall": "\uF76D",
-    "/Msquare": "\u1F13C",
-    "/Msquareblack": "\u1F17C",
-    "/Mturned": "\u019C",
-    "/Mturnedsmall": "\uA7FA",
-    "/Mu": "\u039C",
-    "/N": "\u004E",
-    "/NJ": "\u01CA",
-    "/Nacute": "\u0143",
-    "/Ncaron": "\u0147",
-    "/Ncedilla": "\u0145",
-    "/Ncircle": "\u24C3",
-    "/Ncircleblack": "\u1F15D",
-    "/Ncircumflexbelow": "\u1E4A",
-    "/Ncommaaccent": "\u0145",
-    "/Ndblstruck": "\u2115",
-    "/Ndescender": "\uA790",
-    "/Ndot": "\u1E44",
-    "/Ndotaccent": "\u1E44",
-    "/Ndotbelow": "\u1E46",
-    "/Ngrave": "\u01F8",
-    "/Nhookleft": "\u019D",
-    "/Nineroman": "\u2168",
-    "/Nj": "\u01CB",
-    "/Njecyr": "\u040A",
-    "/Njecyrillic": "\u040A",
-    "/Njekomicyr": "\u050A",
-    "/Nlinebelow": "\u1E48",
-    "/Nlongrightleg": "\u0220",
-    "/Nmonospace": "\uFF2E",
-    "/Nobliquestroke": "\uA7A4",
-    "/Nowarmenian": "\u0546",
-    "/Nparens": "\u1F11D",
-    "/Nsmall": "\uF76E",
-    "/Nsquare": "\u1F13D",
-    "/Nsquareblack": "\u1F17D",
-    "/Ntilde": "\u00D1",
-    "/Ntildesmall": "\uF7F1",
-    "/Nu": "\u039D",
-    "/O": "\u004F",
-    "/OE": "\u0152",
-    "/OEsmall": "\uF6FA",
-    "/OO": "\uA74E",
-    "/Oacute": "\u00D3",
-    "/Oacutesmall": "\uF7F3",
-    "/Obar": "\u019F",
-    "/Obarcyr": "\u04E8",
-    "/Obardieresiscyr": "\u04EA",
-    "/Obarredcyrillic": "\u04E8",
-    "/Obarreddieresiscyrillic": "\u04EA",
-    "/Obreve": "\u014E",
-    "/Ocaron": "\u01D1",
-    "/Ocenteredtilde": "\u019F",
-    "/Ocircle": "\u24C4",
-    "/Ocircleblack": "\u1F15E",
-    "/Ocircumflex": "\u00D4",
-    "/Ocircumflexacute": "\u1ED0",
-    "/Ocircumflexdotbelow": "\u1ED8",
-    "/Ocircumflexgrave": "\u1ED2",
-    "/Ocircumflexhoi": "\u1ED4",
-    "/Ocircumflexhookabove": "\u1ED4",
-    "/Ocircumflexsmall": "\uF7F4",
-    "/Ocircumflextilde": "\u1ED6",
-    "/Ocyr": "\u041E",
-    "/Ocyrillic": "\u041E",
-    "/Odblacute": "\u0150",
-    "/Odblgrave": "\u020C",
-    "/Odieresis": "\u00D6",
-    "/Odieresiscyr": "\u04E6",
-    "/Odieresiscyrillic": "\u04E6",
-    "/Odieresismacron": "\u022A",
-    "/Odieresissmall": "\uF7F6",
-    "/Odot": "\u022E",
-    "/Odotbelow": "\u1ECC",
-    "/Odotmacron": "\u0230",
-    "/Ogoneksmall": "\uF6FB",
-    "/Ograve": "\u00D2",
-    "/Ogravedbl": "\u020C",
-    "/Ogravesmall": "\uF7F2",
-    "/Oharmenian": "\u0555",
-    "/Ohm": "\u2126",
-    "/Ohoi": "\u1ECE",
-    "/Ohookabove": "\u1ECE",
-    "/Ohorn": "\u01A0",
-    "/Ohornacute": "\u1EDA",
-    "/Ohorndotbelow": "\u1EE2",
-    "/Ohorngrave": "\u1EDC",
-    "/Ohornhoi": "\u1EDE",
-    "/Ohornhookabove": "\u1EDE",
-    "/Ohorntilde": "\u1EE0",
-    "/Ohungarumlaut": "\u0150",
-    "/Oi": "\u01A2",
-    "/Oinvertedbreve": "\u020E",
-    "/Oloop": "\uA74C",
-    "/Omacron": "\u014C",
-    "/Omacronacute": "\u1E52",
-    "/Omacrongrave": "\u1E50",
-    "/Omega": "\u2126",
-    "/Omegaacute": "\u1FFB",
-    "/Omegaasper": "\u1F69",
-    "/Omegaasperacute": "\u1F6D",
-    "/Omegaasperacuteiotasub": "\u1FAD",
-    "/Omegaaspergrave": "\u1F6B",
-    "/Omegaaspergraveiotasub": "\u1FAB",
-    "/Omegaasperiotasub": "\u1FA9",
-    "/Omegaaspertilde": "\u1F6F",
-    "/Omegaaspertildeiotasub": "\u1FAF",
-    "/Omegacyr": "\u0460",
-    "/Omegacyrillic": "\u0460",
-    "/Omegagrave": "\u1FFA",
-    "/Omegagreek": "\u03A9",
-    "/Omegaiotasub": "\u1FFC",
-    "/Omegalenis": "\u1F68",
-    "/Omegalenisacute": "\u1F6C",
-    "/Omegalenisacuteiotasub": "\u1FAC",
-    "/Omegalenisgrave": "\u1F6A",
-    "/Omegalenisgraveiotasub": "\u1FAA",
-    "/Omegalenisiotasub": "\u1FA8",
-    "/Omegalenistilde": "\u1F6E",
-    "/Omegalenistildeiotasub": "\u1FAE",
-    "/Omegaroundcyr": "\u047A",
-    "/Omegaroundcyrillic": "\u047A",
-    "/Omegatitlocyr": "\u047C",
-    "/Omegatitlocyrillic": "\u047C",
-    "/Omegatonos": "\u038F",
-    "/Omicron": "\u039F",
-    "/Omicronacute": "\u1FF9",
-    "/Omicronasper": "\u1F49",
-    "/Omicronasperacute": "\u1F4D",
-    "/Omicronaspergrave": "\u1F4B",
-    "/Omicrongrave": "\u1FF8",
-    "/Omicronlenis": "\u1F48",
-    "/Omicronlenisacute": "\u1F4C",
-    "/Omicronlenisgrave": "\u1F4A",
-    "/Omicrontonos": "\u038C",
-    "/Omonospace": "\uFF2F",
-    "/Oneroman": "\u2160",
-    "/Oogonek": "\u01EA",
-    "/Oogonekmacron": "\u01EC",
-    "/Oopen": "\u0186",
-    "/Oparens": "\u1F11E",
-    "/Oslash": "\u00D8",
-    "/Oslashacute": "\u01FE",
-    "/Oslashsmall": "\uF7F8",
-    "/Osmall": "\uF76F",
-    "/Osquare": "\u1F13E",
-    "/Osquareblack": "\u1F17E",
-    "/Ostroke": "\uA74A",
-    "/Ostrokeacute": "\u01FE",
-    "/Otcyr": "\u047E",
-    "/Otcyrillic": "\u047E",
-    "/Otilde": "\u00D5",
-    "/Otildeacute": "\u1E4C",
-    "/Otildedieresis": "\u1E4E",
-    "/Otildemacron": "\u022C",
-    "/Otildesmall": "\uF7F5",
-    "/Ou": "\u0222",
-    "/P": "\u0050",
-    "/PAsquareblack": "\u1F18C",
-    "/PPVsquare": "\u1F14E",
-    "/Pacute": "\u1E54",
-    "/Palochkacyr": "\u04C0",
-    "/Pcircle": "\u24C5",
-    "/Pcircleblack": "\u1F15F",
-    "/Pcrosssquareblack": "\u1F18A",
-    "/Pdblstruck": "\u2119",
-    "/Pdot": "\u1E56",
-    "/Pdotaccent": "\u1E56",
-    "/Pecyr": "\u041F",
-    "/Pecyrillic": "\u041F",
-    "/Peharmenian": "\u054A",
-    "/Pehookcyr": "\u04A6",
-    "/Pemiddlehookcyrillic": "\u04A6",
-    "/Petailcyr": "\u0524",
-    "/Pflourish": "\uA752",
-    "/Phi": "\u03A6",
-    "/Phook": "\u01A4",
-    "/Pi": "\u03A0",
-    "/Pidblstruck": "\u213F",
-    "/Piwrarmenian": "\u0553",
-    "/Pmonospace": "\uFF30",
-    "/Pparens": "\u1F11F",
-    "/Psi": "\u03A8",
-    "/Psicyr": "\u0470",
-    "/Psicyrillic": "\u0470",
-    "/Psmall": "\uF770",
-    "/Psquare": "\u1F13F",
-    "/Psquareblack": "\u1F17F",
-    "/Pstroke": "\u2C63",
-    "/Pstrokedescender": "\uA750",
-    "/Ptail": "\uA754",
-    "/Q": "\u0051",
-    "/Qacyr": "\u051A",
-    "/QalaUsedAsKoranicStopSign": "\uFDF1",
-    "/Qcircle": "\u24C6",
-    "/Qcircleblack": "\u1F160",
-    "/Qdblstruck": "\u211A",
-    "/Qdiagonalstroke": "\uA758",
-    "/Qmonospace": "\uFF31",
-    "/Qparens": "\u1F120",
-    "/Qrotated": "\u213A",
-    "/Qsmall": "\uF771",
-    "/Qsmallhooktail": "\u024A",
-    "/Qsquare": "\u1F140",
-    "/Qsquareblack": "\u1F180",
-    "/Qstrokedescender": "\uA756",
-    "/R": "\u0052",
-    "/Raarmenian": "\u054C",
-    "/Racute": "\u0154",
-    "/Rasoul": "\uFDF6",
-    "/Rcaron": "\u0158",
-    "/Rcedilla": "\u0156",
-    "/Rcircle": "\u24C7",
-    "/Rcircleblack": "\u1F161",
-    "/Rcommaaccent": "\u0156",
-    "/Rdblgrave": "\u0210",
-    "/Rdblstruck": "\u211D",
-    "/Rdot": "\u1E58",
-    "/Rdotaccent": "\u1E58",
-    "/Rdotbelow": "\u1E5A",
-    "/Rdotbelowmacron": "\u1E5C",
-    "/Reharmenian": "\u0550",
-    "/Reverseddottedsigmalunatesymbol": "\u03FF",
-    "/Reversedzecyr": "\u0510",
-    "/Rfraktur": "\u211C",
-    "/Rgravedbl": "\u0210",
-    "/Rhacyr": "\u0516",
-    "/Rho": "\u03A1",
-    "/Rhoasper": "\u1FEC",
-    "/Ringsmall": "\uF6FC",
-    "/Rinsular": "\uA782",
-    "/Rinvertedbreve": "\u0212",
-    "/Rinvertedsmall": "\u0281",
-    "/Ritaliccircle": "\u1F12C",
-    "/Rlinebelow": "\u1E5E",
-    "/Rmacrondot": "\u1E5C",
-    "/Rmonospace": "\uFF32",
-    "/Robliquestroke": "\uA7A6",
-    "/Rparens": "\u1F121",
-    "/Rrotunda": "\uA75A",
-    "/Rscript": "\u211B",
-    "/Rsmall": "\uF772",
-    "/Rsmallinverted": "\u0281",
-    "/Rsmallinvertedsuperior": "\u02B6",
-    "/Rsquare": "\u1F141",
-    "/Rsquareblack": "\u1F181",
-    "/Rstroke": "\u024C",
-    "/Rsupinvertedmod": "\u02B6",
-    "/Rtail": "\u2C64",
-    "/RubElHizbstart": "\u06DE",
-    "/Rumrotunda": "\uA75C",
-    "/Rumsmall": "\uA776",
-    "/S": "\u0053",
-    "/SAsquareblack": "\u1F18D",
-    "/SDsquare": "\u1F14C",
-    "/SF010000": "\u250C",
-    "/SF020000": "\u2514",
-    "/SF030000": "\u2510",
-    "/SF040000": "\u2518",
-    "/SF050000": "\u253C",
-    "/SF060000": "\u252C",
-    "/SF070000": "\u2534",
-    "/SF080000": "\u251C",
-    "/SF090000": "\u2524",
-    "/SF100000": "\u2500",
-    "/SF110000": "\u2502",
-    "/SF190000": "\u2561",
-    "/SF200000": "\u2562",
-    "/SF210000": "\u2556",
-    "/SF220000": "\u2555",
-    "/SF230000": "\u2563",
-    "/SF240000": "\u2551",
-    "/SF250000": "\u2557",
-    "/SF260000": "\u255D",
-    "/SF270000": "\u255C",
-    "/SF280000": "\u255B",
-    "/SF360000": "\u255E",
-    "/SF370000": "\u255F",
-    "/SF380000": "\u255A",
-    "/SF390000": "\u2554",
-    "/SF400000": "\u2569",
-    "/SF410000": "\u2566",
-    "/SF420000": "\u2560",
-    "/SF430000": "\u2550",
-    "/SF440000": "\u256C",
-    "/SF450000": "\u2567",
-    "/SF460000": "\u2568",
-    "/SF470000": "\u2564",
-    "/SF480000": "\u2565",
-    "/SF490000": "\u2559",
-    "/SF500000": "\u2558",
-    "/SF510000": "\u2552",
-    "/SF520000": "\u2553",
-    "/SF530000": "\u256B",
-    "/SF540000": "\u256A",
-    "/SSsquare": "\u1F14D",
-    "/Sacute": "\u015A",
-    "/Sacutedotaccent": "\u1E64",
-    "/Safha": "\u0603",
-    "/Sajdah": "\u06E9",
-    "/Salam": "\uFDF5",
-    "/Salla": "\uFDF9",
-    "/SallaUsedAsKoranicStopSign": "\uFDF0",
-    "/SallallahouAlayheWasallam": "\uFDFA",
-    "/Saltillo": "\uA78B",
-    "/Sampi": "\u03E0",
-    "/Sampiarchaic": "\u0372",
-    "/Sampigreek": "\u03E0",
-    "/San": "\u03FA",
-    "/Sanah": "\u0601",
-    "/Scaron": "\u0160",
-    "/Scarondot": "\u1E66",
-    "/Scarondotaccent": "\u1E66",
-    "/Scaronsmall": "\uF6FD",
-    "/Scedilla": "\u015E",
-    "/Schwa": "\u018F",
-    "/Schwacyr": "\u04D8",
-    "/Schwacyrillic": "\u04D8",
-    "/Schwadieresiscyr": "\u04DA",
-    "/Schwadieresiscyrillic": "\u04DA",
-    "/Scircle": "\u24C8",
-    "/Scircleblack": "\u1F162",
-    "/Scircumflex": "\u015C",
-    "/Scommaaccent": "\u0218",
-    "/Scriptg": "\uA7AC",
-    "/Sdot": "\u1E60",
-    "/Sdotaccent": "\u1E60",
-    "/Sdotbelow": "\u1E62",
-    "/Sdotbelowdotabove": "\u1E68",
-    "/Sdotbelowdotaccent": "\u1E68",
-    "/Seharmenian": "\u054D",
-    "/Semisoftcyr": "\u048C",
-    "/Sevenroman": "\u2166",
-    "/Shaarmenian": "\u0547",
-    "/Shacyr": "\u0428",
-    "/Shacyrillic": "\u0428",
-    "/Shchacyr": "\u0429",
-    "/Shchacyrillic": "\u0429",
-    "/Sheicoptic": "\u03E2",
-    "/SheneGerishin:hb": "\u059E",
-    "/Shhacyr": "\u04BA",
-    "/Shhacyrillic": "\u04BA",
-    "/Shhatailcyr": "\u0526",
-    "/Shimacoptic": "\u03EC",
-    "/Sho": "\u03F7",
-    "/Sigma": "\u03A3",
-    "/Sigmalunatesymbol": "\u03F9",
-    "/Sigmalunatesymboldotted": "\u03FE",
-    "/Sigmareversedlunatesymbol": "\u03FD",
-    "/Sinsular": "\uA784",
-    "/Sixroman": "\u2165",
-    "/Sjekomicyr": "\u050C",
-    "/Smonospace": "\uFF33",
-    "/Sobliquestroke": "\uA7A8",
-    "/Softcyr": "\u042C",
-    "/Softsigncyrillic": "\u042C",
-    "/Sparens": "\u1F122",
-    "/Sshell": "\u1F12A",
-    "/Ssmall": "\uF773",
-    "/Ssquare": "\u1F142",
-    "/Ssquareblack": "\u1F182",
-    "/Sswashtail": "\u2C7E",
-    "/Stigma": "\u03DA",
-    "/Stigmagreek": "\u03DA",
-    "/T": "\u0054",
-    "/Tau": "\u03A4",
-    "/Tbar": "\u0166",
-    "/Tcaron": "\u0164",
-    "/Tcedilla": "\u0162",
-    "/Tcircle": "\u24C9",
-    "/Tcircleblack": "\u1F163",
-    "/Tcircumflexbelow": "\u1E70",
-    "/Tcommaaccent": "\u0162",
-    "/Tdot": "\u1E6A",
-    "/Tdotaccent": "\u1E6A",
-    "/Tdotbelow": "\u1E6C",
-    "/Tecyr": "\u0422",
-    "/Tecyrillic": "\u0422",
-    "/Tedescendercyrillic": "\u04AC",
-    "/Tenroman": "\u2169",
-    "/Tetailcyr": "\u04AC",
-    "/Tetsecyr": "\u04B4",
-    "/Tetsecyrillic": "\u04B4",
-    "/Theta": "\u0398",
-    "/Thetasymbol": "\u03F4",
-    "/Thook": "\u01AC",
-    "/Thorn": "\u00DE",
-    "/Thornsmall": "\uF7FE",
-    "/Thornstroke": "\uA764",
-    "/Thornstrokedescender": "\uA766",
-    "/Threeroman": "\u2162",
-    "/Tildesmall": "\uF6FE",
-    "/Tinsular": "\uA786",
-    "/Tiwnarmenian": "\u054F",
-    "/Tjekomicyr": "\u050E",
-    "/Tlinebelow": "\u1E6E",
-    "/Tmonospace": "\uFF34",
-    "/Toarmenian": "\u0539",
-    "/Tonefive": "\u01BC",
-    "/Tonesix": "\u0184",
-    "/Tonetwo": "\u01A7",
-    "/Tparens": "\u1F123",
-    "/Tresillo": "\uA72A",
-    "/Tretroflexhook": "\u01AE",
-    "/Tsecyr": "\u0426",
-    "/Tsecyrillic": "\u0426",
-    "/Tshecyr": "\u040B",
-    "/Tshecyrillic": "\u040B",
-    "/Tsmall": "\uF774",
-    "/Tsquare": "\u1F143",
-    "/Tsquareblack": "\u1F183",
-    "/Tturned": "\uA7B1",
-    "/Twelveroman": "\u216B",
-    "/Twithdiagonalstroke": "\u023E",
-    "/Tworoman": "\u2161",
-    "/Tz": "\uA728",
-    "/U": "\u0055",
-    "/Uacute": "\u00DA",
-    "/Uacutedblcyr": "\u04F2",
-    "/Uacutesmall": "\uF7FA",
-    "/Ubar": "\u0244",
-    "/Ubreve": "\u016C",
-    "/Ucaron": "\u01D3",
-    "/Ucircle": "\u24CA",
-    "/Ucircleblack": "\u1F164",
-    "/Ucircumflex": "\u00DB",
-    "/Ucircumflexbelow": "\u1E76",
-    "/Ucircumflexsmall": "\uF7FB",
-    "/Ucyr": "\u0423",
-    "/Ucyrillic": "\u0423",
-    "/Udblacute": "\u0170",
-    "/Udblgrave": "\u0214",
-    "/Udieresis": "\u00DC",
-    "/Udieresisacute": "\u01D7",
-    "/Udieresisbelow": "\u1E72",
-    "/Udieresiscaron": "\u01D9",
-    "/Udieresiscyr": "\u04F0",
-    "/Udieresiscyrillic": "\u04F0",
-    "/Udieresisgrave": "\u01DB",
-    "/Udieresismacron": "\u01D5",
-    "/Udieresissmall": "\uF7FC",
-    "/Udotbelow": "\u1EE4",
-    "/Ugrave": "\u00D9",
-    "/Ugravedbl": "\u0214",
-    "/Ugravesmall": "\uF7F9",
-    "/Uhoi": "\u1EE6",
-    "/Uhookabove": "\u1EE6",
-    "/Uhorn": "\u01AF",
-    "/Uhornacute": "\u1EE8",
-    "/Uhorndotbelow": "\u1EF0",
-    "/Uhorngrave": "\u1EEA",
-    "/Uhornhoi": "\u1EEC",
-    "/Uhornhookabove": "\u1EEC",
-    "/Uhorntilde": "\u1EEE",
-    "/Uhungarumlaut": "\u0170",
-    "/Uhungarumlautcyrillic": "\u04F2",
-    "/Uinvertedbreve": "\u0216",
-    "/Ukcyr": "\u0478",
-    "/Ukcyrillic": "\u0478",
-    "/Umacron": "\u016A",
-    "/Umacroncyr": "\u04EE",
-    "/Umacroncyrillic": "\u04EE",
-    "/Umacrondieresis": "\u1E7A",
-    "/Umonospace": "\uFF35",
-    "/Uogonek": "\u0172",
-    "/Uparens": "\u1F124",
-    "/Upsilon": "\u03A5",
-    "/Upsilon1": "\u03D2",
-    "/Upsilonacute": "\u1FEB",
-    "/Upsilonacutehooksymbol": "\u03D3",
-    "/Upsilonacutehooksymbolgreek": "\u03D3",
-    "/Upsilonadieresishooksymbol": "\u03D4",
-    "/Upsilonafrican": "\u01B1",
-    "/Upsilonasper": "\u1F59",
-    "/Upsilonasperacute": "\u1F5D",
-    "/Upsilonaspergrave": "\u1F5B",
-    "/Upsilonaspertilde": "\u1F5F",
-    "/Upsilonbreve": "\u1FE8",
-    "/Upsilondieresis": "\u03AB",
-    "/Upsilondieresishooksymbolgreek": "\u03D4",
-    "/Upsilongrave": "\u1FEA",
-    "/Upsilonhooksymbol": "\u03D2",
-    "/Upsilontonos": "\u038E",
-    "/Upsilonwithmacron": "\u1FE9",
-    "/Uring": "\u016E",
-    "/Ushortcyr": "\u040E",
-    "/Ushortcyrillic": "\u040E",
-    "/Usmall": "\uF775",
-    "/Usquare": "\u1F144",
-    "/Usquareblack": "\u1F184",
-    "/Ustraightcyr": "\u04AE",
-    "/Ustraightcyrillic": "\u04AE",
-    "/Ustraightstrokecyr": "\u04B0",
-    "/Ustraightstrokecyrillic": "\u04B0",
-    "/Utilde": "\u0168",
-    "/Utildeacute": "\u1E78",
-    "/Utildebelow": "\u1E74",
-    "/V": "\u0056",
-    "/Vcircle": "\u24CB",
-    "/Vcircleblack": "\u1F165",
-    "/Vdiagonalstroke": "\uA75E",
-    "/Vdotbelow": "\u1E7E",
-    "/Vecyr": "\u0412",
-    "/Vecyrillic": "\u0412",
-    "/Vend": "\uA768",
-    "/Vewarmenian": "\u054E",
-    "/Vhook": "\u01B2",
-    "/Visigothicz": "\uA762",
-    "/Vmod": "\u2C7D",
-    "/Vmonospace": "\uFF36",
-    "/Voarmenian": "\u0548",
-    "/Volapukae": "\uA79A",
-    "/Volapukoe": "\uA79C",
-    "/Volapukue": "\uA79E",
-    "/Vparens": "\u1F125",
-    "/Vsmall": "\uF776",
-    "/Vsquare": "\u1F145",
-    "/Vsquareblack": "\u1F185",
-    "/Vtilde": "\u1E7C",
-    "/Vturned": "\u0245",
-    "/Vwelsh": "\u1EFC",
-    "/Vy": "\uA760",
-    "/W": "\u0057",
-    "/WZcircle": "\u1F12E",
-    "/Wacute": "\u1E82",
-    "/Wasallam": "\uFDF8",
-    "/Wcircle": "\u24CC",
-    "/Wcircleblack": "\u1F166",
-    "/Wcircumflex": "\u0174",
-    "/Wdieresis": "\u1E84",
-    "/Wdot": "\u1E86",
-    "/Wdotaccent": "\u1E86",
-    "/Wdotbelow": "\u1E88",
-    "/Wecyr": "\u051C",
-    "/Wgrave": "\u1E80",
-    "/Whook": "\u2C72",
-    "/Wmonospace": "\uFF37",
-    "/Wparens": "\u1F126",
-    "/Wsmall": "\uF777",
-    "/Wsquare": "\u1F146",
-    "/Wsquareblack": "\u1F186",
-    "/Wynn": "\u01F7",
-    "/X": "\u0058",
-    "/Xatailcyr": "\u04B2",
-    "/Xcircle": "\u24CD",
-    "/Xcircleblack": "\u1F167",
-    "/Xdieresis": "\u1E8C",
-    "/Xdot": "\u1E8A",
-    "/Xdotaccent": "\u1E8A",
-    "/Xeharmenian": "\u053D",
-    "/Xi": "\u039E",
-    "/Xmonospace": "\uFF38",
-    "/Xparens": "\u1F127",
-    "/Xsmall": "\uF778",
-    "/Xsquare": "\u1F147",
-    "/Xsquareblack": "\u1F187",
-    "/Y": "\u0059",
-    "/Yacute": "\u00DD",
-    "/Yacutesmall": "\uF7FD",
-    "/Yacyr": "\u042F",
-    "/Yaecyr": "\u0518",
-    "/Yatcyr": "\u0462",
-    "/Yatcyrillic": "\u0462",
-    "/Ycircle": "\u24CE",
-    "/Ycircleblack": "\u1F168",
-    "/Ycircumflex": "\u0176",
-    "/Ydieresis": "\u0178",
-    "/Ydieresissmall": "\uF7FF",
-    "/Ydot": "\u1E8E",
-    "/Ydotaccent": "\u1E8E",
-    "/Ydotbelow": "\u1EF4",
-    "/Yericyrillic": "\u042B",
-    "/Yerudieresiscyrillic": "\u04F8",
-    "/Ygrave": "\u1EF2",
-    "/Yhoi": "\u1EF6",
-    "/Yhook": "\u01B3",
-    "/Yhookabove": "\u1EF6",
-    "/Yiarmenian": "\u0545",
-    "/Yicyrillic": "\u0407",
-    "/Yiwnarmenian": "\u0552",
-    "/Ylongcyr": "\u042B",
-    "/Ylongdieresiscyr": "\u04F8",
-    "/Yloop": "\u1EFE",
-    "/Ymacron": "\u0232",
-    "/Ymonospace": "\uFF39",
-    "/Yogh": "\u021C",
-    "/Yot": "\u037F",
-    "/Yparens": "\u1F128",
-    "/Ysmall": "\uF779",
-    "/Ysquare": "\u1F148",
-    "/Ysquareblack": "\u1F188",
-    "/Ystroke": "\u024E",
-    "/Ytilde": "\u1EF8",
-    "/Yturnedsans": "\u2144",
-    "/Yucyr": "\u042E",
-    "/Yukrcyr": "\u0407",
-    "/Yusbigcyr": "\u046A",
-    "/Yusbigcyrillic": "\u046A",
-    "/Yusbigiotifiedcyr": "\u046C",
-    "/Yusbigiotifiedcyrillic": "\u046C",
-    "/Yuslittlecyr": "\u0466",
-    "/Yuslittlecyrillic": "\u0466",
-    "/Yuslittleiotifiedcyr": "\u0468",
-    "/Yuslittleiotifiedcyrillic": "\u0468",
-    "/Z": "\u005A",
-    "/Zaarmenian": "\u0536",
-    "/Zacute": "\u0179",
-    "/Zcaron": "\u017D",
-    "/Zcaronsmall": "\uF6FF",
-    "/Zcircle": "\u24CF",
-    "/Zcircleblack": "\u1F169",
-    "/Zcircumflex": "\u1E90",
-    "/Zdblstruck": "\u2124",
-    "/Zdescender": "\u2C6B",
-    "/Zdot": "\u017B",
-    "/Zdotaccent": "\u017B",
-    "/Zdotbelow": "\u1E92",
-    "/Zecyr": "\u0417",
-    "/Zecyrillic": "\u0417",
-    "/Zedescendercyrillic": "\u0498",
-    "/Zedieresiscyr": "\u04DE",
-    "/Zedieresiscyrillic": "\u04DE",
-    "/Zeta": "\u0396",
-    "/Zetailcyr": "\u0498",
-    "/Zfraktur": "\u2128",
-    "/Zhearmenian": "\u053A",
-    "/Zhebrevecyr": "\u04C1",
-    "/Zhebrevecyrillic": "\u04C1",
-    "/Zhecyr": "\u0416",
-    "/Zhecyrillic": "\u0416",
-    "/Zhedescendercyrillic": "\u0496",
-    "/Zhedieresiscyr": "\u04DC",
-    "/Zhedieresiscyrillic": "\u04DC",
-    "/Zhetailcyr": "\u0496",
-    "/Zhook": "\u0224",
-    "/Zjekomicyr": "\u0504",
-    "/Zlinebelow": "\u1E94",
-    "/Zmonospace": "\uFF3A",
-    "/Zparens": "\u1F129",
-    "/Zsmall": "\uF77A",
-    "/Zsquare": "\u1F149",
-    "/Zsquareblack": "\u1F189",
-    "/Zstroke": "\u01B5",
-    "/Zswashtail": "\u2C7F",
-    "/a": "\u0061",
-    "/a.inferior": "\u2090",
-    "/aHonRAA": "\u0613",
-    "/aa": "\uA733",
-    "/aabengali": "\u0986",
-    "/aacute": "\u00E1",
-    "/aadeva": "\u0906",
-    "/aagujarati": "\u0A86",
-    "/aagurmukhi": "\u0A06",
-    "/aamatragurmukhi": "\u0A3E",
-    "/aarusquare": "\u3303",
-    "/aavowelsignbengali": "\u09BE",
-    "/aavowelsigndeva": "\u093E",
-    "/aavowelsigngujarati": "\u0ABE",
-    "/abbreviationmarkarmenian": "\u055F",
-    "/abbreviationsigndeva": "\u0970",
-    "/abengali": "\u0985",
-    "/abopomofo": "\u311A",
-    "/abreve": "\u0103",
-    "/abreveacute": "\u1EAF",
-    "/abrevecyr": "\u04D1",
-    "/abrevecyrillic": "\u04D1",
-    "/abrevedotbelow": "\u1EB7",
-    "/abrevegrave": "\u1EB1",
-    "/abrevehoi": "\u1EB3",
-    "/abrevehookabove": "\u1EB3",
-    "/abrevetilde": "\u1EB5",
-    "/absquareblack": "\u1F18E",
-    "/acaron": "\u01CE",
-    "/accountof": "\u2100",
-    "/accurrent": "\u23E6",
-    "/acircle": "\u24D0",
-    "/acirclekatakana": "\u32D0",
-    "/acircumflex": "\u00E2",
-    "/acircumflexacute": "\u1EA5",
-    "/acircumflexdotbelow": "\u1EAD",
-    "/acircumflexgrave": "\u1EA7",
-    "/acircumflexhoi": "\u1EA9",
-    "/acircumflexhookabove": "\u1EA9",
-    "/acircumflextilde": "\u1EAB",
-    "/activatearabicformshaping": "\u206D",
-    "/activatesymmetricswapping": "\u206B",
-    "/acute": "\u00B4",
-    "/acutebelowcmb": "\u0317",
-    "/acutecmb": "\u0301",
-    "/acutecomb": "\u0301",
-    "/acutedblmiddlemod": "\u02F6",
-    "/acutedeva": "\u0954",
-    "/acutelowmod": "\u02CF",
-    "/acutemod": "\u02CA",
-    "/acutetonecmb": "\u0341",
-    "/acyr": "\u0430",
-    "/acyrillic": "\u0430",
-    "/adblgrave": "\u0201",
-    "/addakgurmukhi": "\u0A71",
-    "/addressedsubject": "\u2101",
-    "/adegadegpada": "\uA9CB",
-    "/adegpada": "\uA9CA",
-    "/adeva": "\u0905",
-    "/adieresis": "\u00E4",
-    "/adieresiscyr": "\u04D3",
-    "/adieresiscyrillic": "\u04D3",
-    "/adieresismacron": "\u01DF",
-    "/adishakti": "\u262C",
-    "/admissionTickets": "\u1F39F",
-    "/adot": "\u0227",
-    "/adotbelow": "\u1EA1",
-    "/adotmacron": "\u01E1",
-    "/ae": "\u00E6",
-    "/aeacute": "\u01FD",
-    "/aekorean": "\u3150",
-    "/aemacron": "\u01E3",
-    "/aerialTramway": "\u1F6A1",
-    "/afghani": "\u060B",
-    "/afii00208": "\u2015",
-    "/afii08941": "\u20A4",
-    "/afii10017": "\u0410",
-    "/afii10018": "\u0411",
-    "/afii10019": "\u0412",
-    "/afii10020": "\u0413",
-    "/afii10021": "\u0414",
-    "/afii10022": "\u0415",
-    "/afii10023": "\u0401",
-    "/afii10024": "\u0416",
-    "/afii10025": "\u0417",
-    "/afii10026": "\u0418",
-    "/afii10027": "\u0419",
-    "/afii10028": "\u041A",
-    "/afii10029": "\u041B",
-    "/afii10030": "\u041C",
-    "/afii10031": "\u041D",
-    "/afii10032": "\u041E",
-    "/afii10033": "\u041F",
-    "/afii10034": "\u0420",
-    "/afii10035": "\u0421",
-    "/afii10036": "\u0422",
-    "/afii10037": "\u0423",
-    "/afii10038": "\u0424",
-    "/afii10039": "\u0425",
-    "/afii10040": "\u0426",
-    "/afii10041": "\u0427",
-    "/afii10042": "\u0428",
-    "/afii10043": "\u0429",
-    "/afii10044": "\u042A",
-    "/afii10045": "\u042B",
-    "/afii10046": "\u042C",
-    "/afii10047": "\u042D",
-    "/afii10048": "\u042E",
-    "/afii10049": "\u042F",
-    "/afii10050": "\u0490",
-    "/afii10051": "\u0402",
-    "/afii10052": "\u0403",
-    "/afii10053": "\u0404",
-    "/afii10054": "\u0405",
-    "/afii10055": "\u0406",
-    "/afii10056": "\u0407",
-    "/afii10057": "\u0408",
-    "/afii10058": "\u0409",
-    "/afii10059": "\u040A",
-    "/afii10060": "\u040B",
-    "/afii10061": "\u040C",
-    "/afii10062": "\u040E",
-    "/afii10063": "\uF6C4",
-    "/afii10064": "\uF6C5",
-    "/afii10065": "\u0430",
-    "/afii10066": "\u0431",
-    "/afii10067": "\u0432",
-    "/afii10068": "\u0433",
-    "/afii10069": "\u0434",
-    "/afii10070": "\u0435",
-    "/afii10071": "\u0451",
-    "/afii10072": "\u0436",
-    "/afii10073": "\u0437",
-    "/afii10074": "\u0438",
-    "/afii10075": "\u0439",
-    "/afii10076": "\u043A",
-    "/afii10077": "\u043B",
-    "/afii10078": "\u043C",
-    "/afii10079": "\u043D",
-    "/afii10080": "\u043E",
-    "/afii10081": "\u043F",
-    "/afii10082": "\u0440",
-    "/afii10083": "\u0441",
-    "/afii10084": "\u0442",
-    "/afii10085": "\u0443",
-    "/afii10086": "\u0444",
-    "/afii10087": "\u0445",
-    "/afii10088": "\u0446",
-    "/afii10089": "\u0447",
-    "/afii10090": "\u0448",
-    "/afii10091": "\u0449",
-    "/afii10092": "\u044A",
-    "/afii10093": "\u044B",
-    "/afii10094": "\u044C",
-    "/afii10095": "\u044D",
-    "/afii10096": "\u044E",
-    "/afii10097": "\u044F",
-    "/afii10098": "\u0491",
-    "/afii10099": "\u0452",
-    "/afii10100": "\u0453",
-    "/afii10101": "\u0454",
-    "/afii10102": "\u0455",
-    "/afii10103": "\u0456",
-    "/afii10104": "\u0457",
-    "/afii10105": "\u0458",
-    "/afii10106": "\u0459",
-    "/afii10107": "\u045A",
-    "/afii10108": "\u045B",
-    "/afii10109": "\u045C",
-    "/afii10110": "\u045E",
-    "/afii10145": "\u040F",
-    "/afii10146": "\u0462",
-    "/afii10147": "\u0472",
-    "/afii10148": "\u0474",
-    "/afii10192": "\uF6C6",
-    "/afii10193": "\u045F",
-    "/afii10194": "\u0463",
-    "/afii10195": "\u0473",
-    "/afii10196": "\u0475",
-    "/afii10831": "\uF6C7",
-    "/afii10832": "\uF6C8",
-    "/afii10846": "\u04D9",
-    "/afii299": "\u200E",
-    "/afii300": "\u200F",
-    "/afii301": "\u200D",
-    "/afii57381": "\u066A",
-    "/afii57388": "\u060C",
-    "/afii57392": "\u0660",
-    "/afii57393": "\u0661",
-    "/afii57394": "\u0662",
-    "/afii57395": "\u0663",
-    "/afii57396": "\u0664",
-    "/afii57397": "\u0665",
-    "/afii57398": "\u0666",
-    "/afii57399": "\u0667",
-    "/afii57400": "\u0668",
-    "/afii57401": "\u0669",
-    "/afii57403": "\u061B",
-    "/afii57407": "\u061F",
-    "/afii57409": "\u0621",
-    "/afii57410": "\u0622",
-    "/afii57411": "\u0623",
-    "/afii57412": "\u0624",
-    "/afii57413": "\u0625",
-    "/afii57414": "\u0626",
-    "/afii57415": "\u0627",
-    "/afii57416": "\u0628",
-    "/afii57417": "\u0629",
-    "/afii57418": "\u062A",
-    "/afii57419": "\u062B",
-    "/afii57420": "\u062C",
-    "/afii57421": "\u062D",
-    "/afii57422": "\u062E",
-    "/afii57423": "\u062F",
-    "/afii57424": "\u0630",
-    "/afii57425": "\u0631",
-    "/afii57426": "\u0632",
-    "/afii57427": "\u0633",
-    "/afii57428": "\u0634",
-    "/afii57429": "\u0635",
-    "/afii57430": "\u0636",
-    "/afii57431": "\u0637",
-    "/afii57432": "\u0638",
-    "/afii57433": "\u0639",
-    "/afii57434": "\u063A",
-    "/afii57440": "\u0640",
-    "/afii57441": "\u0641",
-    "/afii57442": "\u0642",
-    "/afii57443": "\u0643",
-    "/afii57444": "\u0644",
-    "/afii57445": "\u0645",
-    "/afii57446": "\u0646",
-    "/afii57448": "\u0648",
-    "/afii57449": "\u0649",
-    "/afii57450": "\u064A",
-    "/afii57451": "\u064B",
-    "/afii57452": "\u064C",
-    "/afii57453": "\u064D",
-    "/afii57454": "\u064E",
-    "/afii57455": "\u064F",
-    "/afii57456": "\u0650",
-    "/afii57457": "\u0651",
-    "/afii57458": "\u0652",
-    "/afii57470": "\u0647",
-    "/afii57505": "\u06A4",
-    "/afii57506": "\u067E",
-    "/afii57507": "\u0686",
-    "/afii57508": "\u0698",
-    "/afii57509": "\u06AF",
-    "/afii57511": "\u0679",
-    "/afii57512": "\u0688",
-    "/afii57513": "\u0691",
-    "/afii57514": "\u06BA",
-    "/afii57519": "\u06D2",
-    "/afii57534": "\u06D5",
-    "/afii57636": "\u20AA",
-    "/afii57645": "\u05BE",
-    "/afii57658": "\u05C3",
-    "/afii57664": "\u05D0",
-    "/afii57665": "\u05D1",
-    "/afii57666": "\u05D2",
-    "/afii57667": "\u05D3",
-    "/afii57668": "\u05D4",
-    "/afii57669": "\u05D5",
-    "/afii57670": "\u05D6",
-    "/afii57671": "\u05D7",
-    "/afii57672": "\u05D8",
-    "/afii57673": "\u05D9",
-    "/afii57674": "\u05DA",
-    "/afii57675": "\u05DB",
-    "/afii57676": "\u05DC",
-    "/afii57677": "\u05DD",
-    "/afii57678": "\u05DE",
-    "/afii57679": "\u05DF",
-    "/afii57680": "\u05E0",
-    "/afii57681": "\u05E1",
-    "/afii57682": "\u05E2",
-    "/afii57683": "\u05E3",
-    "/afii57684": "\u05E4",
-    "/afii57685": "\u05E5",
-    "/afii57686": "\u05E6",
-    "/afii57687": "\u05E7",
-    "/afii57688": "\u05E8",
-    "/afii57689": "\u05E9",
-    "/afii57690": "\u05EA",
-    "/afii57694": "\uFB2A",
-    "/afii57695": "\uFB2B",
-    "/afii57700": "\uFB4B",
-    "/afii57705": "\uFB1F",
-    "/afii57716": "\u05F0",
-    "/afii57717": "\u05F1",
-    "/afii57718": "\u05F2",
-    "/afii57723": "\uFB35",
-    "/afii57793": "\u05B4",
-    "/afii57794": "\u05B5",
-    "/afii57795": "\u05B6",
-    "/afii57796": "\u05BB",
-    "/afii57797": "\u05B8",
-    "/afii57798": "\u05B7",
-    "/afii57799": "\u05B0",
-    "/afii57800": "\u05B2",
-    "/afii57801": "\u05B1",
-    "/afii57802": "\u05B3",
-    "/afii57803": "\u05C2",
-    "/afii57804": "\u05C1",
-    "/afii57806": "\u05B9",
-    "/afii57807": "\u05BC",
-    "/afii57839": "\u05BD",
-    "/afii57841": "\u05BF",
-    "/afii57842": "\u05C0",
-    "/afii57929": "\u02BC",
-    "/afii61248": "\u2105",
-    "/afii61289": "\u2113",
-    "/afii61352": "\u2116",
-    "/afii61573": "\u202C",
-    "/afii61574": "\u202D",
-    "/afii61575": "\u202E",
-    "/afii61664": "\u200C",
-    "/afii63167": "\u066D",
-    "/afii64937": "\u02BD",
-    "/agrave": "\u00E0",
-    "/agravedbl": "\u0201",
-    "/agujarati": "\u0A85",
-    "/agurmukhi": "\u0A05",
-    "/ahiragana": "\u3042",
-    "/ahoi": "\u1EA3",
-    "/ahookabove": "\u1EA3",
-    "/aibengali": "\u0990",
-    "/aibopomofo": "\u311E",
-    "/aideva": "\u0910",
-    "/aiecyr": "\u04D5",
-    "/aiecyrillic": "\u04D5",
-    "/aigujarati": "\u0A90",
-    "/aigurmukhi": "\u0A10",
-    "/aimatragurmukhi": "\u0A48",
-    "/ain.fina": "\uFECA",
-    "/ain.init": "\uFECB",
-    "/ain.init_alefmaksura.fina": "\uFCF7",
-    "/ain.init_jeem.fina": "\uFC29",
-    "/ain.init_jeem.medi": "\uFCBA",
-    "/ain.init_jeem.medi_meem.medi": "\uFDC4",
-    "/ain.init_meem.fina": "\uFC2A",
-    "/ain.init_meem.medi": "\uFCBB",
-    "/ain.init_meem.medi_meem.medi": "\uFD77",
-    "/ain.init_yeh.fina": "\uFCF8",
-    "/ain.isol": "\uFEC9",
-    "/ain.medi": "\uFECC",
-    "/ain.medi_alefmaksura.fina": "\uFD13",
-    "/ain.medi_jeem.medi_meem.fina": "\uFD75",
-    "/ain.medi_meem.medi_alefmaksura.fina": "\uFD78",
-    "/ain.medi_meem.medi_meem.fina": "\uFD76",
-    "/ain.medi_meem.medi_yeh.fina": "\uFDB6",
-    "/ain.medi_yeh.fina": "\uFD14",
-    "/ainThreeDotsDownAbove": "\u075E",
-    "/ainTwoDotsAbove": "\u075D",
-    "/ainTwoDotsVerticallyAbove": "\u075F",
-    "/ainarabic": "\u0639",
-    "/ainfinalarabic": "\uFECA",
-    "/aininitialarabic": "\uFECB",
-    "/ainmedialarabic": "\uFECC",
-    "/ainthreedotsabove": "\u06A0",
-    "/ainvertedbreve": "\u0203",
-    "/airplaneArriving": "\u1F6EC",
-    "/airplaneDeparture": "\u1F6EB",
-    "/aivowelsignbengali": "\u09C8",
-    "/aivowelsigndeva": "\u0948",
-    "/aivowelsigngujarati": "\u0AC8",
-    "/akatakana": "\u30A2",
-    "/akatakanahalfwidth": "\uFF71",
-    "/akorean": "\u314F",
-    "/aktieselskab": "\u214D",
-    "/alarmclock": "\u23F0",
-    "/alef": "\u05D0",
-    "/alef.fina": "\uFE8E",
-    "/alef.init_fathatan.fina": "\uFD3D",
-    "/alef.isol": "\uFE8D",
-    "/alef.medi_fathatan.fina": "\uFD3C",
-    "/alef:hb": "\u05D0",
-    "/alefDigitThreeAbove": "\u0774",
-    "/alefDigitTwoAbove": "\u0773",
-    "/alefLamYehabove": "\u0616",
-    "/alefabove": "\u0670",
-    "/alefarabic": "\u0627",
-    "/alefdageshhebrew": "\uFB30",
-    "/aleffinalarabic": "\uFE8E",
-    "/alefhamza": "\u0623",
-    "/alefhamza.fina": "\uFE84",
-    "/alefhamza.isol": "\uFE83",
-    "/alefhamzaabovearabic": "\u0623",
-    "/alefhamzaabovefinalarabic": "\uFE84",
-    "/alefhamzabelow": "\u0625",
-    "/alefhamzabelow.fina": "\uFE88",
-    "/alefhamzabelow.isol": "\uFE87",
-    "/alefhamzabelowarabic": "\u0625",
-    "/alefhamzabelowfinalarabic": "\uFE88",
-    "/alefhebrew": "\u05D0",
-    "/alefhighhamza": "\u0675",
-    "/aleflamedhebrew": "\uFB4F",
-    "/alefmadda": "\u0622",
-    "/alefmadda.fina": "\uFE82",
-    "/alefmadda.isol": "\uFE81",
-    "/alefmaddaabovearabic": "\u0622",
-    "/alefmaddaabovefinalarabic": "\uFE82",
-    "/alefmaksura": "\u0649",
-    "/alefmaksura.fina": "\uFEF0",
-    "/alefmaksura.init_superscriptalef.fina": "\uFC5D",
-    "/alefmaksura.isol": "\uFEEF",
-    "/alefmaksura.medi_superscriptalef.fina": "\uFC90",
-    "/alefmaksuraarabic": "\u0649",
-    "/alefmaksurafinalarabic": "\uFEF0",
-    "/alefmaksurainitialarabic": "\uFEF3",
-    "/alefmaksuramedialarabic": "\uFEF4",
-    "/alefpatahhebrew": "\uFB2E",
-    "/alefqamatshebrew": "\uFB2F",
-    "/alefwasla": "\u0671",
-    "/alefwasla.fina": "\uFB51",
-    "/alefwasla.isol": "\uFB50",
-    "/alefwavyhamza": "\u0672",
-    "/alefwavyhamzabelow": "\u0673",
-    "/alefwide:hb": "\uFB21",
-    "/alefwithmapiq:hb": "\uFB30",
-    "/alefwithpatah:hb": "\uFB2E",
-    "/alefwithqamats:hb": "\uFB2F",
-    "/alembic": "\u2697",
-    "/aleph": "\u2135",
-    "/alienMonster": "\u1F47E",
-    "/allaroundprofile": "\u232E",
-    "/allequal": "\u224C",
-    "/allianceideographiccircled": "\u32AF",
-    "/allianceideographicparen": "\u323F",
-    "/almostequalorequal": "\u224A",
-    "/alpha": "\u03B1",
-    "/alphaacute": "\u1F71",
-    "/alphaacuteiotasub": "\u1FB4",
-    "/alphaasper": "\u1F01",
-    "/alphaasperacute": "\u1F05",
-    "/alphaasperacuteiotasub": "\u1F85",
-    "/alphaaspergrave": "\u1F03",
-    "/alphaaspergraveiotasub": "\u1F83",
-    "/alphaasperiotasub": "\u1F81",
-    "/alphaaspertilde": "\u1F07",
-    "/alphaaspertildeiotasub": "\u1F87",
-    "/alphabreve": "\u1FB0",
-    "/alphafunc": "\u237A",
-    "/alphagrave": "\u1F70",
-    "/alphagraveiotasub": "\u1FB2",
-    "/alphaiotasub": "\u1FB3",
-    "/alphalenis": "\u1F00",
-    "/alphalenisacute": "\u1F04",
-    "/alphalenisacuteiotasub": "\u1F84",
-    "/alphalenisgrave": "\u1F02",
-    "/alphalenisgraveiotasub": "\u1F82",
-    "/alphalenisiotasub": "\u1F80",
-    "/alphalenistilde": "\u1F06",
-    "/alphalenistildeiotasub": "\u1F86",
-    "/alphatilde": "\u1FB6",
-    "/alphatildeiotasub": "\u1FB7",
-    "/alphatonos": "\u03AC",
-    "/alphaturned": "\u0252",
-    "/alphaunderlinefunc": "\u2376",
-    "/alphawithmacron": "\u1FB1",
-    "/alternateonewayleftwaytraffic": "\u26D5",
-    "/alternative": "\u2387",
-    "/amacron": "\u0101",
-    "/ambulance": "\u1F691",
-    "/americanFootball": "\u1F3C8",
-    "/amfullwidth": "\u33C2",
-    "/amonospace": "\uFF41",
-    "/amountofcheck": "\u2447",
-    "/ampersand": "\u0026",
-    "/ampersandSindhi": "\u06FD",
-    "/ampersandmonospace": "\uFF06",
-    "/ampersandsmall": "\uF726",
-    "/ampersandturned": "\u214B",
-    "/amphora": "\u1F3FA",
-    "/amsquare": "\u33C2",
-    "/anbopomofo": "\u3122",
-    "/anchor": "\u2693",
-    "/ancoradown": "\u2E14",
-    "/ancoraup": "\u2E15",
-    "/andappada": "\uA9C3",
-    "/angbopomofo": "\u3124",
-    "/anger": "\u1F4A2",
-    "/angkhankhuthai": "\u0E5A",
-    "/angle": "\u2220",
-    "/anglearcright": "\u22BE",
-    "/anglebracketleft": "\u3008",
-    "/anglebracketleftvertical": "\uFE3F",
-    "/anglebracketright": "\u3009",
-    "/anglebracketrightvertical": "\uFE40",
-    "/angledottedright": "\u2E16",
-    "/angleleft": "\u2329",
-    "/anglemarkerdottedsubstitutionright": "\u2E01",
-    "/anglemarkersubstitutionright": "\u2E00",
-    "/angleright": "\u232A",
-    "/anglezigzagarrowdownright": "\u237C",
-    "/angryFace": "\u1F620",
-    "/angstrom": "\u212B",
-    "/anguishedFace": "\u1F627",
-    "/ankh": "\u2625",
-    "/anoteleia": "\u0387",
-    "/anpeasquare": "\u3302",
-    "/ant": "\u1F41C",
-    "/antennaBars": "\u1F4F6",
-    "/anticlockwiseDownwardsAndUpwardsOpenCircleArrows": "\u1F504",
-    "/anudattadeva": "\u0952",
-    "/anusvarabengali": "\u0982",
-    "/anusvaradeva": "\u0902",
-    "/anusvaragujarati": "\u0A82",
-    "/ao": "\uA735",
-    "/aogonek": "\u0105",
-    "/aovermfullwidth": "\u33DF",
-    "/apaatosquare": "\u3300",
-    "/aparen": "\u249C",
-    "/aparenthesized": "\u249C",
-    "/apostrophearmenian": "\u055A",
-    "/apostrophedblmod": "\u02EE",
-    "/apostrophemod": "\u02BC",
-    "/apple": "\uF8FF",
-    "/approaches": "\u2250",
-    "/approacheslimit": "\u2250",
-    "/approxequal": "\u2248",
-    "/approxequalorimage": "\u2252",
-    "/approximatelybutnotactuallyequal": "\u2246",
-    "/approximatelyequal": "\u2245",
-    "/approximatelyequalorimage": "\u2252",
-    "/apriltelegraph": "\u32C3",
-    "/aquarius": "\u2652",
-    "/ar:ae": "\u06D5",
-    "/ar:ain": "\u0639",
-    "/ar:alef": "\u0627",
-    "/ar:comma": "\u060C",
-    "/ar:cuberoot": "\u0606",
-    "/ar:decimalseparator": "\u066B",
-    "/ar:e": "\u06D0",
-    "/ar:eight": "\u0668",
-    "/ar:feh": "\u0641",
-    "/ar:five": "\u0665",
-    "/ar:four": "\u0664",
-    "/ar:fourthroot": "\u0607",
-    "/ar:kaf": "\u0643",
-    "/ar:ng": "\u06AD",
-    "/ar:nine": "\u0669",
-    "/ar:numbersign": "\u0600",
-    "/ar:oe": "\u06C6",
-    "/ar:one": "\u0661",
-    "/ar:peh": "\u067E",
-    "/ar:percent": "\u066A",
-    "/ar:perthousand": "\u060A",
-    "/ar:question": "\u061F",
-    "/ar:reh": "\u0631",
-    "/ar:semicolon": "\u061B",
-    "/ar:seven": "\u0667",
-    "/ar:shadda": "\u0651",
-    "/ar:six": "\u0666",
-    "/ar:sukun": "\u0652",
-    "/ar:three": "\u0663",
-    "/ar:two": "\u0662",
-    "/ar:u": "\u06C7",
-    "/ar:ve": "\u06CB",
-    "/ar:yu": "\u06C8",
-    "/ar:zero": "\u0660",
-    "/araeaekorean": "\u318E",
-    "/araeakorean": "\u318D",
-    "/arc": "\u2312",
-    "/archaicmepigraphic": "\uA7FF",
-    "/aries": "\u2648",
-    "/arighthalfring": "\u1E9A",
-    "/aring": "\u00E5",
-    "/aringacute": "\u01FB",
-    "/aringbelow": "\u1E01",
-    "/armn:Ayb": "\u0531",
-    "/armn:Ben": "\u0532",
-    "/armn:Ca": "\u053E",
-    "/armn:Cha": "\u0549",
-    "/armn:Cheh": "\u0543",
-    "/armn:Co": "\u0551",
-    "/armn:DRAMSIGN": "\u058F",
-    "/armn:Da": "\u0534",
-    "/armn:Ech": "\u0535",
-    "/armn:Eh": "\u0537",
-    "/armn:Et": "\u0538",
-    "/armn:Feh": "\u0556",
-    "/armn:Ghad": "\u0542",
-    "/armn:Gim": "\u0533",
-    "/armn:Ho": "\u0540",
-    "/armn:Ini": "\u053B",
-    "/armn:Ja": "\u0541",
-    "/armn:Jheh": "\u054B",
-    "/armn:Keh": "\u0554",
-    "/armn:Ken": "\u053F",
-    "/armn:Liwn": "\u053C",
-    "/armn:Men": "\u0544",
-    "/armn:Now": "\u0546",
-    "/armn:Oh": "\u0555",
-    "/armn:Peh": "\u054A",
-    "/armn:Piwr": "\u0553",
-    "/armn:Ra": "\u054C",
-    "/armn:Reh": "\u0550",
-    "/armn:Seh": "\u054D",
-    "/armn:Sha": "\u0547",
-    "/armn:Tiwn": "\u054F",
-    "/armn:To": "\u0539",
-    "/armn:Vew": "\u054E",
-    "/armn:Vo": "\u0548",
-    "/armn:Xeh": "\u053D",
-    "/armn:Yi": "\u0545",
-    "/armn:Yiwn": "\u0552",
-    "/armn:Za": "\u0536",
-    "/armn:Zhe": "\u053A",
-    "/armn:abbreviationmark": "\u055F",
-    "/armn:apostrophe": "\u055A",
-    "/armn:ayb": "\u0561",
-    "/armn:ben": "\u0562",
-    "/armn:ca": "\u056E",
-    "/armn:cha": "\u0579",
-    "/armn:cheh": "\u0573",
-    "/armn:co": "\u0581",
-    "/armn:comma": "\u055D",
-    "/armn:da": "\u0564",
-    "/armn:ech": "\u0565",
-    "/armn:ech_yiwn": "\u0587",
-    "/armn:eh": "\u0567",
-    "/armn:emphasismark": "\u055B",
-    "/armn:et": "\u0568",
-    "/armn:exclam": "\u055C",
-    "/armn:feh": "\u0586",
-    "/armn:ghad": "\u0572",
-    "/armn:gim": "\u0563",
-    "/armn:ho": "\u0570",
-    "/armn:hyphen": "\u058A",
-    "/armn:ini": "\u056B",
-    "/armn:ja": "\u0571",
-    "/armn:jheh": "\u057B",
-    "/armn:keh": "\u0584",
-    "/armn:ken": "\u056F",
-    "/armn:leftfacingeternitysign": "\u058E",
-    "/armn:liwn": "\u056C",
-    "/armn:men": "\u0574",
-    "/armn:men_ech": "\uFB14",
-    "/armn:men_ini": "\uFB15",
-    "/armn:men_now": "\uFB13",
-    "/armn:men_xeh": "\uFB17",
-    "/armn:now": "\u0576",
-    "/armn:oh": "\u0585",
-    "/armn:peh": "\u057A",
-    "/armn:period": "\u0589",
-    "/armn:piwr": "\u0583",
-    "/armn:question": "\u055E",
-    "/armn:ra": "\u057C",
-    "/armn:reh": "\u0580",
-    "/armn:rightfacingeternitysign": "\u058D",
-    "/armn:ringhalfleft": "\u0559",
-    "/armn:seh": "\u057D",
-    "/armn:sha": "\u0577",
-    "/armn:tiwn": "\u057F",
-    "/armn:to": "\u0569",
-    "/armn:vew": "\u057E",
-    "/armn:vew_now": "\uFB16",
-    "/armn:vo": "\u0578",
-    "/armn:xeh": "\u056D",
-    "/armn:yi": "\u0575",
-    "/armn:yiwn": "\u0582",
-    "/armn:za": "\u0566",
-    "/armn:zhe": "\u056A",
-    "/arrowNE": "\u2197",
-    "/arrowNW": "\u2196",
-    "/arrowSE": "\u2198",
-    "/arrowSW": "\u2199",
-    "/arrowanticlockwiseopencircle": "\u21BA",
-    "/arrowanticlockwisesemicircle": "\u21B6",
-    "/arrowboth": "\u2194",
-    "/arrowclockwiseopencircle": "\u21BB",
-    "/arrowclockwisesemicircle": "\u21B7",
-    "/arrowdashdown": "\u21E3",
-    "/arrowdashleft": "\u21E0",
-    "/arrowdashright": "\u21E2",
-    "/arrowdashup": "\u21E1",
-    "/arrowdblboth": "\u21D4",
-    "/arrowdbldown": "\u21D3",
-    "/arrowdblleft": "\u21D0",
-    "/arrowdblright": "\u21D2",
-    "/arrowdblup": "\u21D1",
-    "/arrowdown": "\u2193",
-    "/arrowdowndashed": "\u21E3",
-    "/arrowdownfrombar": "\u21A7",
-    "/arrowdownleft": "\u2199",
-    "/arrowdownright": "\u2198",
-    "/arrowdowntwoheaded": "\u21A1",
-    "/arrowdownwhite": "\u21E9",
-    "/arrowdownzigzag": "\u21AF",
-    "/arrowheaddown": "\u2304",
-    "/arrowheaddownlowmod": "\u02EF",
-    "/arrowheaddownmod": "\u02C5",
-    "/arrowheadleftlowmod": "\u02F1",
-    "/arrowheadleftmod": "\u02C2",
-    "/arrowheadrightlowmod": "\u02F2",
-    "/arrowheadrightmod": "\u02C3",
-    "/arrowheadtwobarsuphorizontal": "\u2324",
-    "/arrowheadup": "\u2303",
-    "/arrowheaduplowmod": "\u02F0",
-    "/arrowheadupmod": "\u02C4",
-    "/arrowhorizex": "\uF8E7",
-    "/arrowleft": "\u2190",
-    "/arrowleftdashed": "\u21E0",
-    "/arrowleftdbl": "\u21D0",
-    "/arrowleftdblstroke": "\u21CD",
-    "/arrowleftdowncorner": "\u21B5",
-    "/arrowleftdowntip": "\u21B2",
-    "/arrowleftfrombar": "\u21A4",
-    "/arrowlefthook": "\u21A9",
-    "/arrowleftloop": "\u21AB",
-    "/arrowleftlowmod": "\u02FF",
-    "/arrowleftoverright": "\u21C6",
-    "/arrowleftoverrighttobar": "\u21B9",
-    "/arrowleftright": "\u2194",
-    "/arrowleftrightstroke": "\u21AE",
-    "/arrowleftrightwave": "\u21AD",
-    "/arrowleftsquiggle": "\u21DC",
-    "/arrowleftstroke": "\u219A",
-    "/arrowlefttail": "\u21A2",
-    "/arrowlefttobar": "\u21E4",
-    "/arrowlefttwoheaded": "\u219E",
-    "/arrowleftuptip": "\u21B0",
-    "/arrowleftwave": "\u219C",
-    "/arrowleftwhite": "\u21E6",
-    "/arrowlongNWtobar": "\u21B8",
-    "/arrowright": "\u2192",
-    "/arrowrightdashed": "\u21E2",
-    "/arrowrightdblstroke": "\u21CF",
-    "/arrowrightdowncorner": "\u21B4",
-    "/arrowrightdowntip": "\u21B3",
-    "/arrowrightfrombar": "\u21A6",
-    "/arrowrightheavy": "\u279E",
-    "/arrowrighthook": "\u21AA",
-    "/arrowrightloop": "\u21AC",
-    "/arrowrightoverleft": "\u21C4",
-    "/arrowrightsmallcircle": "\u21F4",
-    "/arrowrightsquiggle": "\u21DD",
-    "/arrowrightstroke": "\u219B",
-    "/arrowrighttail": "\u21A3",
-    "/arrowrighttobar": "\u21E5",
-    "/arrowrighttwoheaded": "\u21A0",
-    "/arrowrightwave": "\u219D",
-    "/arrowrightwhite": "\u21E8",
-    "/arrowspaireddown": "\u21CA",
-    "/arrowspairedleft": "\u21C7",
-    "/arrowspairedright": "\u21C9",
-    "/arrowspairedup": "\u21C8",
-    "/arrowtableft": "\u21E4",
-    "/arrowtabright": "\u21E5",
-    "/arrowup": "\u2191",
-    "/arrowupdashed": "\u21E1",
-    "/arrowupdn": "\u2195",
-    "/arrowupdnbse": "\u21A8",
-    "/arrowupdown": "\u2195",
-    "/arrowupdownbase": "\u21A8",
-    "/arrowupdownwithbase": "\u21A8",
-    "/arrowupfrombar": "\u21A5",
-    "/arrowupleft": "\u2196",
-    "/arrowupleftofdown": "\u21C5",
-    "/arrowupright": "\u2197",
-    "/arrowuprighttip": "\u21B1",
-    "/arrowuptwoheaded": "\u219F",
-    "/arrowupwhite": "\u21E7",
-    "/arrowvertex": "\uF8E6",
-    "/articulatedLorry": "\u1F69B",
-    "/artistPalette": "\u1F3A8",
-    "/aruhuasquare": "\u3301",
-    "/asciicircum": "\u005E",
-    "/asciicircummonospace": "\uFF3E",
-    "/asciitilde": "\u007E",
-    "/asciitildemonospace": "\uFF5E",
-    "/ascript": "\u0251",
-    "/ascriptturned": "\u0252",
-    "/asmallhiragana": "\u3041",
-    "/asmallkatakana": "\u30A1",
-    "/asmallkatakanahalfwidth": "\uFF67",
-    "/asper": "\u1FFE",
-    "/asperacute": "\u1FDE",
-    "/aspergrave": "\u1FDD",
-    "/aspertilde": "\u1FDF",
-    "/assertion": "\u22A6",
-    "/asterisk": "\u002A",
-    "/asteriskaltonearabic": "\u066D",
-    "/asteriskarabic": "\u066D",
-    "/asteriskmath": "\u2217",
-    "/asteriskmonospace": "\uFF0A",
-    "/asterisksmall": "\uFE61",
-    "/asterism": "\u2042",
-    "/astonishedFace": "\u1F632",
-    "/astroke": "\u2C65",
-    "/astronomicaluranus": "\u26E2",
-    "/asuperior": "\uF6E9",
-    "/asympticallyequal": "\u2243",
-    "/asymptoticallyequal": "\u2243",
-    "/at": "\u0040",
-    "/athleticShoe": "\u1F45F",
-    "/atilde": "\u00E3",
-    "/atmonospace": "\uFF20",
-    "/atnachHafukh:hb": "\u05A2",
-    "/atom": "\u269B",
-    "/atsmall": "\uFE6B",
-    "/attentionideographiccircled": "\u329F",
-    "/aturned": "\u0250",
-    "/au": "\uA737",
-    "/aubengali": "\u0994",
-    "/aubergine": "\u1F346",
-    "/aubopomofo": "\u3120",
-    "/audeva": "\u0914",
-    "/aufullwidth": "\u3373",
-    "/augujarati": "\u0A94",
-    "/augurmukhi": "\u0A14",
-    "/augusttelegraph": "\u32C7",
-    "/aulengthmarkbengali": "\u09D7",
-    "/aumatragurmukhi": "\u0A4C",
-    "/austral": "\u20B3",
-    "/automatedTellerMachine": "\u1F3E7",
-    "/automobile": "\u1F697",
-    "/auvowelsignbengali": "\u09CC",
-    "/auvowelsigndeva": "\u094C",
-    "/auvowelsigngujarati": "\u0ACC",
-    "/av": "\uA739",
-    "/avagrahadeva": "\u093D",
-    "/avhorizontalbar": "\uA73B",
-    "/ay": "\uA73D",
-    "/aybarmenian": "\u0561",
-    "/ayin": "\u05E2",
-    "/ayin:hb": "\u05E2",
-    "/ayinalt:hb": "\uFB20",
-    "/ayinaltonehebrew": "\uFB20",
-    "/ayinhebrew": "\u05E2",
-    "/azla:hb": "\u059C",
-    "/b": "\u0062",
-    "/baarerusquare": "\u332D",
-    "/babengali": "\u09AC",
-    "/babyAngel": "\u1F47C",
-    "/babyBottle": "\u1F37C",
-    "/babyChick": "\u1F424",
-    "/backLeftwardsArrowAbove": "\u1F519",
-    "/backOfEnvelope": "\u1F582",
-    "/backslash": "\u005C",
-    "/backslashbarfunc": "\u2340",
-    "/backslashdbl": "\u244A",
-    "/backslashmonospace": "\uFF3C",
-    "/bactrianCamel": "\u1F42B",
-    "/badeva": "\u092C",
-    "/badmintonRacquetAndShuttlecock": "\u1F3F8",
-    "/bagdelimitersshapeleft": "\u27C5",
-    "/bagdelimitersshaperight": "\u27C6",
-    "/baggageClaim": "\u1F6C4",
-    "/bagujarati": "\u0AAC",
-    "/bagurmukhi": "\u0A2C",
-    "/bahiragana": "\u3070",
-    "/bahtthai": "\u0E3F",
-    "/bakatakana": "\u30D0",
-    "/balloon": "\u1F388",
-    "/ballotBoldScriptX": "\u1F5F6",
-    "/ballotBoxBallot": "\u1F5F3",
-    "/ballotBoxBoldCheck": "\u1F5F9",
-    "/ballotBoxBoldScriptX": "\u1F5F7",
-    "/ballotBoxScriptX": "\u1F5F5",
-    "/ballotScriptX": "\u1F5F4",
-    "/bamurda": "\uA9A8",
-    "/banana": "\u1F34C",
-    "/bank": "\u1F3E6",
-    "/banknoteDollarSign": "\u1F4B5",
-    "/banknoteEuroSign": "\u1F4B6",
-    "/banknotePoundSign": "\u1F4B7",
-    "/banknoteYenSign": "\u1F4B4",
-    "/bar": "\u007C",
-    "/barChart": "\u1F4CA",
-    "/barberPole": "\u1F488",
-    "/barfullwidth": "\u3374",
-    "/barmonospace": "\uFF5C",
-    "/barquillverticalleft": "\u2E20",
-    "/barquillverticalright": "\u2E21",
-    "/baseball": "\u26BE",
-    "/basketballAndHoop": "\u1F3C0",
-    "/bath": "\u1F6C0",
-    "/bathtub": "\u1F6C1",
-    "/battery": "\u1F50B",
-    "/bbopomofo": "\u3105",
-    "/bcircle": "\u24D1",
-    "/bdot": "\u1E03",
-    "/bdotaccent": "\u1E03",
-    "/bdotbelow": "\u1E05",
-    "/beachUmbrella": "\u1F3D6",
-    "/beamedAscendingMusicalNotes": "\u1F39C",
-    "/beamedDescendingMusicalNotes": "\u1F39D",
-    "/beamedeighthnotes": "\u266B",
-    "/beamedsixteenthnotes": "\u266C",
-    "/beamfunc": "\u2336",
-    "/bearFace": "\u1F43B",
-    "/beatingHeart": "\u1F493",
-    "/because": "\u2235",
-    "/becyr": "\u0431",
-    "/becyrillic": "\u0431",
-    "/bed": "\u1F6CF",
-    "/beeh": "\u067B",
-    "/beeh.fina": "\uFB53",
-    "/beeh.init": "\uFB54",
-    "/beeh.isol": "\uFB52",
-    "/beeh.medi": "\uFB55",
-    "/beerMug": "\u1F37A",
-    "/beetasquare": "\u333C",
-    "/beh": "\u0628",
-    "/beh.fina": "\uFE90",
-    "/beh.init": "\uFE91",
-    "/beh.init_alefmaksura.fina": "\uFC09",
-    "/beh.init_hah.fina": "\uFC06",
-    "/beh.init_hah.medi": "\uFC9D",
-    "/beh.init_heh.medi": "\uFCA0",
-    "/beh.init_jeem.fina": "\uFC05",
-    "/beh.init_jeem.medi": "\uFC9C",
-    "/beh.init_khah.fina": "\uFC07",
-    "/beh.init_khah.medi": "\uFC9E",
-    "/beh.init_meem.fina": "\uFC08",
-    "/beh.init_meem.medi": "\uFC9F",
-    "/beh.init_yeh.fina": "\uFC0A",
-    "/beh.isol": "\uFE8F",
-    "/beh.medi": "\uFE92",
-    "/beh.medi_alefmaksura.fina": "\uFC6E",
-    "/beh.medi_hah.medi_yeh.fina": "\uFDC2",
-    "/beh.medi_heh.medi": "\uFCE2",
-    "/beh.medi_khah.medi_yeh.fina": "\uFD9E",
-    "/beh.medi_meem.fina": "\uFC6C",
-    "/beh.medi_meem.medi": "\uFCE1",
-    "/beh.medi_noon.fina": "\uFC6D",
-    "/beh.medi_reh.fina": "\uFC6A",
-    "/beh.medi_yeh.fina": "\uFC6F",
-    "/beh.medi_zain.fina": "\uFC6B",
-    "/behDotBelowThreeDotsAbove": "\u0751",
-    "/behInvertedSmallVBelow": "\u0755",
-    "/behSmallV": "\u0756",
-    "/behThreeDotsHorizontallyBelow": "\u0750",
-    "/behThreeDotsUpBelow": "\u0752",
-    "/behThreeDotsUpBelowTwoDotsAbove": "\u0753",
-    "/behTwoDotsBelowDotAbove": "\u0754",
-    "/beharabic": "\u0628",
-    "/beheh": "\u0680",
-    "/beheh.fina": "\uFB5B",
-    "/beheh.init": "\uFB5C",
-    "/beheh.isol": "\uFB5A",
-    "/beheh.medi": "\uFB5D",
-    "/behfinalarabic": "\uFE90",
-    "/behinitialarabic": "\uFE91",
-    "/behiragana": "\u3079",
-    "/behmedialarabic": "\uFE92",
-    "/behmeeminitialarabic": "\uFC9F",
-    "/behmeemisolatedarabic": "\uFC08",
-    "/behnoonfinalarabic": "\uFC6D",
-    "/bekatakana": "\u30D9",
-    "/bellCancellationStroke": "\u1F515",
-    "/bellhopBell": "\u1F6CE",
-    "/beltbuckle": "\u2444",
-    "/benarmenian": "\u0562",
-    "/beng:a": "\u0985",
-    "/beng:aa": "\u0986",
-    "/beng:aasign": "\u09BE",
-    "/beng:abbreviationsign": "\u09FD",
-    "/beng:ai": "\u0990",
-    "/beng:aisign": "\u09C8",
-    "/beng:anji": "\u0980",
-    "/beng:anusvara": "\u0982",
-    "/beng:au": "\u0994",
-    "/beng:aulengthmark": "\u09D7",
-    "/beng:ausign": "\u09CC",
-    "/beng:avagraha": "\u09BD",
-    "/beng:ba": "\u09AC",
-    "/beng:bha": "\u09AD",
-    "/beng:ca": "\u099A",
-    "/beng:candrabindu": "\u0981",
-    "/beng:cha": "\u099B",
-    "/beng:currencyoneless": "\u09F8",
-    "/beng:da": "\u09A6",
-    "/beng:dda": "\u09A1",
-    "/beng:ddha": "\u09A2",
-    "/beng:dha": "\u09A7",
-    "/beng:e": "\u098F",
-    "/beng:eight": "\u09EE",
-    "/beng:esign": "\u09C7",
-    "/beng:five": "\u09EB",
-    "/beng:four": "\u09EA",
-    "/beng:fourcurrencynumerator": "\u09F7",
-    "/beng:ga": "\u0997",
-    "/beng:gandamark": "\u09FB",
-    "/beng:gha": "\u0998",
-    "/beng:ha": "\u09B9",
-    "/beng:i": "\u0987",
-    "/beng:ii": "\u0988",
-    "/beng:iisign": "\u09C0",
-    "/beng:isign": "\u09BF",
-    "/beng:isshar": "\u09FA",
-    "/beng:ja": "\u099C",
-    "/beng:jha": "\u099D",
-    "/beng:ka": "\u0995",
-    "/beng:kha": "\u0996",
-    "/beng:khandata": "\u09CE",
-    "/beng:la": "\u09B2",
-    "/beng:llvocal": "\u09E1",
-    "/beng:llvocalsign": "\u09E3",
-    "/beng:lvocal": "\u098C",
-    "/beng:lvocalsign": "\u09E2",
-    "/beng:ma": "\u09AE",
-    "/beng:na": "\u09A8",
-    "/beng:nga": "\u0999",
-    "/beng:nine": "\u09EF",
-    "/beng:nna": "\u09A3",
-    "/beng:nukta": "\u09BC",
-    "/beng:nya": "\u099E",
-    "/beng:o": "\u0993",
-    "/beng:one": "\u09E7",
-    "/beng:onecurrencynumerator": "\u09F4",
-    "/beng:osign": "\u09CB",
-    "/beng:pa": "\u09AA",
-    "/beng:pha": "\u09AB",
-    "/beng:ra": "\u09B0",
-    "/beng:ralowdiagonal": "\u09F1",
-    "/beng:ramiddiagonal": "\u09F0",
-    "/beng:rha": "\u09DD",
-    "/beng:rra": "\u09DC",
-    "/beng:rrvocal": "\u09E0",
-    "/beng:rrvocalsign": "\u09C4",
-    "/beng:rupee": "\u09F3",
-    "/beng:rupeemark": "\u09F2",
-    "/beng:rvocal": "\u098B",
-    "/beng:rvocalsign": "\u09C3",
-    "/beng:sa": "\u09B8",
-    "/beng:seven": "\u09ED",
-    "/beng:sha": "\u09B6",
-    "/beng:six": "\u09EC",
-    "/beng:sixteencurrencydenominator": "\u09F9",
-    "/beng:ssa": "\u09B7",
-    "/beng:ta": "\u09A4",
-    "/beng:tha": "\u09A5",
-    "/beng:three": "\u09E9",
-    "/beng:threecurrencynumerator": "\u09F6",
-    "/beng:tta": "\u099F",
-    "/beng:ttha": "\u09A0",
-    "/beng:two": "\u09E8",
-    "/beng:twocurrencynumerator": "\u09F5",
-    "/beng:u": "\u0989",
-    "/beng:usign": "\u09C1",
-    "/beng:uu": "\u098A",
-    "/beng:uusign": "\u09C2",
-    "/beng:vedicanusvara": "\u09FC",
-    "/beng:virama": "\u09CD",
-    "/beng:visarga": "\u0983",
-    "/beng:ya": "\u09AF",
-    "/beng:yya": "\u09DF",
-    "/beng:zero": "\u09E6",
-    "/bentoBox": "\u1F371",
-    "/benzenering": "\u232C",
-    "/benzeneringcircle": "\u23E3",
-    "/bet": "\u05D1",
-    "/bet:hb": "\u05D1",
-    "/beta": "\u03B2",
-    "/betasymbol": "\u03D0",
-    "/betasymbolgreek": "\u03D0",
-    "/betdagesh": "\uFB31",
-    "/betdageshhebrew": "\uFB31",
-    "/bethebrew": "\u05D1",
-    "/betrafehebrew": "\uFB4C",
-    "/between": "\u226C",
-    "/betwithdagesh:hb": "\uFB31",
-    "/betwithrafe:hb": "\uFB4C",
-    "/bflourish": "\uA797",
-    "/bhabengali": "\u09AD",
-    "/bhadeva": "\u092D",
-    "/bhagujarati": "\u0AAD",
-    "/bhagurmukhi": "\u0A2D",
-    "/bhook": "\u0253",
-    "/bicycle": "\u1F6B2",
-    "/bicyclist": "\u1F6B4",
-    "/bihiragana": "\u3073",
-    "/bikatakana": "\u30D3",
-    "/bikini": "\u1F459",
-    "/bilabialclick": "\u0298",
-    "/billiards": "\u1F3B1",
-    "/bindigurmukhi": "\u0A02",
-    "/biohazard": "\u2623",
-    "/bird": "\u1F426",
-    "/birthdayCake": "\u1F382",
-    "/birusquare": "\u3331",
-    "/bishopblack": "\u265D",
-    "/bishopwhite": "\u2657",
-    "/bitcoin": "\u20BF",
-    "/blackDownPointingBackhandIndex": "\u1F5A3",
-    "/blackDroplet": "\u1F322",
-    "/blackFolder": "\u1F5BF",
-    "/blackHardShellFloppyDisk": "\u1F5AA",
-    "/blackHeart": "\u1F5A4",
-    "/blackLeftPointingBackhandIndex": "\u1F59C",
-    "/blackPennant": "\u1F3F2",
-    "/blackPushpin": "\u1F588",
-    "/blackRightPointingBackhandIndex": "\u1F59D",
-    "/blackRosette": "\u1F3F6",
-    "/blackSkullAndCrossbones": "\u1F571",
-    "/blackSquareButton": "\u1F532",
-    "/blackTouchtoneTelephone": "\u1F57F",
-    "/blackUpPointingBackhandIndex": "\u1F5A2",
-    "/blackcircle": "\u25CF",
-    "/blackcircleforrecord": "\u23FA",
-    "/blackdiamond": "\u25C6",
-    "/blackdownpointingtriangle": "\u25BC",
-    "/blackforstopsquare": "\u23F9",
-    "/blackleftpointingpointer": "\u25C4",
-    "/blackleftpointingtriangle": "\u25C0",
-    "/blacklenticularbracketleft": "\u3010",
-    "/blacklenticularbracketleftvertical": "\uFE3B",
-    "/blacklenticularbracketright": "\u3011",
-    "/blacklenticularbracketrightvertical": "\uFE3C",
-    "/blacklowerlefttriangle": "\u25E3",
-    "/blacklowerrighttriangle": "\u25E2",
-    "/blackmediumpointingtriangledown": "\u23F7",
-    "/blackmediumpointingtriangleleft": "\u23F4",
-    "/blackmediumpointingtriangleright": "\u23F5",
-    "/blackmediumpointingtriangleup": "\u23F6",
-    "/blackpointingdoubletrianglebarverticalleft": "\u23EE",
-    "/blackpointingdoubletrianglebarverticalright": "\u23ED",
-    "/blackpointingdoubletriangledown": "\u23EC",
-    "/blackpointingdoubletriangleleft": "\u23EA",
-    "/blackpointingdoubletriangleright": "\u23E9",
-    "/blackpointingdoubletriangleup": "\u23EB",
-    "/blackpointingtriangledoublebarverticalright": "\u23EF",
-    "/blackrectangle": "\u25AC",
-    "/blackrightpointingpointer": "\u25BA",
-    "/blackrightpointingtriangle": "\u25B6",
-    "/blacksmallsquare": "\u25AA",
-    "/blacksmilingface": "\u263B",
-    "/blacksquare": "\u25A0",
-    "/blackstar": "\u2605",
-    "/blackupperlefttriangle": "\u25E4",
-    "/blackupperrighttriangle": "\u25E5",
-    "/blackuppointingsmalltriangle": "\u25B4",
-    "/blackuppointingtriangle": "\u25B2",
-    "/blackwardsbulletleft": "\u204C",
-    "/blackwardsbulletright": "\u204D",
-    "/blank": "\u2423",
-    "/blinebelow": "\u1E07",
-    "/block": "\u2588",
-    "/blossom": "\u1F33C",
-    "/blowfish": "\u1F421",
-    "/blueBook": "\u1F4D8",
-    "/blueHeart": "\u1F499",
-    "/bmonospace": "\uFF42",
-    "/boar": "\u1F417",
-    "/board": "\u2328",
-    "/bobaimaithai": "\u0E1A",
-    "/bohiragana": "\u307C",
-    "/bokatakana": "\u30DC",
-    "/bomb": "\u1F4A3",
-    "/book": "\u1F56E",
-    "/bookmark": "\u1F516",
-    "/bookmarkTabs": "\u1F4D1",
-    "/books": "\u1F4DA",
-    "/bopo:a": "\u311A",
-    "/bopo:ai": "\u311E",
-    "/bopo:an": "\u3122",
-    "/bopo:ang": "\u3124",
-    "/bopo:au": "\u3120",
-    "/bopo:b": "\u3105",
-    "/bopo:c": "\u3118",
-    "/bopo:ch": "\u3114",
-    "/bopo:d": "\u3109",
-    "/bopo:e": "\u311C",
-    "/bopo:eh": "\u311D",
-    "/bopo:ei": "\u311F",
-    "/bopo:en": "\u3123",
-    "/bopo:eng": "\u3125",
-    "/bopo:er": "\u3126",
-    "/bopo:f": "\u3108",
-    "/bopo:g": "\u310D",
-    "/bopo:gn": "\u312C",
-    "/bopo:h": "\u310F",
-    "/bopo:i": "\u3127",
-    "/bopo:ih": "\u312D",
-    "/bopo:iu": "\u3129",
-    "/bopo:j": "\u3110",
-    "/bopo:k": "\u310E",
-    "/bopo:l": "\u310C",
-    "/bopo:m": "\u3107",
-    "/bopo:n": "\u310B",
-    "/bopo:ng": "\u312B",
-    "/bopo:o": "\u311B",
-    "/bopo:ou": "\u3121",
-    "/bopo:owithdotabove": "\u312E",
-    "/bopo:p": "\u3106",
-    "/bopo:q": "\u3111",
-    "/bopo:r": "\u3116",
-    "/bopo:s": "\u3119",
-    "/bopo:sh": "\u3115",
-    "/bopo:t": "\u310A",
-    "/bopo:u": "\u3128",
-    "/bopo:v": "\u312A",
-    "/bopo:x": "\u3112",
-    "/bopo:z": "\u3117",
-    "/bopo:zh": "\u3113",
-    "/borutosquare": "\u333E",
-    "/bottlePoppingCork": "\u1F37E",
-    "/bouquet": "\u1F490",
-    "/bouquetOfFlowers": "\u1F395",
-    "/bowAndArrow": "\u1F3F9",
-    "/bowlOfHygieia": "\u1F54F",
-    "/bowling": "\u1F3B3",
-    "/boxlineverticalleft": "\u23B8",
-    "/boxlineverticalright": "\u23B9",
-    "/boy": "\u1F466",
-    "/boys": "\u1F6C9",
-    "/bparen": "\u249D",
-    "/bparenthesized": "\u249D",
-    "/bqfullwidth": "\u33C3",
-    "/bqsquare": "\u33C3",
-    "/braceex": "\uF8F4",
-    "/braceleft": "\u007B",
-    "/braceleftbt": "\uF8F3",
-    "/braceleftmid": "\uF8F2",
-    "/braceleftmonospace": "\uFF5B",
-    "/braceleftsmall": "\uFE5B",
-    "/bracelefttp": "\uF8F1",
-    "/braceleftvertical": "\uFE37",
-    "/braceright": "\u007D",
-    "/bracerightbt": "\uF8FE",
-    "/bracerightmid": "\uF8FD",
-    "/bracerightmonospace": "\uFF5D",
-    "/bracerightsmall": "\uFE5C",
-    "/bracerighttp": "\uF8FC",
-    "/bracerightvertical": "\uFE38",
-    "/bracketangledblleft": "\u27EA",
-    "/bracketangledblright": "\u27EB",
-    "/bracketangleleft": "\u27E8",
-    "/bracketangleright": "\u27E9",
-    "/bracketbottomcurly": "\u23DF",
-    "/bracketbottomsquare": "\u23B5",
-    "/bracketcornerupleftsquare": "\u23A1",
-    "/bracketcorneruprightsquare": "\u23A4",
-    "/bracketdottedsubstitutionleft": "\u2E04",
-    "/bracketdottedsubstitutionright": "\u2E05",
-    "/bracketextensioncurly": "\u23AA",
-    "/bracketextensionleftsquare": "\u23A2",
-    "/bracketextensionrightsquare": "\u23A5",
-    "/brackethalfbottomleft": "\u2E24",
-    "/brackethalfbottomright": "\u2E25",
-    "/brackethalftopleft": "\u2E22",
-    "/brackethalftopright": "\u2E23",
-    "/brackethookupleftcurly": "\u23A7",
-    "/brackethookuprightcurly": "\u23AB",
-    "/bracketleft": "\u005B",
-    "/bracketleftbt": "\uF8F0",
-    "/bracketleftex": "\uF8EF",
-    "/bracketleftmonospace": "\uFF3B",
-    "/bracketleftsquarequill": "\u2045",
-    "/bracketlefttp": "\uF8EE",
-    "/bracketlowercornerleftsquare": "\u23A3",
-    "/bracketlowercornerrightsquare": "\u23A6",
-    "/bracketlowerhookleftcurly": "\u23A9",
-    "/bracketlowerhookrightcurly": "\u23AD",
-    "/bracketmiddlepieceleftcurly": "\u23A8",
-    "/bracketmiddlepiecerightcurly": "\u23AC",
-    "/bracketoverbrackettopbottomsquare": "\u23B6",
-    "/bracketparaphraselowleft": "\u2E1C",
-    "/bracketparaphraselowright": "\u2E1D",
-    "/bracketraisedleft": "\u2E0C",
-    "/bracketraisedright": "\u2E0D",
-    "/bracketright": "\u005D",
-    "/bracketrightbt": "\uF8FB",
-    "/bracketrightex": "\uF8FA",
-    "/bracketrightmonospace": "\uFF3D",
-    "/bracketrightsquarequill": "\u2046",
-    "/bracketrighttp": "\uF8F9",
-    "/bracketsectionupleftlowerrightcurly": "\u23B0",
-    "/bracketsectionuprightlowerleftcurly": "\u23B1",
-    "/bracketshellbottom": "\u23E1",
-    "/bracketshelltop": "\u23E0",
-    "/bracketshellwhiteleft": "\u27EC",
-    "/bracketshellwhiteright": "\u27ED",
-    "/bracketsubstitutionleft": "\u2E02",
-    "/bracketsubstitutionright": "\u2E03",
-    "/brackettopcurly": "\u23DE",
-    "/brackettopsquare": "\u23B4",
-    "/brackettranspositionleft": "\u2E09",
-    "/brackettranspositionright": "\u2E0A",
-    "/bracketwhitesquareleft": "\u27E6",
-    "/bracketwhitesquareright": "\u27E7",
-    "/branchbankidentification": "\u2446",
-    "/bread": "\u1F35E",
-    "/breve": "\u02D8",
-    "/brevebelowcmb": "\u032E",
-    "/brevecmb": "\u0306",
-    "/breveinvertedbelowcmb": "\u032F",
-    "/breveinvertedcmb": "\u0311",
-    "/breveinverteddoublecmb": "\u0361",
-    "/brevemetrical": "\u23D1",
-    "/brideVeil": "\u1F470",
-    "/bridgeAtNight": "\u1F309",
-    "/bridgebelowcmb": "\u032A",
-    "/bridgeinvertedbelowcmb": "\u033A",
-    "/briefcase": "\u1F4BC",
-    "/brll:blank": "\u2800",
-    "/brokenHeart": "\u1F494",
-    "/brokenbar": "\u00A6",
-    "/brokencirclenorthwestarrow": "\u238B",
-    "/bstroke": "\u0180",
-    "/bsuperior": "\uF6EA",
-    "/btopbar": "\u0183",
-    "/bug": "\u1F41B",
-    "/buhiragana": "\u3076",
-    "/buildingConstruction": "\u1F3D7",
-    "/bukatakana": "\u30D6",
-    "/bullet": "\u2022",
-    "/bulletinverse": "\u25D8",
-    "/bulletoperator": "\u2219",
-    "/bullhorn": "\u1F56B",
-    "/bullhornSoundWaves": "\u1F56C",
-    "/bullseye": "\u25CE",
-    "/burrito": "\u1F32F",
-    "/bus": "\u1F68C",
-    "/busStop": "\u1F68F",
-    "/bussyerusquare": "\u3334",
-    "/bustInSilhouette": "\u1F464",
-    "/bustsInSilhouette": "\u1F465",
-    "/c": "\u0063",
-    "/caarmenian": "\u056E",
-    "/cabengali": "\u099A",
-    "/cactus": "\u1F335",
-    "/cacute": "\u0107",
-    "/cadauna": "\u2106",
-    "/cadeva": "\u091A",
-    "/caduceus": "\u2624",
-    "/cagujarati": "\u0A9A",
-    "/cagurmukhi": "\u0A1A",
-    "/cakraconsonant": "\uA9BF",
-    "/calendar": "\u1F4C5",
-    "/calfullwidth": "\u3388",
-    "/callideographicparen": "\u323A",
-    "/calsquare": "\u3388",
-    "/camera": "\u1F4F7",
-    "/cameraFlash": "\u1F4F8",
-    "/camping": "\u1F3D5",
-    "/camurda": "\uA996",
-    "/cancellationX": "\u1F5D9",
-    "/cancer": "\u264B",
-    "/candle": "\u1F56F",
-    "/candrabindubengali": "\u0981",
-    "/candrabinducmb": "\u0310",
-    "/candrabindudeva": "\u0901",
-    "/candrabindugujarati": "\u0A81",
-    "/candy": "\u1F36C",
-    "/canoe": "\u1F6F6",
-    "/capitulum": "\u2E3F",
-    "/capricorn": "\u2651",
-    "/capslock": "\u21EA",
-    "/cardFileBox": "\u1F5C3",
-    "/cardIndex": "\u1F4C7",
-    "/cardIndexDividers": "\u1F5C2",
-    "/careof": "\u2105",
-    "/caret": "\u2038",
-    "/caretinsertionpoint": "\u2041",
-    "/carettildedownfunc": "\u2371",
-    "/carettildeupfunc": "\u2372",
-    "/caron": "\u02C7",
-    "/caronbelowcmb": "\u032C",
-    "/caroncmb": "\u030C",
-    "/carouselHorse": "\u1F3A0",
-    "/carpStreamer": "\u1F38F",
-    "/carriagereturn": "\u21B5",
-    "/carsliding": "\u26D0",
-    "/castle": "\u26EB",
-    "/cat": "\u1F408",
-    "/catFace": "\u1F431",
-    "/catFaceWithTearsOfJoy": "\u1F639",
-    "/catFaceWithWrySmile": "\u1F63C",
-    "/caution": "\u2621",
-    "/cbar": "\uA793",
-    "/cbopomofo": "\u3118",
-    "/ccaron": "\u010D",
-    "/ccedilla": "\u00E7",
-    "/ccedillaacute": "\u1E09",
-    "/ccfullwidth": "\u33C4",
-    "/ccircle": "\u24D2",
-    "/ccircumflex": "\u0109",
-    "/ccurl": "\u0255",
-    "/cdfullwidth": "\u33C5",
-    "/cdot": "\u010B",
-    "/cdotaccent": "\u010B",
-    "/cdotreversed": "\uA73F",
-    "/cdsquare": "\u33C5",
-    "/cecak": "\uA981",
-    "/cecaktelu": "\uA9B3",
-    "/cedi": "\u20B5",
-    "/cedilla": "\u00B8",
-    "/cedillacmb": "\u0327",
-    "/ceilingleft": "\u2308",
-    "/ceilingright": "\u2309",
-    "/celticCross": "\u1F548",
-    "/cent": "\u00A2",
-    "/centigrade": "\u2103",
-    "/centinferior": "\uF6DF",
-    "/centmonospace": "\uFFE0",
-    "/centoldstyle": "\uF7A2",
-    "/centreddotwhitediamond": "\u27D0",
-    "/centreideographiccircled": "\u32A5",
-    "/centreline": "\u2104",
-    "/centrelineverticalsquarewhite": "\u2385",
-    "/centsuperior": "\uF6E0",
-    "/ceres": "\u26B3",
-    "/chaarmenian": "\u0579",
-    "/chabengali": "\u099B",
-    "/chadeva": "\u091B",
-    "/chagujarati": "\u0A9B",
-    "/chagurmukhi": "\u0A1B",
-    "/chains": "\u26D3",
-    "/chair": "\u2441",
-    "/chamkocircle": "\u327C",
-    "/charactertie": "\u2040",
-    "/chartDownwardsTrend": "\u1F4C9",
-    "/chartUpwardsTrend": "\u1F4C8",
-    "/chartUpwardsTrendAndYenSign": "\u1F4B9",
-    "/chbopomofo": "\u3114",
-    "/cheabkhasiancyrillic": "\u04BD",
-    "/cheabkhcyr": "\u04BD",
-    "/cheabkhtailcyr": "\u04BF",
-    "/checkbox": "\u2610",
-    "/checkboxchecked": "\u2611",
-    "/checkboxx": "\u2612",
-    "/checkmark": "\u2713",
-    "/checyr": "\u0447",
-    "/checyrillic": "\u0447",
-    "/chedescenderabkhasiancyrillic": "\u04BF",
-    "/chedescendercyrillic": "\u04B7",
-    "/chedieresiscyr": "\u04F5",
-    "/chedieresiscyrillic": "\u04F5",
-    "/cheeringMegaphone": "\u1F4E3",
-    "/cheharmenian": "\u0573",
-    "/chekhakascyr": "\u04CC",
-    "/chekhakassiancyrillic": "\u04CC",
-    "/chequeredFlag": "\u1F3C1",
-    "/cherries": "\u1F352",
-    "/cherryBlossom": "\u1F338",
-    "/chestnut": "\u1F330",
-    "/chetailcyr": "\u04B7",
-    "/chevertcyr": "\u04B9",
-    "/cheverticalstrokecyrillic": "\u04B9",
-    "/chi": "\u03C7",
-    "/chicken": "\u1F414",
-    "/chieuchacirclekorean": "\u3277",
-    "/chieuchaparenkorean": "\u3217",
-    "/chieuchcirclekorean": "\u3269",
-    "/chieuchkorean": "\u314A",
-    "/chieuchparenkorean": "\u3209",
-    "/childrenCrossing": "\u1F6B8",
-    "/chipmunk": "\u1F43F",
-    "/chirho": "\u2627",
-    "/chiron": "\u26B7",
-    "/chochangthai": "\u0E0A",
-    "/chochanthai": "\u0E08",
-    "/chochingthai": "\u0E09",
-    "/chochoethai": "\u0E0C",
-    "/chocolateBar": "\u1F36B",
-    "/chook": "\u0188",
-    "/christmasTree": "\u1F384",
-    "/church": "\u26EA",
-    "/cieucacirclekorean": "\u3276",
-    "/cieucaparenkorean": "\u3216",
-    "/cieuccirclekorean": "\u3268",
-    "/cieuckorean": "\u3148",
-    "/cieucparenkorean": "\u3208",
-    "/cieucuparenkorean": "\u321C",
-    "/cinema": "\u1F3A6",
-    "/circle": "\u25CB",
-    "/circleallbutupperquadrantleftblack": "\u25D5",
-    "/circlebackslashfunc": "\u2349",
-    "/circleblack": "\u25CF",
-    "/circledCrossPommee": "\u1F540",
-    "/circledInformationSource": "\u1F6C8",
-    "/circledasteriskoperator": "\u229B",
-    "/circledbarnotchhorizontal": "\u2389",
-    "/circledcrossinglanes": "\u26D2",
-    "/circleddash": "\u229D",
-    "/circleddivisionslash": "\u2298",
-    "/circleddotoperator": "\u2299",
-    "/circledequals": "\u229C",
-    "/circlediaeresisfunc": "\u2365",
-    "/circledminus": "\u2296",
-    "/circledot": "\u2299",
-    "/circledotrightwhite": "\u2686",
-    "/circledotted": "\u25CC",
-    "/circledringoperator": "\u229A",
-    "/circledtriangledown": "\u238A",
-    "/circlehalfleftblack": "\u25D0",
-    "/circlehalfrightblack": "\u25D1",
-    "/circleinversewhite": "\u25D9",
-    "/circlejotfunc": "\u233E",
-    "/circlelowerhalfblack": "\u25D2",
-    "/circlelowerquadrantleftwhite": "\u25F5",
-    "/circlelowerquadrantrightwhite": "\u25F6",
-    "/circlemultiply": "\u2297",
-    "/circleot": "\u2299",
-    "/circleplus": "\u2295",
-    "/circlepostalmark": "\u3036",
-    "/circlestarfunc": "\u235F",
-    "/circlestilefunc": "\u233D",
-    "/circlestroketwodotsaboveheavy": "\u26E3",
-    "/circletwodotsblackwhite": "\u2689",
-    "/circletwodotswhite": "\u2687",
-    "/circleunderlinefunc": "\u235C",
-    "/circleupperhalfblack": "\u25D3",
-    "/circleupperquadrantleftwhite": "\u25F4",
-    "/circleupperquadrantrightblack": "\u25D4",
-    "/circleupperquadrantrightwhite": "\u25F7",
-    "/circleverticalfill": "\u25CD",
-    "/circlewhite": "\u25CB",
-    "/circlewhitedotrightblack": "\u2688",
-    "/circlewithlefthalfblack": "\u25D0",
-    "/circlewithrighthalfblack": "\u25D1",
-    "/circumflex": "\u02C6",
-    "/circumflexbelowcmb": "\u032D",
-    "/circumflexcmb": "\u0302",
-    "/circumflexlow": "\uA788",
-    "/circusTent": "\u1F3AA",
-    "/cityscape": "\u1F3D9",
-    "/cityscapeAtDusk": "\u1F306",
-    "/cjk:ideographiccomma": "\u3001",
-    "/cjk:tortoiseshellbracketleft": "\u3014",
-    "/cjk:tortoiseshellbracketright": "\u3015",
-    "/clamshellMobilePhone": "\u1F581",
-    "/clapperBoard": "\u1F3AC",
-    "/clappingHandsSign": "\u1F44F",
-    "/classicalBuilding": "\u1F3DB",
-    "/clear": "\u2327",
-    "/clearscreen": "\u239A",
-    "/clickalveolar": "\u01C2",
-    "/clickbilabial": "\u0298",
-    "/clickdental": "\u01C0",
-    "/clicklateral": "\u01C1",
-    "/clickretroflex": "\u01C3",
-    "/clinkingBeerMugs": "\u1F37B",
-    "/clipboard": "\u1F4CB",
-    "/clockFaceEight-thirty": "\u1F563",
-    "/clockFaceEightOclock": "\u1F557",
-    "/clockFaceEleven-thirty": "\u1F566",
-    "/clockFaceElevenOclock": "\u1F55A",
-    "/clockFaceFive-thirty": "\u1F560",
-    "/clockFaceFiveOclock": "\u1F554",
-    "/clockFaceFour-thirty": "\u1F55F",
-    "/clockFaceFourOclock": "\u1F553",
-    "/clockFaceNine-thirty": "\u1F564",
-    "/clockFaceNineOclock": "\u1F558",
-    "/clockFaceOne-thirty": "\u1F55C",
-    "/clockFaceOneOclock": "\u1F550",
-    "/clockFaceSeven-thirty": "\u1F562",
-    "/clockFaceSevenOclock": "\u1F556",
-    "/clockFaceSix-thirty": "\u1F561",
-    "/clockFaceSixOclock": "\u1F555",
-    "/clockFaceTen-thirty": "\u1F565",
-    "/clockFaceTenOclock": "\u1F559",
-    "/clockFaceThree-thirty": "\u1F55E",
-    "/clockFaceThreeOclock": "\u1F552",
-    "/clockFaceTwelve-thirty": "\u1F567",
-    "/clockFaceTwelveOclock": "\u1F55B",
-    "/clockFaceTwo-thirty": "\u1F55D",
-    "/clockFaceTwoOclock": "\u1F551",
-    "/clockwiseDownwardsAndUpwardsOpenCircleArrows": "\u1F503",
-    "/clockwiseRightAndLeftSemicircleArrows": "\u1F5D8",
-    "/clockwiseRightwardsAndLeftwardsOpenCircleArrows": "\u1F501",
-    "/clockwiseRightwardsAndLeftwardsOpenCircleArrowsCircledOneOverlay": "\u1F502",
-    "/closedBook": "\u1F4D5",
-    "/closedLockKey": "\u1F510",
-    "/closedMailboxLoweredFlag": "\u1F4EA",
-    "/closedMailboxRaisedFlag": "\u1F4EB",
-    "/closedUmbrella": "\u1F302",
-    "/closedentryleft": "\u26DC",
-    "/closeup": "\u2050",
-    "/cloud": "\u2601",
-    "/cloudLightning": "\u1F329",
-    "/cloudRain": "\u1F327",
-    "/cloudSnow": "\u1F328",
-    "/cloudTornado": "\u1F32A",
-    "/clsquare": "\u1F191",
-    "/club": "\u2663",
-    "/clubblack": "\u2663",
-    "/clubsuitblack": "\u2663",
-    "/clubsuitwhite": "\u2667",
-    "/clubwhite": "\u2667",
-    "/cm2fullwidth": "\u33A0",
-    "/cm3fullwidth": "\u33A4",
-    "/cmb:a": "\u0363",
-    "/cmb:aaboveflat": "\u1DD3",
-    "/cmb:aboveogonek": "\u1DCE",
-    "/cmb:acute": "\u0301",
-    "/cmb:acutebelow": "\u0317",
-    "/cmb:acutegraveacute": "\u1DC9",
-    "/cmb:acutemacron": "\u1DC7",
-    "/cmb:acutetone": "\u0341",
-    "/cmb:adieresis": "\u1DF2",
-    "/cmb:ae": "\u1DD4",
-    "/cmb:almostequalabove": "\u034C",
-    "/cmb:almostequaltobelow": "\u1DFD",
-    "/cmb:alpha": "\u1DE7",
-    "/cmb:ao": "\u1DD5",
-    "/cmb:arrowheadleftbelow": "\u0354",
-    "/cmb:arrowheadrightabove": "\u0350",
-    "/cmb:arrowheadrightarrowheadupbelow": "\u0356",
-    "/cmb:arrowheadrightbelow": "\u0355",
-    "/cmb:arrowleftrightbelow": "\u034D",
-    "/cmb:arrowrightdoublebelow": "\u0362",
-    "/cmb:arrowupbelow": "\u034E",
-    "/cmb:asteriskbelow": "\u0359",
-    "/cmb:av": "\u1DD6",
-    "/cmb:b": "\u1DE8",
-    "/cmb:belowbreve": "\u032E",
-    "/cmb:beta": "\u1DE9",
-    "/cmb:breve": "\u0306",
-    "/cmb:brevemacron": "\u1DCB",
-    "/cmb:bridgeabove": "\u0346",
-    "/cmb:bridgebelow": "\u032A",
-    "/cmb:c": "\u0368",
-    "/cmb:candrabindu": "\u0310",
-    "/cmb:caron": "\u030C",
-    "/cmb:caronbelow": "\u032C",
-    "/cmb:ccedilla": "\u1DD7",
-    "/cmb:cedilla": "\u0327",
-    "/cmb:circumflex": "\u0302",
-    "/cmb:circumflexbelow": "\u032D",
-    "/cmb:commaaccentbelow": "\u0326",
-    "/cmb:commaturnedabove": "\u0312",
-    "/cmb:d": "\u0369",
-    "/cmb:dblarchinvertedbelow": "\u032B",
-    "/cmb:dbloverline": "\u033F",
-    "/cmb:dblverticallineabove": "\u030E",
-    "/cmb:dblverticallinebelow": "\u0348",
-    "/cmb:deletionmark": "\u1DFB",
-    "/cmb:dialytikatonos": "\u0344",
-    "/cmb:dieresis": "\u0308",
-    "/cmb:dieresisbelow": "\u0324",
-    "/cmb:dotaboveleft": "\u1DF8",
-    "/cmb:dotaccent": "\u0307",
-    "/cmb:dotbelowcomb": "\u0323",
-    "/cmb:dotrightabove": "\u0358",
-    "/cmb:dottedacute": "\u1DC1",
-    "/cmb:dottedgrave": "\u1DC0",
-    "/cmb:doubleabovecircumflex": "\u1DCD",
-    "/cmb:doublebelowbreve": "\u035C",
-    "/cmb:doublebreve": "\u035D",
-    "/cmb:doubleinvertedbelowbreve": "\u1DFC",
-    "/cmb:doubleringbelow": "\u035A",
-    "/cmb:downtackbelow": "\u031E",
-    "/cmb:e": "\u0364",
-    "/cmb:equalbelow": "\u0347",
-    "/cmb:esh": "\u1DEF",
-    "/cmb:eth": "\u1DD9",
-    "/cmb:f": "\u1DEB",
-    "/cmb:fermata": "\u0352",
-    "/cmb:g": "\u1DDA",
-    "/cmb:graphemejoiner": "\u034F",
-    "/cmb:grave": "\u0300",
-    "/cmb:graveacutegrave": "\u1DC8",
-    "/cmb:gravebelow": "\u0316",
-    "/cmb:gravedouble": "\u030F",
-    "/cmb:gravemacron": "\u1DC5",
-    "/cmb:gravetone": "\u0340",
-    "/cmb:gsmall": "\u1DDB",
-    "/cmb:h": "\u036A",
-    "/cmb:halfleftringabove": "\u0351",
-    "/cmb:halfleftringbelow": "\u031C",
-    "/cmb:halfrightringabove": "\u0357",
-    "/cmb:halfrightringbelow": "\u0339",
-    "/cmb:homotheticabove": "\u034B",
-    "/cmb:hookabove": "\u0309",
-    "/cmb:horn": "\u031B",
-    "/cmb:hungarumlaut": "\u030B",
-    "/cmb:i": "\u0365",
-    "/cmb:insulard": "\u1DD8",
-    "/cmb:invertedbelowbreve": "\u032F",
-    "/cmb:invertedbreve": "\u0311",
-    "/cmb:invertedbridgebelow": "\u033A",
-    "/cmb:inverteddoublebreve": "\u0361",
-    "/cmb:iotasub": "\u0345",
-    "/cmb:isbelow": "\u1DD0",
-    "/cmb:k": "\u1DDC",
-    "/cmb:kavykaaboveleft": "\u1DF7",
-    "/cmb:kavykaaboveright": "\u1DF6",
-    "/cmb:koronis": "\u0343",
-    "/cmb:l": "\u1DDD",
-    "/cmb:leftangleabove": "\u031A",
-    "/cmb:leftanglebelow": "\u0349",
-    "/cmb:leftarrowheadabove": "\u1DFE",
-    "/cmb:lefttackbelow": "\u0318",
-    "/cmb:lineverticalabove": "\u030D",
-    "/cmb:lineverticalbelow": "\u0329",
-    "/cmb:longs": "\u1DE5",
-    "/cmb:lowline": "\u0332",
-    "/cmb:lowlinedouble": "\u0333",
-    "/cmb:lsmall": "\u1DDE",
-    "/cmb:lwithdoublemiddletilde": "\u1DEC",
-    "/cmb:m": "\u036B",
-    "/cmb:macron": "\u0304",
-    "/cmb:macronacute": "\u1DC4",
-    "/cmb:macronbelow": "\u0331",
-    "/cmb:macronbreve": "\u1DCC",
-    "/cmb:macrondouble": "\u035E",
-    "/cmb:macrondoublebelow": "\u035F",
-    "/cmb:macrongrave": "\u1DC6",
-    "/cmb:minusbelow": "\u0320",
-    "/cmb:msmall": "\u1DDF",
-    "/cmb:n": "\u1DE0",
-    "/cmb:nottildeabove": "\u034A",
-    "/cmb:nsmall": "\u1DE1",
-    "/cmb:o": "\u0366",
-    "/cmb:odieresis": "\u1DF3",
-    "/cmb:ogonek": "\u0328",
-    "/cmb:overlaystrokelong": "\u0336",
-    "/cmb:overlaystrokeshort": "\u0335",
-    "/cmb:overline": "\u0305",
-    "/cmb:owithlightcentralizationstroke": "\u1DED",
-    "/cmb:p": "\u1DEE",
-    "/cmb:palatalizedhookbelow": "\u0321",
-    "/cmb:perispomeni": "\u0342",
-    "/cmb:plusbelow": "\u031F",
-    "/cmb:r": "\u036C",
-    "/cmb:rbelow": "\u1DCA",
-    "/cmb:retroflexhookbelow": "\u0322",
-    "/cmb:reversedcommaabove": "\u0314",
-    "/cmb:rightarrowheadanddownarrowheadbelow": "\u1DFF",
-    "/cmb:righttackbelow": "\u0319",
-    "/cmb:ringabove": "\u030A",
-    "/cmb:ringbelow": "\u0325",
-    "/cmb:rrotunda": "\u1DE3",
-    "/cmb:rsmall": "\u1DE2",
-    "/cmb:s": "\u1DE4",
-    "/cmb:schwa": "\u1DEA",
-    "/cmb:seagullbelow": "\u033C",
-    "/cmb:snakebelow": "\u1DC2",
-    "/cmb:soliduslongoverlay": "\u0338",
-    "/cmb:solidusshortoverlay": "\u0337",
-    "/cmb:squarebelow": "\u033B",
-    "/cmb:suspensionmark": "\u1DC3",
-    "/cmb:t": "\u036D",
-    "/cmb:tilde": "\u0303",
-    "/cmb:tildebelow": "\u0330",
-    "/cmb:tildedouble": "\u0360",
-    "/cmb:tildeoverlay": "\u0334",
-    "/cmb:tildevertical": "\u033E",
-    "/cmb:turnedabove": "\u0313",
-    "/cmb:turnedcommaabove": "\u0315",
-    "/cmb:u": "\u0367",
-    "/cmb:udieresis": "\u1DF4",
-    "/cmb:uptackabove": "\u1DF5",
-    "/cmb:uptackbelow": "\u031D",
-    "/cmb:urabove": "\u1DD1",
-    "/cmb:usabove": "\u1DD2",
-    "/cmb:uwithlightcentralizationstroke": "\u1DF0",
-    "/cmb:v": "\u036E",
-    "/cmb:w": "\u1DF1",
-    "/cmb:wideinvertedbridgebelow": "\u1DF9",
-    "/cmb:x": "\u036F",
-    "/cmb:xabove": "\u033D",
-    "/cmb:xbelow": "\u0353",
-    "/cmb:z": "\u1DE6",
-    "/cmb:zigzagabove": "\u035B",
-    "/cmb:zigzagbelow": "\u1DCF",
-    "/cmcubedsquare": "\u33A4",
-    "/cmfullwidth": "\u339D",
-    "/cmonospace": "\uFF43",
-    "/cmsquaredsquare": "\u33A0",
-    "/cntr:acknowledge": "\u2406",
-    "/cntr:backspace": "\u2408",
-    "/cntr:bell": "\u2407",
-    "/cntr:blank": "\u2422",
-    "/cntr:cancel": "\u2418",
-    "/cntr:carriagereturn": "\u240D",
-    "/cntr:datalinkescape": "\u2410",
-    "/cntr:delete": "\u2421",
-    "/cntr:deleteformtwo": "\u2425",
-    "/cntr:devicecontrolfour": "\u2414",
-    "/cntr:devicecontrolone": "\u2411",
-    "/cntr:devicecontrolthree": "\u2413",
-    "/cntr:devicecontroltwo": "\u2412",
-    "/cntr:endofmedium": "\u2419",
-    "/cntr:endoftext": "\u2403",
-    "/cntr:endoftransmission": "\u2404",
-    "/cntr:endoftransmissionblock": "\u2417",
-    "/cntr:enquiry": "\u2405",
-    "/cntr:escape": "\u241B",
-    "/cntr:fileseparator": "\u241C",
-    "/cntr:formfeed": "\u240C",
-    "/cntr:groupseparator": "\u241D",
-    "/cntr:horizontaltab": "\u2409",
-    "/cntr:linefeed": "\u240A",
-    "/cntr:negativeacknowledge": "\u2415",
-    "/cntr:newline": "\u2424",
-    "/cntr:null": "\u2400",
-    "/cntr:openbox": "\u2423",
-    "/cntr:recordseparator": "\u241E",
-    "/cntr:shiftin": "\u240F",
-    "/cntr:shiftout": "\u240E",
-    "/cntr:space": "\u2420",
-    "/cntr:startofheading": "\u2401",
-    "/cntr:startoftext": "\u2402",
-    "/cntr:substitute": "\u241A",
-    "/cntr:substituteformtwo": "\u2426",
-    "/cntr:synchronousidle": "\u2416",
-    "/cntr:unitseparator": "\u241F",
-    "/cntr:verticaltab": "\u240B",
-    "/coarmenian": "\u0581",
-    "/cocktailGlass": "\u1F378",
-    "/coffin": "\u26B0",
-    "/cofullwidth": "\u33C7",
-    "/collision": "\u1F4A5",
-    "/colon": "\u003A",
-    "/colonequals": "\u2254",
-    "/colonmod": "\uA789",
-    "/colonmonetary": "\u20A1",
-    "/colonmonospace": "\uFF1A",
-    "/colonraisedmod": "\u02F8",
-    "/colonsign": "\u20A1",
-    "/colonsmall": "\uFE55",
-    "/colontriangularhalfmod": "\u02D1",
-    "/colontriangularmod": "\u02D0",
-    "/comet": "\u2604",
-    "/comma": "\u002C",
-    "/commaabovecmb": "\u0313",
-    "/commaaboverightcmb": "\u0315",
-    "/commaaccent": "\uF6C3",
-    "/commaarabic": "\u060C",
-    "/commaarmenian": "\u055D",
-    "/commabarfunc": "\u236A",
-    "/commainferior": "\uF6E1",
-    "/commamonospace": "\uFF0C",
-    "/commaraised": "\u2E34",
-    "/commareversed": "\u2E41",
-    "/commareversedabovecmb": "\u0314",
-    "/commareversedmod": "\u02BD",
-    "/commasmall": "\uFE50",
-    "/commasuperior": "\uF6E2",
-    "/commaturnedabovecmb": "\u0312",
-    "/commaturnedmod": "\u02BB",
-    "/commercialat": "\uFE6B",
-    "/commercialminussign": "\u2052",
-    "/compass": "\u263C",
-    "/complement": "\u2201",
-    "/composition": "\u2384",
-    "/compression": "\u1F5DC",
-    "/con": "\uA76F",
-    "/confettiBall": "\u1F38A",
-    "/confoundedFace": "\u1F616",
-    "/confusedFace": "\u1F615",
-    "/congratulationideographiccircled": "\u3297",
-    "/congratulationideographicparen": "\u3237",
-    "/congruent": "\u2245",
-    "/conicaltaper": "\u2332",
-    "/conjunction": "\u260C",
-    "/consquareupblack": "\u26FE",
-    "/constructionSign": "\u1F6A7",
-    "/constructionWorker": "\u1F477",
-    "/containsasmembersmall": "\u220D",
-    "/containsasnormalsubgroorequalup": "\u22B5",
-    "/containsasnormalsubgroup": "\u22B3",
-    "/containslonghorizontalstroke": "\u22FA",
-    "/containsoverbar": "\u22FD",
-    "/containsoverbarsmall": "\u22FE",
-    "/containssmallverticalbarhorizontalstroke": "\u22FC",
-    "/containsverticalbarhorizontalstroke": "\u22FB",
-    "/continuousunderline": "\u2381",
-    "/contourintegral": "\u222E",
-    "/control": "\u2303",
-    "/controlACK": "\u0006",
-    "/controlBEL": "\u0007",
-    "/controlBS": "\u0008",
-    "/controlCAN": "\u0018",
-    "/controlCR": "\u000D",
-    "/controlDC1": "\u0011",
-    "/controlDC2": "\u0012",
-    "/controlDC3": "\u0013",
-    "/controlDC4": "\u0014",
-    "/controlDEL": "\u007F",
-    "/controlDLE": "\u0010",
-    "/controlEM": "\u0019",
-    "/controlENQ": "\u0005",
-    "/controlEOT": "\u0004",
-    "/controlESC": "\u001B",
-    "/controlETB": "\u0017",
-    "/controlETX": "\u0003",
-    "/controlFF": "\u000C",
-    "/controlFS": "\u001C",
-    "/controlGS": "\u001D",
-    "/controlHT": "\u0009",
-    "/controlKnobs": "\u1F39B",
-    "/controlLF": "\u000A",
-    "/controlNAK": "\u0015",
-    "/controlRS": "\u001E",
-    "/controlSI": "\u000F",
-    "/controlSO": "\u000E",
-    "/controlSOT": "\u0002",
-    "/controlSTX": "\u0001",
-    "/controlSUB": "\u001A",
-    "/controlSYN": "\u0016",
-    "/controlUS": "\u001F",
-    "/controlVT": "\u000B",
-    "/convavediamondwhite": "\u27E1",
-    "/convenienceStore": "\u1F3EA",
-    "/cookedRice": "\u1F35A",
-    "/cookie": "\u1F36A",
-    "/cooking": "\u1F373",
-    "/coolsquare": "\u1F192",
-    "/coproductarray": "\u2210",
-    "/copyideographiccircled": "\u32A2",
-    "/copyright": "\u00A9",
-    "/copyrightsans": "\uF8E9",
-    "/copyrightserif": "\uF6D9",
-    "/cornerbottomleft": "\u231E",
-    "/cornerbottomright": "\u231F",
-    "/cornerbracketleft": "\u300C",
-    "/cornerbracketlefthalfwidth": "\uFF62",
-    "/cornerbracketleftvertical": "\uFE41",
-    "/cornerbracketright": "\u300D",
-    "/cornerbracketrighthalfwidth": "\uFF63",
-    "/cornerbracketrightvertical": "\uFE42",
-    "/cornerdotupleft": "\u27D4",
-    "/cornertopleft": "\u231C",
-    "/cornertopright": "\u231D",
-    "/coroniseditorial": "\u2E0E",
-    "/corporationsquare": "\u337F",
-    "/correctideographiccircled": "\u32A3",
-    "/corresponds": "\u2258",
-    "/cosquare": "\u33C7",
-    "/couchAndLamp": "\u1F6CB",
-    "/counterbore": "\u2334",
-    "/countersink": "\u2335",
-    "/coupleHeart": "\u1F491",
-    "/coverkgfullwidth": "\u33C6",
-    "/coverkgsquare": "\u33C6",
-    "/cow": "\u1F404",
-    "/cowFace": "\u1F42E",
-    "/cpalatalhook": "\uA794",
-    "/cparen": "\u249E",
-    "/cparenthesized": "\u249E",
-    "/creditCard": "\u1F4B3",
-    "/crescentMoon": "\u1F319",
-    "/creversed": "\u2184",
-    "/cricketBatAndBall": "\u1F3CF",
-    "/crocodile": "\u1F40A",
-    "/cropbottomleft": "\u230D",
-    "/cropbottomright": "\u230C",
-    "/croptopleft": "\u230F",
-    "/croptopright": "\u230E",
-    "/crossPommee": "\u1F542",
-    "/crossPommeeHalf-circleBelow": "\u1F541",
-    "/crossedFlags": "\u1F38C",
-    "/crossedswords": "\u2694",
-    "/crossinglanes": "\u26CC",
-    "/crossmod": "\u02DF",
-    "/crossofjerusalem": "\u2629",
-    "/crossoflorraine": "\u2628",
-    "/crossonshieldblack": "\u26E8",
-    "/crown": "\u1F451",
-    "/crrn:rupee": "\u20A8",
-    "/cruzeiro": "\u20A2",
-    "/cryingCatFace": "\u1F63F",
-    "/cryingFace": "\u1F622",
-    "/crystalBall": "\u1F52E",
-    "/cstretched": "\u0297",
-    "/cstroke": "\u023C",
-    "/cuatrillo": "\uA72D",
-    "/cuatrillocomma": "\uA72F",
-    "/curlyand": "\u22CF",
-    "/curlylogicaland": "\u22CF",
-    "/curlylogicalor": "\u22CE",
-    "/curlyor": "\u22CE",
-    "/currency": "\u00A4",
-    "/currencyExchange": "\u1F4B1",
-    "/curryAndRice": "\u1F35B",
-    "/custard": "\u1F36E",
-    "/customeraccountnumber": "\u2449",
-    "/customs": "\u1F6C3",
-    "/cyclone": "\u1F300",
-    "/cylindricity": "\u232D",
-    "/cyrBreve": "\uF6D1",
-    "/cyrFlex": "\uF6D2",
-    "/cyrbreve": "\uF6D4",
-    "/cyrflex": "\uF6D5",
-    "/d": "\u0064",
-    "/daarmenian": "\u0564",
-    "/daasusquare": "\u3324",
-    "/dabengali": "\u09A6",
-    "/dad": "\u0636",
-    "/dad.fina": "\uFEBE",
-    "/dad.init": "\uFEBF",
-    "/dad.init_alefmaksura.fina": "\uFD07",
-    "/dad.init_hah.fina": "\uFC23",
-    "/dad.init_hah.medi": "\uFCB5",
-    "/dad.init_jeem.fina": "\uFC22",
-    "/dad.init_jeem.medi": "\uFCB4",
-    "/dad.init_khah.fina": "\uFC24",
-    "/dad.init_khah.medi": "\uFCB6",
-    "/dad.init_khah.medi_meem.medi": "\uFD70",
-    "/dad.init_meem.fina": "\uFC25",
-    "/dad.init_meem.medi": "\uFCB7",
-    "/dad.init_reh.fina": "\uFD10",
-    "/dad.init_yeh.fina": "\uFD08",
-    "/dad.isol": "\uFEBD",
-    "/dad.medi": "\uFEC0",
-    "/dad.medi_alefmaksura.fina": "\uFD23",
-    "/dad.medi_hah.medi_alefmaksura.fina": "\uFD6E",
-    "/dad.medi_hah.medi_yeh.fina": "\uFDAB",
-    "/dad.medi_khah.medi_meem.fina": "\uFD6F",
-    "/dad.medi_reh.fina": "\uFD2C",
-    "/dad.medi_yeh.fina": "\uFD24",
-    "/dadarabic": "\u0636",
-    "/daddotbelow": "\u06FB",
-    "/dadeva": "\u0926",
-    "/dadfinalarabic": "\uFEBE",
-    "/dadinitialarabic": "\uFEBF",
-    "/dadmedialarabic": "\uFEC0",
-    "/dafullwidth": "\u3372",
-    "/dagesh": "\u05BC",
-    "/dagesh:hb": "\u05BC",
-    "/dageshhebrew": "\u05BC",
-    "/dagger": "\u2020",
-    "/daggerKnife": "\u1F5E1",
-    "/daggerdbl": "\u2021",
-    "/daggerwithguardleft": "\u2E36",
-    "/daggerwithguardright": "\u2E37",
-    "/dagujarati": "\u0AA6",
-    "/dagurmukhi": "\u0A26",
-    "/dahal": "\u068C",
-    "/dahal.fina": "\uFB85",
-    "/dahal.isol": "\uFB84",
-    "/dahiragana": "\u3060",
-    "/dakatakana": "\u30C0",
-    "/dal": "\u062F",
-    "/dal.fina": "\uFEAA",
-    "/dal.isol": "\uFEA9",
-    "/dalInvertedSmallVBelow": "\u075A",
-    "/dalTwoDotsVerticallyBelowSmallTah": "\u0759",
-    "/dalarabic": "\u062F",
-    "/daldotbelow": "\u068A",
-    "/daldotbelowtahsmall": "\u068B",
-    "/daldownthreedotsabove": "\u068F",
-    "/dalet": "\u05D3",
-    "/dalet:hb": "\u05D3",
-    "/daletdagesh": "\uFB33",
-    "/daletdageshhebrew": "\uFB33",
-    "/dalethatafpatah": "\u05D3",
-    "/dalethatafpatahhebrew": "\u05D3",
-    "/dalethatafsegol": "\u05D3",
-    "/dalethatafsegolhebrew": "\u05D3",
-    "/dalethebrew": "\u05D3",
-    "/dalethiriq": "\u05D3",
-    "/dalethiriqhebrew": "\u05D3",
-    "/daletholam": "\u05D3",
-    "/daletholamhebrew": "\u05D3",
-    "/daletpatah": "\u05D3",
-    "/daletpatahhebrew": "\u05D3",
-    "/daletqamats": "\u05D3",
-    "/daletqamatshebrew": "\u05D3",
-    "/daletqubuts": "\u05D3",
-    "/daletqubutshebrew": "\u05D3",
-    "/daletsegol": "\u05D3",
-    "/daletsegolhebrew": "\u05D3",
-    "/daletsheva": "\u05D3",
-    "/daletshevahebrew": "\u05D3",
-    "/dalettsere": "\u05D3",
-    "/dalettserehebrew": "\u05D3",
-    "/daletwide:hb": "\uFB22",
-    "/daletwithdagesh:hb": "\uFB33",
-    "/dalfinalarabic": "\uFEAA",
-    "/dalfourdotsabove": "\u0690",
-    "/dalinvertedV": "\u06EE",
-    "/dalring": "\u0689",
-    "/damahaprana": "\uA9A3",
-    "/damma": "\u064F",
-    "/dammaIsol": "\uFE78",
-    "/dammaMedi": "\uFE79",
-    "/dammaarabic": "\u064F",
-    "/dammalowarabic": "\u064F",
-    "/dammareversed": "\u065D",
-    "/dammasmall": "\u0619",
-    "/dammatan": "\u064C",
-    "/dammatanIsol": "\uFE72",
-    "/dammatanaltonearabic": "\u064C",
-    "/dammatanarabic": "\u064C",
-    "/dancer": "\u1F483",
-    "/danda": "\u0964",
-    "/dango": "\u1F361",
-    "/darga:hb": "\u05A7",
-    "/dargahebrew": "\u05A7",
-    "/dargalefthebrew": "\u05A7",
-    "/darkShade": "\u2593",
-    "/darkSunglasses": "\u1F576",
-    "/dashwithupturnleft": "\u2E43",
-    "/dasiacmbcyr": "\u0485",
-    "/dasiapneumatacyrilliccmb": "\u0485",
-    "/dateseparator": "\u060D",
-    "/dayeighteentelegraph": "\u33F1",
-    "/dayeighttelegraph": "\u33E7",
-    "/dayeleventelegraph": "\u33EA",
-    "/dayfifteentelegraph": "\u33EE",
-    "/dayfivetelegraph": "\u33E4",
-    "/dayfourteentelegraph": "\u33ED",
-    "/dayfourtelegraph": "\u33E3",
-    "/daynineteentelegraph": "\u33F2",
-    "/dayninetelegraph": "\u33E8",
-    "/dayonetelegraph": "\u33E0",
-    "/dayseventeentelegraph": "\u33F0",
-    "/dayseventelegraph": "\u33E6",
-    "/daysixteentelegraph": "\u33EF",
-    "/daysixtelegraph": "\u33E5",
-    "/daytentelegraph": "\u33E9",
-    "/daythirteentelegraph": "\u33EC",
-    "/daythirtyonetelegraph": "\u33FE",
-    "/daythirtytelegraph": "\u33FD",
-    "/daythreetelegraph": "\u33E2",
-    "/daytwelvetelegraph": "\u33EB",
-    "/daytwentyeighttelegraph": "\u33FB",
-    "/daytwentyfivetelegraph": "\u33F8",
-    "/daytwentyfourtelegraph": "\u33F7",
-    "/daytwentyninetelegraph": "\u33FC",
-    "/daytwentyonetelegraph": "\u33F4",
-    "/daytwentyseventelegraph": "\u33FA",
-    "/daytwentysixtelegraph": "\u33F9",
-    "/daytwentytelegraph": "\u33F3",
-    "/daytwentythreetelegraph": "\u33F6",
-    "/daytwentytwotelegraph": "\u33F5",
-    "/daytwotelegraph": "\u33E1",
-    "/dbdigraph": "\u0238",
-    "/dbfullwidth": "\u33C8",
-    "/dblGrave": "\uF6D3",
-    "/dblanglebracketleft": "\u300A",
-    "/dblanglebracketleftvertical": "\uFE3D",
-    "/dblanglebracketright": "\u300B",
-    "/dblanglebracketrightvertical": "\uFE3E",
-    "/dblarchinvertedbelowcmb": "\u032B",
-    "/dblarrowNE": "\u21D7",
-    "/dblarrowNW": "\u21D6",
-    "/dblarrowSE": "\u21D8",
-    "/dblarrowSW": "\u21D9",
-    "/dblarrowdown": "\u21D3",
-    "/dblarrowleft": "\u21D4",
-    "/dblarrowleftright": "\u21D4",
-    "/dblarrowleftrightstroke": "\u21CE",
-    "/dblarrowleftstroke": "\u21CD",
-    "/dblarrowright": "\u21D2",
-    "/dblarrowrightstroke": "\u21CF",
-    "/dblarrowup": "\u21D1",
-    "/dblarrowupdown": "\u21D5",
-    "/dbldanda": "\u0965",
-    "/dbldnhorz": "\u2566",
-    "/dbldnleft": "\u2557",
-    "/dbldnright": "\u2554",
-    "/dblgrave": "\uF6D6",
-    "/dblgravecmb": "\u030F",
-    "/dblhorz": "\u2550",
-    "/dblintegral": "\u222C",
-    "/dbllowline": "\u2017",
-    "/dbllowlinecmb": "\u0333",
-    "/dbloverlinecmb": "\u033F",
-    "/dblprimemod": "\u02BA",
-    "/dblstrokearrowdown": "\u21DF",
-    "/dblstrokearrowup": "\u21DE",
-    "/dbluphorz": "\u2569",
-    "/dblupleft": "\u255D",
-    "/dblupright": "\u255A",
-    "/dblvert": "\u2551",
-    "/dblverthorz": "\u256C",
-    "/dblverticalbar": "\u2016",
-    "/dblverticallineabovecmb": "\u030E",
-    "/dblvertleft": "\u2563",
-    "/dblvertright": "\u2560",
-    "/dbopomofo": "\u3109",
-    "/dbsquare": "\u33C8",
-    "/dcaron": "\u010F",
-    "/dcedilla": "\u1E11",
-    "/dchecyr": "\u052D",
-    "/dcircle": "\u24D3",
-    "/dcircumflexbelow": "\u1E13",
-    "/dcroat": "\u0111",
-    "/dcurl": "\u0221",
-    "/ddabengali": "\u09A1",
-    "/ddadeva": "\u0921",
-    "/ddagujarati": "\u0AA1",
-    "/ddagurmukhi": "\u0A21",
-    "/ddahal": "\u068D",
-    "/ddahal.fina": "\uFB83",
-    "/ddahal.isol": "\uFB82",
-    "/ddal": "\u0688",
-    "/ddal.fina": "\uFB89",
-    "/ddal.isol": "\uFB88",
-    "/ddalarabic": "\u0688",
-    "/ddalfinalarabic": "\uFB89",
-    "/ddamahaprana": "\uA99E",
-    "/ddblstruckitalic": "\u2146",
-    "/dddhadeva": "\u095C",
-    "/ddhabengali": "\u09A2",
-    "/ddhadeva": "\u0922",
-    "/ddhagujarati": "\u0AA2",
-    "/ddhagurmukhi": "\u0A22",
-    "/ddot": "\u1E0B",
-    "/ddotaccent": "\u1E0B",
-    "/ddotbelow": "\u1E0D",
-    "/decembertelegraph": "\u32CB",
-    "/deciduousTree": "\u1F333",
-    "/decimalexponent": "\u23E8",
-    "/decimalseparatorarabic": "\u066B",
-    "/decimalseparatorpersian": "\u066B",
-    "/decreaseFontSize": "\u1F5DB",
-    "/decyr": "\u0434",
-    "/decyrillic": "\u0434",
-    "/degree": "\u00B0",
-    "/degreecelsius": "\u2103",
-    "/degreefahrenheit": "\u2109",
-    "/dehi:hb": "\u05AD",
-    "/dehihebrew": "\u05AD",
-    "/dehiragana": "\u3067",
-    "/deicoptic": "\u03EF",
-    "/dekatakana": "\u30C7",
-    "/dekomicyr": "\u0501",
-    "/deldiaeresisfunc": "\u2362",
-    "/deleteleft": "\u232B",
-    "/deleteright": "\u2326",
-    "/deliveryTruck": "\u1F69A",
-    "/delstilefunc": "\u2352",
-    "/delta": "\u03B4",
-    "/deltaequal": "\u225C",
-    "/deltastilefunc": "\u234B",
-    "/deltaturned": "\u018D",
-    "/deltaunderlinefunc": "\u2359",
-    "/deltildefunc": "\u236B",
-    "/denominatorminusonenumeratorbengali": "\u09F8",
-    "/dentistrybottomverticalleft": "\u23CC",
-    "/dentistrybottomverticalright": "\u23BF",
-    "/dentistrycircledownhorizontal": "\u23C1",
-    "/dentistrycircleuphorizontal": "\u23C2",
-    "/dentistrycirclevertical": "\u23C0",
-    "/dentistrydownhorizontal": "\u23C9",
-    "/dentistrytopverticalleft": "\u23CB",
-    "/dentistrytopverticalright": "\u23BE",
-    "/dentistrytriangledownhorizontal": "\u23C4",
-    "/dentistrytriangleuphorizontal": "\u23C5",
-    "/dentistrytrianglevertical": "\u23C3",
-    "/dentistryuphorizontal": "\u23CA",
-    "/dentistrywavedownhorizontal": "\u23C7",
-    "/dentistrywaveuphorizontal": "\u23C8",
-    "/dentistrywavevertical": "\u23C6",
-    "/departmentStore": "\u1F3EC",
-    "/derelictHouseBuilding": "\u1F3DA",
-    "/desert": "\u1F3DC",
-    "/desertIsland": "\u1F3DD",
-    "/desisquare": "\u3325",
-    "/desktopComputer": "\u1F5A5",
-    "/desktopWindow": "\u1F5D4",
-    "/deva:a": "\u0905",
-    "/deva:aa": "\u0906",
-    "/deva:aasign": "\u093E",
-    "/deva:abbreviation": "\u0970",
-    "/deva:acandra": "\u0972",
-    "/deva:acute": "\u0954",
-    "/deva:ai": "\u0910",
-    "/deva:aisign": "\u0948",
-    "/deva:anudatta": "\u0952",
-    "/deva:anusvara": "\u0902",
-    "/deva:ashort": "\u0904",
-    "/deva:au": "\u0914",
-    "/deva:ausign": "\u094C",
-    "/deva:avagraha": "\u093D",
-    "/deva:aw": "\u0975",
-    "/deva:awsign": "\u094F",
-    "/deva:ba": "\u092C",
-    "/deva:bba": "\u097F",
-    "/deva:bha": "\u092D",
-    "/deva:ca": "\u091A",
-    "/deva:candrabindu": "\u0901",
-    "/deva:candrabinduinverted": "\u0900",
-    "/deva:cha": "\u091B",
-    "/deva:da": "\u0926",
-    "/deva:danda": "\u0964",
-    "/deva:dbldanda": "\u0965",
-    "/deva:dda": "\u0921",
-    "/deva:ddda": "\u097E",
-    "/deva:dddha": "\u095C",
-    "/deva:ddha": "\u0922",
-    "/deva:dha": "\u0927",
-    "/deva:dothigh": "\u0971",
-    "/deva:e": "\u090F",
-    "/deva:ecandra": "\u090D",
-    "/deva:eight": "\u096E",
-    "/deva:eshort": "\u090E",
-    "/deva:esign": "\u0947",
-    "/deva:esigncandra": "\u0945",
-    "/deva:esignprishthamatra": "\u094E",
-    "/deva:esignshort": "\u0946",
-    "/deva:fa": "\u095E",
-    "/deva:five": "\u096B",
-    "/deva:four": "\u096A",
-    "/deva:ga": "\u0917",
-    "/deva:gga": "\u097B",
-    "/deva:gha": "\u0918",
-    "/deva:ghha": "\u095A",
-    "/deva:glottalstop": "\u097D",
-    "/deva:grave": "\u0953",
-    "/deva:ha": "\u0939",
-    "/deva:i": "\u0907",
-    "/deva:ii": "\u0908",
-    "/deva:iisign": "\u0940",
-    "/deva:isign": "\u093F",
-    "/deva:ja": "\u091C",
-    "/deva:jha": "\u091D",
-    "/deva:jja": "\u097C",
-    "/deva:ka": "\u0915",
-    "/deva:kha": "\u0916",
-    "/deva:khha": "\u0959",
-    "/deva:la": "\u0932",
-    "/deva:lla": "\u0933",
-    "/deva:llla": "\u0934",
-    "/deva:llvocal": "\u0961",
-    "/deva:llvocalsign": "\u0963",
-    "/deva:lvocal": "\u090C",
-    "/deva:lvocalsign": "\u0962",
-    "/deva:ma": "\u092E",
-    "/deva:marwaridda": "\u0978",
-    "/deva:na": "\u0928",
-    "/deva:nga": "\u0919",
-    "/deva:nine": "\u096F",
-    "/deva:nna": "\u0923",
-    "/deva:nnna": "\u0929",
-    "/deva:nukta": "\u093C",
-    "/deva:nya": "\u091E",
-    "/deva:o": "\u0913",
-    "/deva:ocandra": "\u0911",
-    "/deva:oe": "\u0973",
-    "/deva:oesign": "\u093A",
-    "/deva:om": "\u0950",
-    "/deva:one": "\u0967",
-    "/deva:ooe": "\u0974",
-    "/deva:ooesign": "\u093B",
-    "/deva:oshort": "\u0912",
-    "/deva:osign": "\u094B",
-    "/deva:osigncandra": "\u0949",
-    "/deva:osignshort": "\u094A",
-    "/deva:pa": "\u092A",
-    "/deva:pha": "\u092B",
-    "/deva:qa": "\u0958",
-    "/deva:ra": "\u0930",
-    "/deva:rha": "\u095D",
-    "/deva:rra": "\u0931",
-    "/deva:rrvocal": "\u0960",
-    "/deva:rrvocalsign": "\u0944",
-    "/deva:rvocal": "\u090B",
-    "/deva:rvocalsign": "\u0943",
-    "/deva:sa": "\u0938",
-    "/deva:seven": "\u096D",
-    "/deva:sha": "\u0936",
-    "/deva:signelongcandra": "\u0955",
-    "/deva:six": "\u096C",
-    "/deva:ssa": "\u0937",
-    "/deva:ta": "\u0924",
-    "/deva:tha": "\u0925",
-    "/deva:three": "\u0969",
-    "/deva:tta": "\u091F",
-    "/deva:ttha": "\u0920",
-    "/deva:two": "\u0968",
-    "/deva:u": "\u0909",
-    "/deva:udatta": "\u0951",
-    "/deva:ue": "\u0976",
-    "/deva:uesign": "\u0956",
-    "/deva:usign": "\u0941",
-    "/deva:uu": "\u090A",
-    "/deva:uue": "\u0977",
-    "/deva:uuesign": "\u0957",
-    "/deva:uusign": "\u0942",
-    "/deva:va": "\u0935",
-    "/deva:virama": "\u094D",
-    "/deva:visarga": "\u0903",
-    "/deva:ya": "\u092F",
-    "/deva:yaheavy": "\u097A",
-    "/deva:yya": "\u095F",
-    "/deva:za": "\u095B",
-    "/deva:zero": "\u0966",
-    "/deva:zha": "\u0979",
-    "/dezh": "\u02A4",
-    "/dfemaledbl": "\u26A2",
-    "/dhabengali": "\u09A7",
-    "/dhadeva": "\u0927",
-    "/dhagujarati": "\u0AA7",
-    "/dhagurmukhi": "\u0A27",
-    "/dhook": "\u0257",
-    "/diaeresisgreaterfunc": "\u2369",
-    "/dialytikatonos": "\u0385",
-    "/dialytikatonoscmb": "\u0344",
-    "/diametersign": "\u2300",
-    "/diamond": "\u2666",
-    "/diamondShapeADotInside": "\u1F4A0",
-    "/diamondinsquarewhite": "\u26CB",
-    "/diamondoperator": "\u22C4",
-    "/diamondsuitwhite": "\u2662",
-    "/diamondunderlinefunc": "\u235A",
-    "/diamondwhitewithdiamondsmallblack": "\u25C8",
-    "/diefive": "\u2684",
-    "/diefour": "\u2683",
-    "/dieone": "\u2680",
-    "/dieresis": "\u00A8",
-    "/dieresisacute": "\uF6D7",
-    "/dieresisbelowcmb": "\u0324",
-    "/dieresiscmb": "\u0308",
-    "/dieresisgrave": "\uF6D8",
-    "/dieresistilde": "\u1FC1",
-    "/dieresistonos": "\u0385",
-    "/dieselLocomotive": "\u1F6F2",
-    "/diesix": "\u2685",
-    "/diethree": "\u2682",
-    "/dietwo": "\u2681",
-    "/differencebetween": "\u224F",
-    "/digamma": "\u03DD",
-    "/digammapamphylian": "\u0377",
-    "/digramgreateryang": "\u268C",
-    "/digramgreateryin": "\u268F",
-    "/digramlesseryang": "\u268E",
-    "/digramlesseryin": "\u268D",
-    "/dihiragana": "\u3062",
-    "/dikatakana": "\u30C2",
-    "/dimensionorigin": "\u2331",
-    "/dingbatSAns-serifzerocircle": "\u1F10B",
-    "/dingbatSAns-serifzerocircleblack": "\u1F10C",
-    "/dinsular": "\uA77A",
-    "/directHit": "\u1F3AF",
-    "/directcurrentformtwo": "\u2393",
-    "/dirgamurevowel": "\uA9BB",
-    "/disabledcar": "\u26CD",
-    "/disappointedButRelievedFace": "\u1F625",
-    "/disappointedFace": "\u1F61E",
-    "/discontinuousunderline": "\u2382",
-    "/dittomark": "\u3003",
-    "/divide": "\u00F7",
-    "/divides": "\u2223",
-    "/divisionslash": "\u2215",
-    "/divisiontimes": "\u22C7",
-    "/divorce": "\u26AE",
-    "/dizzy": "\u1F4AB",
-    "/dizzyFace": "\u1F635",
-    "/djecyr": "\u0452",
-    "/djecyrillic": "\u0452",
-    "/djekomicyr": "\u0503",
-    "/dkshade": "\u2593",
-    "/dlfullwidth": "\u3397",
-    "/dlinebelow": "\u1E0F",
-    "/dlogicalorsquare": "\u27CF",
-    "/dlogicalsquare": "\u27CE",
-    "/dlsquare": "\u3397",
-    "/dm2fullwidth": "\u3378",
-    "/dm3fullwidth": "\u3379",
-    "/dmacron": "\u0111",
-    "/dmaledbl": "\u26A3",
-    "/dmfullwidth": "\u3377",
-    "/dmonospace": "\uFF44",
-    "/dnblock": "\u2584",
-    "/dndblhorzsng": "\u2565",
-    "/dndblleftsng": "\u2556",
-    "/dndblrightsng": "\u2553",
-    "/dngb:airplane": "\u2708",
-    "/dngb:arrowfeatheredblackNE": "\u27B6",
-    "/dngb:arrowfeatheredblackSE": "\u27B4",
-    "/dngb:arrowfeatheredblackheavyNE": "\u27B9",
-    "/dngb:arrowfeatheredblackheavySE": "\u27B7",
-    "/dngb:arrowheadrightblack": "\u27A4",
-    "/dngb:arrowheadrightthreeDbottomlight": "\u27A3",
-    "/dngb:arrowheadrightthreeDtoplight": "\u27A2",
-    "/dngb:arrowheavyNE": "\u279A",
-    "/dngb:arrowheavySE": "\u2798",
-    "/dngb:arrowrightbacktiltedshadowedwhite": "\u27AB",
-    "/dngb:arrowrightblack": "\u27A1",
-    "/dngb:arrowrightcircledwhiteheavy": "\u27B2",
-    "/dngb:arrowrightcurvedownblackheavy": "\u27A5",
-    "/dngb:arrowrightcurveupblackheavy": "\u27A6",
-    "/dngb:arrowrightfeatheredblack": "\u27B5",
-    "/dngb:arrowrightfeatheredblackheavy": "\u27B8",
-    "/dngb:arrowrightfeatheredwhite": "\u27B3",
-    "/dngb:arrowrightfronttiltedshadowedwhite": "\u27AC",
-    "/dngb:arrowrightheavy": "\u2799",
-    "/dngb:arrowrightleftshadedwhite": "\u27AA",
-    "/dngb:arrowrightoutlinedopen": "\u27BE",
-    "/dngb:arrowrightpointed": "\u279B",
-    "/dngb:arrowrightpointedblackheavy": "\u27A8",
-    "/dngb:arrowrightrightshadedwhite": "\u27A9",
-    "/dngb:arrowrightroundheavy": "\u279C",
-    "/dngb:arrowrightsquatblack": "\u27A7",
-    "/dngb:arrowrighttriangle": "\u279D",
-    "/dngb:arrowrighttriangledashed": "\u279F",
-    "/dngb:arrowrighttriangledashedheavy": "\u27A0",
-    "/dngb:arrowrighttriangleheavy": "\u279E",
-    "/dngb:arrowrightwedge": "\u27BC",
-    "/dngb:arrowrightwedgeheavy": "\u27BD",
-    "/dngb:arrowrightwideheavy": "\u2794",
-    "/dngb:arrowshadowrightlowerwhiteheavy": "\u27AD",
-    "/dngb:arrowshadowrightnotchedlowerwhite": "\u27AF",
-    "/dngb:arrowshadowrightnotchedupperwhite": "\u27B1",
-    "/dngb:arrowshadowrightupperwhiteheavy": "\u27AE",
-    "/dngb:arrowteardropright": "\u27BA",
-    "/dngb:arrowteardroprightheavy": "\u27BB",
-    "/dngb:asteriskballoon": "\u2749",
-    "/dngb:asteriskballoonfour": "\u2723",
-    "/dngb:asteriskballoonheavyfour": "\u2724",
-    "/dngb:asteriskcentreopen": "\u2732",
-    "/dngb:asteriskclubfour": "\u2725",
-    "/dngb:asteriskheavy": "\u2731",
-    "/dngb:asteriskpointedsixteen": "\u273A",
-    "/dngb:asteriskteardrop": "\u273B",
-    "/dngb:asteriskteardropcentreopen": "\u273C",
-    "/dngb:asteriskteardropfour": "\u2722",
-    "/dngb:asteriskteardropheavy": "\u273D",
-    "/dngb:asteriskteardroppinwheelheavy": "\u2743",
-    "/dngb:asteriskteardroppropellereight": "\u274A",
-    "/dngb:asteriskteardroppropellerheavyeight": "\u274B",
-    "/dngb:ballotx": "\u2717",
-    "/dngb:ballotxheavy": "\u2718",
-    "/dngb:bracketleftpointedangleheavyornament": "\u2770",
-    "/dngb:bracketleftpointedanglemediumornament": "\u276C",
-    "/dngb:bracketrightpointedangleheavyornament": "\u2771",
-    "/dngb:bracketrightpointedanglemediumornament": "\u276D",
-    "/dngb:bracketshellleftlightornament": "\u2772",
-    "/dngb:bracketshellrightlightornament": "\u2773",
-    "/dngb:check": "\u2713",
-    "/dngb:checkheavy": "\u2714",
-    "/dngb:checkwhiteheavy": "\u2705",
-    "/dngb:chevronsnowflakeheavy": "\u2746",
-    "/dngb:circleshadowedwhite": "\u274D",
-    "/dngb:commaheavydoubleornament": "\u275E",
-    "/dngb:commaheavydoubleturnedornament": "\u275D",
-    "/dngb:commaheavyornament": "\u275C",
-    "/dngb:commaheavyturnedornament": "\u275B",
-    "/dngb:compasstarpointedblackeight": "\u2737",
-    "/dngb:compasstarpointedblackheavyeight": "\u2738",
-    "/dngb:cross": "\u274C",
-    "/dngb:crosscentreopen": "\u271B",
-    "/dngb:crosscentreopenheavy": "\u271C",
-    "/dngb:curlybracketleftmediumornament": "\u2774",
-    "/dngb:curlybracketrightmediumornament": "\u2775",
-    "/dngb:curlyloop": "\u27B0",
-    "/dngb:curlyloopdouble": "\u27BF",
-    "/dngb:curvedstemparagraphsignornament": "\u2761",
-    "/dngb:diamondminusxblackwhite": "\u2756",
-    "/dngb:divisionsignheavy": "\u2797",
-    "/dngb:eightnegativecircled": "\u277D",
-    "/dngb:eightsanscircled": "\u2787",
-    "/dngb:eightsansnegativecircled": "\u2791",
-    "/dngb:envelope": "\u2709",
-    "/dngb:exclamationheavy": "\u2757",
-    "/dngb:exclamationheavyornament": "\u2762",
-    "/dngb:exclamationwhiteornament": "\u2755",
-    "/dngb:fivenegativecircled": "\u277A",
-    "/dngb:fivesanscircled": "\u2784",
-    "/dngb:fivesansnegativecircled": "\u278E",
-    "/dngb:floralheart": "\u2766",
-    "/dngb:floralheartbulletrotated": "\u2767",
-    "/dngb:floretteblack": "\u273F",
-    "/dngb:floretteoutlinedpetalledblackeight": "\u2741",
-    "/dngb:florettepetalledblackwhitesix": "\u273E",
-    "/dngb:florettewhite": "\u2740",
-    "/dngb:fournegativecircled": "\u2779",
-    "/dngb:foursanscircled": "\u2783",
-    "/dngb:foursansnegativecircled": "\u278D",
-    "/dngb:greekcrossheavy": "\u271A",
-    "/dngb:greekcrossoutlined": "\u2719",
-    "/dngb:heartblackheavy": "\u2764",
-    "/dngb:heartbulletrotatedblackheavy": "\u2765",
-    "/dngb:heartexclamationheavyornament": "\u2763",
-    "/dngb:hvictory": "\u270C",
-    "/dngb:hwriting": "\u270D",
-    "/dngb:latincross": "\u271D",
-    "/dngb:latincrossoutlined": "\u271F",
-    "/dngb:latincrossshadowedwhite": "\u271E",
-    "/dngb:lowcommaheavydoubleornament": "\u2760",
-    "/dngb:lowcommaheavyornament": "\u275F",
-    "/dngb:maltesecross": "\u2720",
-    "/dngb:minussignheavy": "\u2796",
-    "/dngb:multiplicationx": "\u2715",
-    "/dngb:multiplicationxheavy": "\u2716",
-    "/dngb:nibblack": "\u2712",
-    "/dngb:nibwhite": "\u2711",
-    "/dngb:ninenegativecircled": "\u277E",
-    "/dngb:ninesanscircled": "\u2788",
-    "/dngb:ninesansnegativecircled": "\u2792",
-    "/dngb:onenegativecircled": "\u2776",
-    "/dngb:onesanscircled": "\u2780",
-    "/dngb:onesansnegativecircled": "\u278A",
-    "/dngb:parenthesisleftflattenedmediumornament": "\u276A",
-    "/dngb:parenthesisleftmediumornament": "\u2768",
-    "/dngb:parenthesisrightflattenedmediumornament": "\u276B",
-    "/dngb:parenthesisrightmediumornament": "\u2769",
-    "/dngb:pencil": "\u270F",
-    "/dngb:pencillowerright": "\u270E",
-    "/dngb:pencilupperright": "\u2710",
-    "/dngb:plussignheavy": "\u2795",
-    "/dngb:questionblackornament": "\u2753",
-    "/dngb:questionwhiteornament": "\u2754",
-    "/dngb:quotationleftpointedangleheavyornament": "\u276E",
-    "/dngb:quotationrightpointedangleheavyornament": "\u276F",
-    "/dngb:raisedfist": "\u270A",
-    "/dngb:raisedh": "\u270B",
-    "/dngb:safetyscissorsblack": "\u2700",
-    "/dngb:scissorsblack": "\u2702",
-    "/dngb:scissorslowerblade": "\u2703",
-    "/dngb:scissorsupperblade": "\u2701",
-    "/dngb:scissorswhite": "\u2704",
-    "/dngb:sevennegativecircled": "\u277C",
-    "/dngb:sevensanscircled": "\u2786",
-    "/dngb:sevensansnegativecircled": "\u2790",
-    "/dngb:sixnegativecircled": "\u277B",
-    "/dngb:sixsanscircled": "\u2785",
-    "/dngb:sixsansnegativecircled": "\u278F",
-    "/dngb:snowflake": "\u2744",
-    "/dngb:snowflaketight": "\u2745",
-    "/dngb:sparkle": "\u2747",
-    "/dngb:sparkleheavy": "\u2748",
-    "/dngb:sparkles": "\u2728",
-    "/dngb:spokedasteriskeight": "\u2733",
-    "/dngb:squaredcrossnegative": "\u274E",
-    "/dngb:squarelowerrightshadowedwhite": "\u2751",
-    "/dngb:squareshadowlowerrightwhite": "\u274F",
-    "/dngb:squareshadowupperrightwhite": "\u2750",
-    "/dngb:squareupperrightshadowedwhite": "\u2752",
-    "/dngb:starcentreblackwhite": "\u272C",
-    "/dngb:starcentreopenblack": "\u272B",
-    "/dngb:starcentreopenpointedcircledeight": "\u2742",
-    "/dngb:starcircledwhite": "\u272A",
-    "/dngb:starofdavid": "\u2721",
-    "/dngb:staroutlinedblack": "\u272D",
-    "/dngb:staroutlinedblackheavy": "\u272E",
-    "/dngb:staroutlinedstresswhite": "\u2729",
-    "/dngb:starpinwheel": "\u272F",
-    "/dngb:starpointedblackeight": "\u2734",
-    "/dngb:starpointedblackfour": "\u2726",
-    "/dngb:starpointedblacksix": "\u2736",
-    "/dngb:starpointedblacktwelve": "\u2739",
-    "/dngb:starpointedpinwheeleight": "\u2735",
-    "/dngb:starpointedwhitefour": "\u2727",
-    "/dngb:starshadowedwhite": "\u2730",
-    "/dngb:tapedrive": "\u2707",
-    "/dngb:telephonelocationsign": "\u2706",
-    "/dngb:tennegativecircled": "\u277F",
-    "/dngb:tensanscircled": "\u2789",
-    "/dngb:tensansnegativecircled": "\u2793",
-    "/dngb:threenegativecircled": "\u2778",
-    "/dngb:threesanscircled": "\u2782",
-    "/dngb:threesansnegativecircled": "\u278C",
-    "/dngb:twonegativecircled": "\u2777",
-    "/dngb:twosanscircled": "\u2781",
-    "/dngb:twosansnegativecircled": "\u278B",
-    "/dngb:verticalbarheavy": "\u275A",
-    "/dngb:verticalbarlight": "\u2758",
-    "/dngb:verticalbarmedium": "\u2759",
-    "/dnheavyhorzlight": "\u2530",
-    "/dnheavyleftlight": "\u2512",
-    "/dnheavyleftuplight": "\u2527",
-    "/dnheavyrightlight": "\u250E",
-    "/dnheavyrightuplight": "\u251F",
-    "/dnheavyuphorzlight": "\u2541",
-    "/dnlighthorzheavy": "\u252F",
-    "/dnlightleftheavy": "\u2511",
-    "/dnlightleftupheavy": "\u2529",
-    "/dnlightrightheavy": "\u250D",
-    "/dnlightrightupheavy": "\u2521",
-    "/dnlightuphorzheavy": "\u2547",
-    "/dnsnghorzdbl": "\u2564",
-    "/dnsngleftdbl": "\u2555",
-    "/dnsngrightdbl": "\u2552",
-    "/doNotLitter": "\u1F6AF",
-    "/dochadathai": "\u0E0E",
-    "/document": "\u1F5CE",
-    "/documentPicture": "\u1F5BB",
-    "/documentText": "\u1F5B9",
-    "/documentTextAndPicture": "\u1F5BA",
-    "/dodekthai": "\u0E14",
-    "/doesnotcontainasnormalsubgroorequalup": "\u22ED",
-    "/doesnotcontainasnormalsubgroup": "\u22EB",
-    "/doesnotdivide": "\u2224",
-    "/doesnotforce": "\u22AE",
-    "/doesnotprecede": "\u2280",
-    "/doesnotprecedeorequal": "\u22E0",
-    "/doesnotprove": "\u22AC",
-    "/doesnotsucceed": "\u2281",
-    "/doesnotsucceedorequal": "\u22E1",
-    "/dog": "\u1F415",
-    "/dogFace": "\u1F436",
-    "/dohiragana": "\u3069",
-    "/dokatakana": "\u30C9",
-    "/dollar": "\u0024",
-    "/dollarinferior": "\uF6E3",
-    "/dollarmonospace": "\uFF04",
-    "/dollaroldstyle": "\uF724",
-    "/dollarsmall": "\uFE69",
-    "/dollarsuperior": "\uF6E4",
-    "/dolphin": "\u1F42C",
-    "/dominohorizontal_00_00": "\u1F031",
-    "/dominohorizontal_00_01": "\u1F032",
-    "/dominohorizontal_00_02": "\u1F033",
-    "/dominohorizontal_00_03": "\u1F034",
-    "/dominohorizontal_00_04": "\u1F035",
-    "/dominohorizontal_00_05": "\u1F036",
-    "/dominohorizontal_00_06": "\u1F037",
-    "/dominohorizontal_01_00": "\u1F038",
-    "/dominohorizontal_01_01": "\u1F039",
-    "/dominohorizontal_01_02": "\u1F03A",
-    "/dominohorizontal_01_03": "\u1F03B",
-    "/dominohorizontal_01_04": "\u1F03C",
-    "/dominohorizontal_01_05": "\u1F03D",
-    "/dominohorizontal_01_06": "\u1F03E",
-    "/dominohorizontal_02_00": "\u1F03F",
-    "/dominohorizontal_02_01": "\u1F040",
-    "/dominohorizontal_02_02": "\u1F041",
-    "/dominohorizontal_02_03": "\u1F042",
-    "/dominohorizontal_02_04": "\u1F043",
-    "/dominohorizontal_02_05": "\u1F044",
-    "/dominohorizontal_02_06": "\u1F045",
-    "/dominohorizontal_03_00": "\u1F046",
-    "/dominohorizontal_03_01": "\u1F047",
-    "/dominohorizontal_03_02": "\u1F048",
-    "/dominohorizontal_03_03": "\u1F049",
-    "/dominohorizontal_03_04": "\u1F04A",
-    "/dominohorizontal_03_05": "\u1F04B",
-    "/dominohorizontal_03_06": "\u1F04C",
-    "/dominohorizontal_04_00": "\u1F04D",
-    "/dominohorizontal_04_01": "\u1F04E",
-    "/dominohorizontal_04_02": "\u1F04F",
-    "/dominohorizontal_04_03": "\u1F050",
-    "/dominohorizontal_04_04": "\u1F051",
-    "/dominohorizontal_04_05": "\u1F052",
-    "/dominohorizontal_04_06": "\u1F053",
-    "/dominohorizontal_05_00": "\u1F054",
-    "/dominohorizontal_05_01": "\u1F055",
-    "/dominohorizontal_05_02": "\u1F056",
-    "/dominohorizontal_05_03": "\u1F057",
-    "/dominohorizontal_05_04": "\u1F058",
-    "/dominohorizontal_05_05": "\u1F059",
-    "/dominohorizontal_05_06": "\u1F05A",
-    "/dominohorizontal_06_00": "\u1F05B",
-    "/dominohorizontal_06_01": "\u1F05C",
-    "/dominohorizontal_06_02": "\u1F05D",
-    "/dominohorizontal_06_03": "\u1F05E",
-    "/dominohorizontal_06_04": "\u1F05F",
-    "/dominohorizontal_06_05": "\u1F060",
-    "/dominohorizontal_06_06": "\u1F061",
-    "/dominohorizontalback": "\u1F030",
-    "/dominovertical_00_00": "\u1F063",
-    "/dominovertical_00_01": "\u1F064",
-    "/dominovertical_00_02": "\u1F065",
-    "/dominovertical_00_03": "\u1F066",
-    "/dominovertical_00_04": "\u1F067",
-    "/dominovertical_00_05": "\u1F068",
-    "/dominovertical_00_06": "\u1F069",
-    "/dominovertical_01_00": "\u1F06A",
-    "/dominovertical_01_01": "\u1F06B",
-    "/dominovertical_01_02": "\u1F06C",
-    "/dominovertical_01_03": "\u1F06D",
-    "/dominovertical_01_04": "\u1F06E",
-    "/dominovertical_01_05": "\u1F06F",
-    "/dominovertical_01_06": "\u1F070",
-    "/dominovertical_02_00": "\u1F071",
-    "/dominovertical_02_01": "\u1F072",
-    "/dominovertical_02_02": "\u1F073",
-    "/dominovertical_02_03": "\u1F074",
-    "/dominovertical_02_04": "\u1F075",
-    "/dominovertical_02_05": "\u1F076",
-    "/dominovertical_02_06": "\u1F077",
-    "/dominovertical_03_00": "\u1F078",
-    "/dominovertical_03_01": "\u1F079",
-    "/dominovertical_03_02": "\u1F07A",
-    "/dominovertical_03_03": "\u1F07B",
-    "/dominovertical_03_04": "\u1F07C",
-    "/dominovertical_03_05": "\u1F07D",
-    "/dominovertical_03_06": "\u1F07E",
-    "/dominovertical_04_00": "\u1F07F",
-    "/dominovertical_04_01": "\u1F080",
-    "/dominovertical_04_02": "\u1F081",
-    "/dominovertical_04_03": "\u1F082",
-    "/dominovertical_04_04": "\u1F083",
-    "/dominovertical_04_05": "\u1F084",
-    "/dominovertical_04_06": "\u1F085",
-    "/dominovertical_05_00": "\u1F086",
-    "/dominovertical_05_01": "\u1F087",
-    "/dominovertical_05_02": "\u1F088",
-    "/dominovertical_05_03": "\u1F089",
-    "/dominovertical_05_04": "\u1F08A",
-    "/dominovertical_05_05": "\u1F08B",
-    "/dominovertical_05_06": "\u1F08C",
-    "/dominovertical_06_00": "\u1F08D",
-    "/dominovertical_06_01": "\u1F08E",
-    "/dominovertical_06_02": "\u1F08F",
-    "/dominovertical_06_03": "\u1F090",
-    "/dominovertical_06_04": "\u1F091",
-    "/dominovertical_06_05": "\u1F092",
-    "/dominovertical_06_06": "\u1F093",
-    "/dominoverticalback": "\u1F062",
-    "/dong": "\u20AB",
-    "/door": "\u1F6AA",
-    "/dorusquare": "\u3326",
-    "/dot": "\u27D1",
-    "/dotaccent": "\u02D9",
-    "/dotaccentcmb": "\u0307",
-    "/dotbelowcmb": "\u0323",
-    "/dotbelowcomb": "\u0323",
-    "/dotkatakana": "\u30FB",
-    "/dotlessbeh": "\u066E",
-    "/dotlessfeh": "\u06A1",
-    "/dotlessi": "\u0131",
-    "/dotlessj": "\uF6BE",
-    "/dotlessjstroke": "\u025F",
-    "/dotlessjstrokehook": "\u0284",
-    "/dotlesskhahabove": "\u06E1",
-    "/dotlessqaf": "\u066F",
-    "/dotlower:hb": "\u05C5",
-    "/dotmath": "\u22C5",
-    "/dotminus": "\u2238",
-    "/dotplus": "\u2214",
-    "/dotraised": "\u2E33",
-    "/dots1": "\u2801",
-    "/dots12": "\u2803",
-    "/dots123": "\u2807",
-    "/dots1234": "\u280F",
-    "/dots12345": "\u281F",
-    "/dots123456": "\u283F",
-    "/dots1234567": "\u287F",
-    "/dots12345678": "\u28FF",
-    "/dots1234568": "\u28BF",
-    "/dots123457": "\u285F",
-    "/dots1234578": "\u28DF",
-    "/dots123458": "\u289F",
-    "/dots12346": "\u282F",
-    "/dots123467": "\u286F",
-    "/dots1234678": "\u28EF",
-    "/dots123468": "\u28AF",
-    "/dots12347": "\u284F",
-    "/dots123478": "\u28CF",
-    "/dots12348": "\u288F",
-    "/dots1235": "\u2817",
-    "/dots12356": "\u2837",
-    "/dots123567": "\u2877",
-    "/dots1235678": "\u28F7",
-    "/dots123568": "\u28B7",
-    "/dots12357": "\u2857",
-    "/dots123578": "\u28D7",
-    "/dots12358": "\u2897",
-    "/dots1236": "\u2827",
-    "/dots12367": "\u2867",
-    "/dots123678": "\u28E7",
-    "/dots12368": "\u28A7",
-    "/dots1237": "\u2847",
-    "/dots12378": "\u28C7",
-    "/dots1238": "\u2887",
-    "/dots124": "\u280B",
-    "/dots1245": "\u281B",
-    "/dots12456": "\u283B",
-    "/dots124567": "\u287B",
-    "/dots1245678": "\u28FB",
-    "/dots124568": "\u28BB",
-    "/dots12457": "\u285B",
-    "/dots124578": "\u28DB",
-    "/dots12458": "\u289B",
-    "/dots1246": "\u282B",
-    "/dots12467": "\u286B",
-    "/dots124678": "\u28EB",
-    "/dots12468": "\u28AB",
-    "/dots1247": "\u284B",
-    "/dots12478": "\u28CB",
-    "/dots1248": "\u288B",
-    "/dots125": "\u2813",
-    "/dots1256": "\u2833",
-    "/dots12567": "\u2873",
-    "/dots125678": "\u28F3",
-    "/dots12568": "\u28B3",
-    "/dots1257": "\u2853",
-    "/dots12578": "\u28D3",
-    "/dots1258": "\u2893",
-    "/dots126": "\u2823",
-    "/dots1267": "\u2863",
-    "/dots12678": "\u28E3",
-    "/dots1268": "\u28A3",
-    "/dots127": "\u2843",
-    "/dots1278": "\u28C3",
-    "/dots128": "\u2883",
-    "/dots13": "\u2805",
-    "/dots134": "\u280D",
-    "/dots1345": "\u281D",
-    "/dots13456": "\u283D",
-    "/dots134567": "\u287D",
-    "/dots1345678": "\u28FD",
-    "/dots134568": "\u28BD",
-    "/dots13457": "\u285D",
-    "/dots134578": "\u28DD",
-    "/dots13458": "\u289D",
-    "/dots1346": "\u282D",
-    "/dots13467": "\u286D",
-    "/dots134678": "\u28ED",
-    "/dots13468": "\u28AD",
-    "/dots1347": "\u284D",
-    "/dots13478": "\u28CD",
-    "/dots1348": "\u288D",
-    "/dots135": "\u2815",
-    "/dots1356": "\u2835",
-    "/dots13567": "\u2875",
-    "/dots135678": "\u28F5",
-    "/dots13568": "\u28B5",
-    "/dots1357": "\u2855",
-    "/dots13578": "\u28D5",
-    "/dots1358": "\u2895",
-    "/dots136": "\u2825",
-    "/dots1367": "\u2865",
-    "/dots13678": "\u28E5",
-    "/dots1368": "\u28A5",
-    "/dots137": "\u2845",
-    "/dots1378": "\u28C5",
-    "/dots138": "\u2885",
-    "/dots14": "\u2809",
-    "/dots145": "\u2819",
-    "/dots1456": "\u2839",
-    "/dots14567": "\u2879",
-    "/dots145678": "\u28F9",
-    "/dots14568": "\u28B9",
-    "/dots1457": "\u2859",
-    "/dots14578": "\u28D9",
-    "/dots1458": "\u2899",
-    "/dots146": "\u2829",
-    "/dots1467": "\u2869",
-    "/dots14678": "\u28E9",
-    "/dots1468": "\u28A9",
-    "/dots147": "\u2849",
-    "/dots1478": "\u28C9",
-    "/dots148": "\u2889",
-    "/dots15": "\u2811",
-    "/dots156": "\u2831",
-    "/dots1567": "\u2871",
-    "/dots15678": "\u28F1",
-    "/dots1568": "\u28B1",
-    "/dots157": "\u2851",
-    "/dots1578": "\u28D1",
-    "/dots158": "\u2891",
-    "/dots16": "\u2821",
-    "/dots167": "\u2861",
-    "/dots1678": "\u28E1",
-    "/dots168": "\u28A1",
-    "/dots17": "\u2841",
-    "/dots178": "\u28C1",
-    "/dots18": "\u2881",
-    "/dots2": "\u2802",
-    "/dots23": "\u2806",
-    "/dots234": "\u280E",
-    "/dots2345": "\u281E",
-    "/dots23456": "\u283E",
-    "/dots234567": "\u287E",
-    "/dots2345678": "\u28FE",
-    "/dots234568": "\u28BE",
-    "/dots23457": "\u285E",
-    "/dots234578": "\u28DE",
-    "/dots23458": "\u289E",
-    "/dots2346": "\u282E",
-    "/dots23467": "\u286E",
-    "/dots234678": "\u28EE",
-    "/dots23468": "\u28AE",
-    "/dots2347": "\u284E",
-    "/dots23478": "\u28CE",
-    "/dots2348": "\u288E",
-    "/dots235": "\u2816",
-    "/dots2356": "\u2836",
-    "/dots23567": "\u2876",
-    "/dots235678": "\u28F6",
-    "/dots23568": "\u28B6",
-    "/dots2357": "\u2856",
-    "/dots23578": "\u28D6",
-    "/dots2358": "\u2896",
-    "/dots236": "\u2826",
-    "/dots2367": "\u2866",
-    "/dots23678": "\u28E6",
-    "/dots2368": "\u28A6",
-    "/dots237": "\u2846",
-    "/dots2378": "\u28C6",
-    "/dots238": "\u2886",
-    "/dots24": "\u280A",
-    "/dots245": "\u281A",
-    "/dots2456": "\u283A",
-    "/dots24567": "\u287A",
-    "/dots245678": "\u28FA",
-    "/dots24568": "\u28BA",
-    "/dots2457": "\u285A",
-    "/dots24578": "\u28DA",
-    "/dots2458": "\u289A",
-    "/dots246": "\u282A",
-    "/dots2467": "\u286A",
-    "/dots24678": "\u28EA",
-    "/dots2468": "\u28AA",
-    "/dots247": "\u284A",
-    "/dots2478": "\u28CA",
-    "/dots248": "\u288A",
-    "/dots25": "\u2812",
-    "/dots256": "\u2832",
-    "/dots2567": "\u2872",
-    "/dots25678": "\u28F2",
-    "/dots2568": "\u28B2",
-    "/dots257": "\u2852",
-    "/dots2578": "\u28D2",
-    "/dots258": "\u2892",
-    "/dots26": "\u2822",
-    "/dots267": "\u2862",
-    "/dots2678": "\u28E2",
-    "/dots268": "\u28A2",
-    "/dots27": "\u2842",
-    "/dots278": "\u28C2",
-    "/dots28": "\u2882",
-    "/dots3": "\u2804",
-    "/dots34": "\u280C",
-    "/dots345": "\u281C",
-    "/dots3456": "\u283C",
-    "/dots34567": "\u287C",
-    "/dots345678": "\u28FC",
-    "/dots34568": "\u28BC",
-    "/dots3457": "\u285C",
-    "/dots34578": "\u28DC",
-    "/dots3458": "\u289C",
-    "/dots346": "\u282C",
-    "/dots3467": "\u286C",
-    "/dots34678": "\u28EC",
-    "/dots3468": "\u28AC",
-    "/dots347": "\u284C",
-    "/dots3478": "\u28CC",
-    "/dots348": "\u288C",
-    "/dots35": "\u2814",
-    "/dots356": "\u2834",
-    "/dots3567": "\u2874",
-    "/dots35678": "\u28F4",
-    "/dots3568": "\u28B4",
-    "/dots357": "\u2854",
-    "/dots3578": "\u28D4",
-    "/dots358": "\u2894",
-    "/dots36": "\u2824",
-    "/dots367": "\u2864",
-    "/dots3678": "\u28E4",
-    "/dots368": "\u28A4",
-    "/dots37": "\u2844",
-    "/dots378": "\u28C4",
-    "/dots38": "\u2884",
-    "/dots4": "\u2808",
-    "/dots45": "\u2818",
-    "/dots456": "\u2838",
-    "/dots4567": "\u2878",
-    "/dots45678": "\u28F8",
-    "/dots4568": "\u28B8",
-    "/dots457": "\u2858",
-    "/dots4578": "\u28D8",
-    "/dots458": "\u2898",
-    "/dots46": "\u2828",
-    "/dots467": "\u2868",
-    "/dots4678": "\u28E8",
-    "/dots468": "\u28A8",
-    "/dots47": "\u2848",
-    "/dots478": "\u28C8",
-    "/dots48": "\u2888",
-    "/dots5": "\u2810",
-    "/dots56": "\u2830",
-    "/dots567": "\u2870",
-    "/dots5678": "\u28F0",
-    "/dots568": "\u28B0",
-    "/dots57": "\u2850",
-    "/dots578": "\u28D0",
-    "/dots58": "\u2890",
-    "/dots6": "\u2820",
-    "/dots67": "\u2860",
-    "/dots678": "\u28E0",
-    "/dots68": "\u28A0",
-    "/dots7": "\u2840",
-    "/dots78": "\u28C0",
-    "/dots8": "\u2880",
-    "/dotsquarefour": "\u2E2C",
-    "/dottedcircle": "\u25CC",
-    "/dottedcross": "\u205C",
-    "/dotupper:hb": "\u05C4",
-    "/doublebarvertical": "\u23F8",
-    "/doubleyodpatah": "\uFB1F",
-    "/doubleyodpatahhebrew": "\uFB1F",
-    "/doughnut": "\u1F369",
-    "/doveOfPeace": "\u1F54A",
-    "/downtackbelowcmb": "\u031E",
-    "/downtackmod": "\u02D5",
-    "/downwarrowleftofuparrow": "\u21F5",
-    "/dparen": "\u249F",
-    "/dparenthesized": "\u249F",
-    "/drachma": "\u20AF",
-    "/dragon": "\u1F409",
-    "/dragonFace": "\u1F432",
-    "/draughtskingblack": "\u26C3",
-    "/draughtskingwhite": "\u26C1",
-    "/draughtsmanblack": "\u26C2",
-    "/draughtsmanwhite": "\u26C0",
-    "/dress": "\u1F457",
-    "/driveslow": "\u26DA",
-    "/dromedaryCamel": "\u1F42A",
-    "/droplet": "\u1F4A7",
-    "/dsquare": "\u1F1A5",
-    "/dsuperior": "\uF6EB",
-    "/dtail": "\u0256",
-    "/dtopbar": "\u018C",
-    "/duhiragana": "\u3065",
-    "/dukatakana": "\u30C5",
-    "/dul": "\u068E",
-    "/dul.fina": "\uFB87",
-    "/dul.isol": "\uFB86",
-    "/dum": "\uA771",
-    "/dvd": "\u1F4C0",
-    "/dyeh": "\u0684",
-    "/dyeh.fina": "\uFB73",
-    "/dyeh.init": "\uFB74",
-    "/dyeh.isol": "\uFB72",
-    "/dyeh.medi": "\uFB75",
-    "/dz": "\u01F3",
-    "/dzaltone": "\u02A3",
-    "/dzcaron": "\u01C6",
-    "/dzcurl": "\u02A5",
-    "/dzeabkhasiancyrillic": "\u04E1",
-    "/dzeabkhcyr": "\u04E1",
-    "/dzecyr": "\u0455",
-    "/dzecyrillic": "\u0455",
-    "/dzed": "\u02A3",
-    "/dzedcurl": "\u02A5",
-    "/dzhecyr": "\u045F",
-    "/dzhecyrillic": "\u045F",
-    "/dzjekomicyr": "\u0507",
-    "/dzzhecyr": "\u052B",
-    "/e": "\u0065",
-    "/e-mail": "\u1F4E7",
-    "/e.fina": "\uFBE5",
-    "/e.inferior": "\u2091",
-    "/e.init": "\uFBE6",
-    "/e.isol": "\uFBE4",
-    "/e.medi": "\uFBE7",
-    "/eVfullwidth": "\u32CE",
-    "/eacute": "\u00E9",
-    "/earOfMaize": "\u1F33D",
-    "/earOfRice": "\u1F33E",
-    "/earth": "\u2641",
-    "/earthGlobeAmericas": "\u1F30E",
-    "/earthGlobeAsiaAustralia": "\u1F30F",
-    "/earthGlobeEuropeAfrica": "\u1F30D",
-    "/earthground": "\u23DA",
-    "/earthideographiccircled": "\u328F",
-    "/earthideographicparen": "\u322F",
-    "/eastsyriaccross": "\u2671",
-    "/ebengali": "\u098F",
-    "/ebopomofo": "\u311C",
-    "/ebreve": "\u0115",
-    "/ecandradeva": "\u090D",
-    "/ecandragujarati": "\u0A8D",
-    "/ecandravowelsigndeva": "\u0945",
-    "/ecandravowelsigngujarati": "\u0AC5",
-    "/ecaron": "\u011B",
-    "/ecedilla": "\u0229",
-    "/ecedillabreve": "\u1E1D",
-    "/echarmenian": "\u0565",
-    "/echyiwnarmenian": "\u0587",
-    "/ecircle": "\u24D4",
-    "/ecirclekatakana": "\u32D3",
-    "/ecircumflex": "\u00EA",
-    "/ecircumflexacute": "\u1EBF",
-    "/ecircumflexbelow": "\u1E19",
-    "/ecircumflexdotbelow": "\u1EC7",
-    "/ecircumflexgrave": "\u1EC1",
-    "/ecircumflexhoi": "\u1EC3",
-    "/ecircumflexhookabove": "\u1EC3",
-    "/ecircumflextilde": "\u1EC5",
-    "/ecyrillic": "\u0454",
-    "/edblgrave": "\u0205",
-    "/edblstruckitalic": "\u2147",
-    "/edeva": "\u090F",
-    "/edieresis": "\u00EB",
-    "/edot": "\u0117",
-    "/edotaccent": "\u0117",
-    "/edotbelow": "\u1EB9",
-    "/eegurmukhi": "\u0A0F",
-    "/eekaasquare": "\u3308",
-    "/eematragurmukhi": "\u0A47",
-    "/efcyr": "\u0444",
-    "/efcyrillic": "\u0444",
-    "/egrave": "\u00E8",
-    "/egravedbl": "\u0205",
-    "/egujarati": "\u0A8F",
-    "/egyptain": "\uA725",
-    "/egyptalef": "\uA723",
-    "/eharmenian": "\u0567",
-    "/ehbopomofo": "\u311D",
-    "/ehiragana": "\u3048",
-    "/ehoi": "\u1EBB",
-    "/ehookabove": "\u1EBB",
-    "/eibopomofo": "\u311F",
-    "/eight": "\u0038",
-    "/eight.inferior": "\u2088",
-    "/eight.roman": "\u2167",
-    "/eight.romansmall": "\u2177",
-    "/eight.superior": "\u2078",
-    "/eightarabic": "\u0668",
-    "/eightbengali": "\u09EE",
-    "/eightcircle": "\u2467",
-    "/eightcircledbl": "\u24FC",
-    "/eightcircleinversesansserif": "\u2791",
-    "/eightcomma": "\u1F109",
-    "/eightdeva": "\u096E",
-    "/eighteencircle": "\u2471",
-    "/eighteencircleblack": "\u24F2",
-    "/eighteenparen": "\u2485",
-    "/eighteenparenthesized": "\u2485",
-    "/eighteenperiod": "\u2499",
-    "/eightfar": "\u06F8",
-    "/eightgujarati": "\u0AEE",
-    "/eightgurmukhi": "\u0A6E",
-    "/eighthackarabic": "\u0668",
-    "/eighthangzhou": "\u3028",
-    "/eighthnote": "\u266A",
-    "/eighthnotebeamed": "\u266B",
-    "/eightideographiccircled": "\u3287",
-    "/eightideographicparen": "\u3227",
-    "/eightinferior": "\u2088",
-    "/eightksquare": "\u1F19F",
-    "/eightmonospace": "\uFF18",
-    "/eightoldstyle": "\uF738",
-    "/eightparen": "\u247B",
-    "/eightparenthesized": "\u247B",
-    "/eightperiod": "\u248F",
-    "/eightpersian": "\u06F8",
-    "/eightroman": "\u2177",
-    "/eightsuperior": "\u2078",
-    "/eightthai": "\u0E58",
-    "/eightycirclesquare": "\u324F",
-    "/einvertedbreve": "\u0207",
-    "/eiotifiedcyr": "\u0465",
-    "/eiotifiedcyrillic": "\u0465",
-    "/eject": "\u23CF",
-    "/ekatakana": "\u30A8",
-    "/ekatakanahalfwidth": "\uFF74",
-    "/ekonkargurmukhi": "\u0A74",
-    "/ekorean": "\u3154",
-    "/elcyr": "\u043B",
-    "/elcyrillic": "\u043B",
-    "/electricLightBulb": "\u1F4A1",
-    "/electricPlug": "\u1F50C",
-    "/electricTorch": "\u1F526",
-    "/electricalintersection": "\u23E7",
-    "/electricarrow": "\u2301",
-    "/element": "\u2208",
-    "/elementdotabove": "\u22F5",
-    "/elementlonghorizontalstroke": "\u22F2",
-    "/elementopeningup": "\u27D2",
-    "/elementoverbar": "\u22F6",
-    "/elementoverbarsmall": "\u22F7",
-    "/elementsmall": "\u220A",
-    "/elementsmallverticalbarhorizontalstroke": "\u22F4",
-    "/elementtwoshorizontalstroke": "\u22F9",
-    "/elementunderbar": "\u22F8",
-    "/elementverticalbarhorizontalstroke": "\u22F3",
-    "/elephant": "\u1F418",
-    "/eleven.roman": "\u216A",
-    "/eleven.romansmall": "\u217A",
-    "/elevencircle": "\u246A",
-    "/elevencircleblack": "\u24EB",
-    "/elevenparen": "\u247E",
-    "/elevenparenthesized": "\u247E",
-    "/elevenperiod": "\u2492",
-    "/elevenroman": "\u217A",
-    "/elhookcyr": "\u0513",
-    "/ellipsis": "\u2026",
-    "/ellipsisdiagonaldownright": "\u22F1",
-    "/ellipsisdiagonalupright": "\u22F0",
-    "/ellipsismidhorizontal": "\u22EF",
-    "/ellipsisvertical": "\u22EE",
-    "/elmiddlehookcyr": "\u0521",
-    "/elsharptailcyr": "\u04C6",
-    "/eltailcyr": "\u052F",
-    "/emacron": "\u0113",
-    "/emacronacute": "\u1E17",
-    "/emacrongrave": "\u1E15",
-    "/emcyr": "\u043C",
-    "/emcyrillic": "\u043C",
-    "/emdash": "\u2014",
-    "/emdashdbl": "\u2E3A",
-    "/emdashtpl": "\u2E3B",
-    "/emdashvertical": "\uFE31",
-    "/emojiModifierFitzpatrickType-1-2": "\u1F3FB",
-    "/emojiModifierFitzpatrickType-3": "\u1F3FC",
-    "/emojiModifierFitzpatrickType-4": "\u1F3FD",
-    "/emojiModifierFitzpatrickType-5": "\u1F3FE",
-    "/emojiModifierFitzpatrickType-6": "\u1F3FF",
-    "/emonospace": "\uFF45",
-    "/emphasis": "\u2383",
-    "/emphasismarkarmenian": "\u055B",
-    "/emptyDocument": "\u1F5CB",
-    "/emptyNote": "\u1F5C5",
-    "/emptyNotePad": "\u1F5C7",
-    "/emptyNotePage": "\u1F5C6",
-    "/emptyPage": "\u1F5CC",
-    "/emptyPages": "\u1F5CD",
-    "/emptyset": "\u2205",
-    "/emquad": "\u2001",
-    "/emsharptailcyr": "\u04CE",
-    "/emspace": "\u2003",
-    "/enbopomofo": "\u3123",
-    "/encyr": "\u043D",
-    "/encyrillic": "\u043D",
-    "/endLeftwardsArrowAbove": "\u1F51A",
-    "/endash": "\u2013",
-    "/endashvertical": "\uFE32",
-    "/endescendercyrillic": "\u04A3",
-    "/endpro": "\u220E",
-    "/eng": "\u014B",
-    "/engbopomofo": "\u3125",
-    "/engecyr": "\u04A5",
-    "/enghecyrillic": "\u04A5",
-    "/enhookcyr": "\u04C8",
-    "/enhookcyrillic": "\u04C8",
-    "/enhookleftcyr": "\u0529",
-    "/enmiddlehookcyr": "\u0523",
-    "/enotch": "\u2C78",
-    "/enquad": "\u2000",
-    "/ensharptailcyr": "\u04CA",
-    "/enspace": "\u2002",
-    "/entailcyr": "\u04A3",
-    "/enter": "\u2386",
-    "/enterpriseideographiccircled": "\u32AD",
-    "/enterpriseideographicparen": "\u323D",
-    "/envelopeDownwardsArrowAbove": "\u1F4E9",
-    "/envelopeLightning": "\u1F584",
-    "/eogonek": "\u0119",
-    "/eokorean": "\u3153",
-    "/eopen": "\u025B",
-    "/eopenclosed": "\u029A",
-    "/eopenreversed": "\u025C",
-    "/eopenreversedclosed": "\u025E",
-    "/eopenreversedhook": "\u025D",
-    "/eparen": "\u24A0",
-    "/eparenthesized": "\u24A0",
-    "/epsilon": "\u03B5",
-    "/epsilonacute": "\u1F73",
-    "/epsilonasper": "\u1F11",
-    "/epsilonasperacute": "\u1F15",
-    "/epsilonaspergrave": "\u1F13",
-    "/epsilongrave": "\u1F72",
-    "/epsilonlenis": "\u1F10",
-    "/epsilonlenisacute": "\u1F14",
-    "/epsilonlenisgrave": "\u1F12",
-    "/epsilonlunatesymbol": "\u03F5",
-    "/epsilonreversedlunatesymbol": "\u03F6",
-    "/epsilontonos": "\u03AD",
-    "/epsilonunderlinefunc": "\u2377",
-    "/equal": "\u003D",
-    "/equal.inferior": "\u208C",
-    "/equal.superior": "\u207C",
-    "/equalandparallel": "\u22D5",
-    "/equalbydefinition": "\u225D",
-    "/equalmonospace": "\uFF1D",
-    "/equalorgreater": "\u22DD",
-    "/equalorless": "\u22DC",
-    "/equalorprecedes": "\u22DE",
-    "/equalorsucceeds": "\u22DF",
-    "/equalscolon": "\u2255",
-    "/equalsmall": "\uFE66",
-    "/equalsuperior": "\u207C",
-    "/equiangular": "\u225A",
-    "/equivalence": "\u2261",
-    "/equivalent": "\u224D",
-    "/eranameheiseisquare": "\u337B",
-    "/eranamemeizisquare": "\u337E",
-    "/eranamesyouwasquare": "\u337C",
-    "/eranametaisyousquare": "\u337D",
-    "/eraseleft": "\u232B",
-    "/eraseright": "\u2326",
-    "/erbopomofo": "\u3126",
-    "/ercyr": "\u0440",
-    "/ercyrillic": "\u0440",
-    "/ereversed": "\u0258",
-    "/ereversedcyr": "\u044D",
-    "/ereversedcyrillic": "\u044D",
-    "/ereverseddieresiscyr": "\u04ED",
-    "/ergfullwidth": "\u32CD",
-    "/ertickcyr": "\u048F",
-    "/escript": "\u212F",
-    "/escyr": "\u0441",
-    "/escyrillic": "\u0441",
-    "/esdescendercyrillic": "\u04AB",
-    "/esh": "\u0283",
-    "/eshcurl": "\u0286",
-    "/eshortdeva": "\u090E",
-    "/eshortvowelsigndeva": "\u0946",
-    "/eshreversedloop": "\u01AA",
-    "/eshsquatreversed": "\u0285",
-    "/esmallhiragana": "\u3047",
-    "/esmallkatakana": "\u30A7",
-    "/esmallkatakanahalfwidth": "\uFF6A",
-    "/estailcyr": "\u04AB",
-    "/estimated": "\u212E",
-    "/estimates": "\u2259",
-    "/estroke": "\u0247",
-    "/esukuudosquare": "\u3307",
-    "/esuperior": "\uF6EC",
-    "/et": "\uA76B",
-    "/eta": "\u03B7",
-    "/etaacute": "\u1F75",
-    "/etaacuteiotasub": "\u1FC4",
-    "/etaasper": "\u1F21",
-    "/etaasperacute": "\u1F25",
-    "/etaasperacuteiotasub": "\u1F95",
-    "/etaaspergrave": "\u1F23",
-    "/etaaspergraveiotasub": "\u1F93",
-    "/etaasperiotasub": "\u1F91",
-    "/etaaspertilde": "\u1F27",
-    "/etaaspertildeiotasub": "\u1F97",
-    "/etagrave": "\u1F74",
-    "/etagraveiotasub": "\u1FC2",
-    "/etaiotasub": "\u1FC3",
-    "/etalenis": "\u1F20",
-    "/etalenisacute": "\u1F24",
-    "/etalenisacuteiotasub": "\u1F94",
-    "/etalenisgrave": "\u1F22",
-    "/etalenisgraveiotasub": "\u1F92",
-    "/etalenisiotasub": "\u1F90",
-    "/etalenistilde": "\u1F26",
-    "/etalenistildeiotasub": "\u1F96",
-    "/etarmenian": "\u0568",
-    "/etatilde": "\u1FC6",
-    "/etatildeiotasub": "\u1FC7",
-    "/etatonos": "\u03AE",
-    "/eth": "\u00F0",
-    "/ethi:aaglottal": "\u12A3",
-    "/ethi:aglottal": "\u12A0",
-    "/ethi:ba": "\u1260",
-    "/ethi:baa": "\u1263",
-    "/ethi:be": "\u1265",
-    "/ethi:bee": "\u1264",
-    "/ethi:bi": "\u1262",
-    "/ethi:bo": "\u1266",
-    "/ethi:bu": "\u1261",
-    "/ethi:bwa": "\u1267",
-    "/ethi:ca": "\u1278",
-    "/ethi:caa": "\u127B",
-    "/ethi:ce": "\u127D",
-    "/ethi:cee": "\u127C",
-    "/ethi:cha": "\u1328",
-    "/ethi:chaa": "\u132B",
-    "/ethi:che": "\u132D",
-    "/ethi:chee": "\u132C",
-    "/ethi:chi": "\u132A",
-    "/ethi:cho": "\u132E",
-    "/ethi:chu": "\u1329",
-    "/ethi:chwa": "\u132F",
-    "/ethi:ci": "\u127A",
-    "/ethi:co": "\u127E",
-    "/ethi:colon": "\u1365",
-    "/ethi:comma": "\u1363",
-    "/ethi:cu": "\u1279",
-    "/ethi:cwa": "\u127F",
-    "/ethi:da": "\u12F0",
-    "/ethi:daa": "\u12F3",
-    "/ethi:dda": "\u12F8",
-    "/ethi:ddaa": "\u12FB",
-    "/ethi:dde": "\u12FD",
-    "/ethi:ddee": "\u12FC",
-    "/ethi:ddi": "\u12FA",
-    "/ethi:ddo": "\u12FE",
-    "/ethi:ddu": "\u12F9",
-    "/ethi:ddwa": "\u12FF",
-    "/ethi:de": "\u12F5",
-    "/ethi:dee": "\u12F4",
-    "/ethi:di": "\u12F2",
-    "/ethi:do": "\u12F6",
-    "/ethi:du": "\u12F1",
-    "/ethi:dwa": "\u12F7",
-    "/ethi:eeglottal": "\u12A4",
-    "/ethi:eglottal": "\u12A5",
-    "/ethi:eight": "\u1370",
-    "/ethi:eighty": "\u1379",
-    "/ethi:fa": "\u1348",
-    "/ethi:faa": "\u134B",
-    "/ethi:fe": "\u134D",
-    "/ethi:fee": "\u134C",
-    "/ethi:fi": "\u134A",
-    "/ethi:fifty": "\u1376",
-    "/ethi:five": "\u136D",
-    "/ethi:fo": "\u134E",
-    "/ethi:forty": "\u1375",
-    "/ethi:four": "\u136C",
-    "/ethi:fu": "\u1349",
-    "/ethi:fullstop": "\u1362",
-    "/ethi:fwa": "\u134F",
-    "/ethi:fya": "\u135A",
-    "/ethi:ga": "\u1308",
-    "/ethi:gaa": "\u130B",
-    "/ethi:ge": "\u130D",
-    "/ethi:gee": "\u130C",
-    "/ethi:geminationandvowellengthmarkcmb": "\u135D",
-    "/ethi:geminationmarkcmb": "\u135F",
-    "/ethi:gga": "\u1318",
-    "/ethi:ggaa": "\u131B",
-    "/ethi:gge": "\u131D",
-    "/ethi:ggee": "\u131C",
-    "/ethi:ggi": "\u131A",
-    "/ethi:ggo": "\u131E",
-    "/ethi:ggu": "\u1319",
-    "/ethi:ggwaa": "\u131F",
-    "/ethi:gi": "\u130A",
-    "/ethi:go": "\u130E",
-    "/ethi:goa": "\u130F",
-    "/ethi:gu": "\u1309",
-    "/ethi:gwa": "\u1310",
-    "/ethi:gwaa": "\u1313",
-    "/ethi:gwe": "\u1315",
-    "/ethi:gwee": "\u1314",
-    "/ethi:gwi": "\u1312",
-    "/ethi:ha": "\u1200",
-    "/ethi:haa": "\u1203",
-    "/ethi:he": "\u1205",
-    "/ethi:hee": "\u1204",
-    "/ethi:hha": "\u1210",
-    "/ethi:hhaa": "\u1213",
-    "/ethi:hhe": "\u1215",
-    "/ethi:hhee": "\u1214",
-    "/ethi:hhi": "\u1212",
-    "/ethi:hho": "\u1216",
-    "/ethi:hhu": "\u1211",
-    "/ethi:hhwa": "\u1217",
-    "/ethi:hi": "\u1202",
-    "/ethi:ho": "\u1206",
-    "/ethi:hoa": "\u1207",
-    "/ethi:hu": "\u1201",
-    "/ethi:hundred": "\u137B",
-    "/ethi:iglottal": "\u12A2",
-    "/ethi:ja": "\u1300",
-    "/ethi:jaa": "\u1303",
-    "/ethi:je": "\u1305",
-    "/ethi:jee": "\u1304",
-    "/ethi:ji": "\u1302",
-    "/ethi:jo": "\u1306",
-    "/ethi:ju": "\u1301",
-    "/ethi:jwa": "\u1307",
-    "/ethi:ka": "\u12A8",
-    "/ethi:kaa": "\u12AB",
-    "/ethi:ke": "\u12AD",
-    "/ethi:kee": "\u12AC",
-    "/ethi:ki": "\u12AA",
-    "/ethi:ko": "\u12AE",
-    "/ethi:koa": "\u12AF",
-    "/ethi:ku": "\u12A9",
-    "/ethi:kwa": "\u12B0",
-    "/ethi:kwaa": "\u12B3",
-    "/ethi:kwe": "\u12B5",
-    "/ethi:kwee": "\u12B4",
-    "/ethi:kwi": "\u12B2",
-    "/ethi:kxa": "\u12B8",
-    "/ethi:kxaa": "\u12BB",
-    "/ethi:kxe": "\u12BD",
-    "/ethi:kxee": "\u12BC",
-    "/ethi:kxi": "\u12BA",
-    "/ethi:kxo": "\u12BE",
-    "/ethi:kxu": "\u12B9",
-    "/ethi:kxwa": "\u12C0",
-    "/ethi:kxwaa": "\u12C3",
-    "/ethi:kxwe": "\u12C5",
-    "/ethi:kxwee": "\u12C4",
-    "/ethi:kxwi": "\u12C2",
-    "/ethi:la": "\u1208",
-    "/ethi:laa": "\u120B",
-    "/ethi:le": "\u120D",
-    "/ethi:lee": "\u120C",
-    "/ethi:li": "\u120A",
-    "/ethi:lo": "\u120E",
-    "/ethi:lu": "\u1209",
-    "/ethi:lwa": "\u120F",
-    "/ethi:ma": "\u1218",
-    "/ethi:maa": "\u121B",
-    "/ethi:me": "\u121D",
-    "/ethi:mee": "\u121C",
-    "/ethi:mi": "\u121A",
-    "/ethi:mo": "\u121E",
-    "/ethi:mu": "\u1219",
-    "/ethi:mwa": "\u121F",
-    "/ethi:mya": "\u1359",
-    "/ethi:na": "\u1290",
-    "/ethi:naa": "\u1293",
-    "/ethi:ne": "\u1295",
-    "/ethi:nee": "\u1294",
-    "/ethi:ni": "\u1292",
-    "/ethi:nine": "\u1371",
-    "/ethi:ninety": "\u137A",
-    "/ethi:no": "\u1296",
-    "/ethi:nu": "\u1291",
-    "/ethi:nwa": "\u1297",
-    "/ethi:nya": "\u1298",
-    "/ethi:nyaa": "\u129B",
-    "/ethi:nye": "\u129D",
-    "/ethi:nyee": "\u129C",
-    "/ethi:nyi": "\u129A",
-    "/ethi:nyo": "\u129E",
-    "/ethi:nyu": "\u1299",
-    "/ethi:nywa": "\u129F",
-    "/ethi:oglottal": "\u12A6",
-    "/ethi:one": "\u1369",
-    "/ethi:pa": "\u1350",
-    "/ethi:paa": "\u1353",
-    "/ethi:paragraphseparator": "\u1368",
-    "/ethi:pe": "\u1355",
-    "/ethi:pee": "\u1354",
-    "/ethi:pha": "\u1330",
-    "/ethi:phaa": "\u1333",
-    "/ethi:pharyngeala": "\u12D0",
-    "/ethi:pharyngealaa": "\u12D3",
-    "/ethi:pharyngeale": "\u12D5",
-    "/ethi:pharyngealee": "\u12D4",
-    "/ethi:pharyngeali": "\u12D2",
-    "/ethi:pharyngealo": "\u12D6",
-    "/ethi:pharyngealu": "\u12D1",
-    "/ethi:phe": "\u1335",
-    "/ethi:phee": "\u1334",
-    "/ethi:phi": "\u1332",
-    "/ethi:pho": "\u1336",
-    "/ethi:phu": "\u1331",
-    "/ethi:phwa": "\u1337",
-    "/ethi:pi": "\u1352",
-    "/ethi:po": "\u1356",
-    "/ethi:prefacecolon": "\u1366",
-    "/ethi:pu": "\u1351",
-    "/ethi:pwa": "\u1357",
-    "/ethi:qa": "\u1240",
-    "/ethi:qaa": "\u1243",
-    "/ethi:qe": "\u1245",
-    "/ethi:qee": "\u1244",
-    "/ethi:qha": "\u1250",
-    "/ethi:qhaa": "\u1253",
-    "/ethi:qhe": "\u1255",
-    "/ethi:qhee": "\u1254",
-    "/ethi:qhi": "\u1252",
-    "/ethi:qho": "\u1256",
-    "/ethi:qhu": "\u1251",
-    "/ethi:qhwa": "\u1258",
-    "/ethi:qhwaa": "\u125B",
-    "/ethi:qhwe": "\u125D",
-    "/ethi:qhwee": "\u125C",
-    "/ethi:qhwi": "\u125A",
-    "/ethi:qi": "\u1242",
-    "/ethi:qo": "\u1246",
-    "/ethi:qoa": "\u1247",
-    "/ethi:qu": "\u1241",
-    "/ethi:questionmark": "\u1367",
-    "/ethi:qwa": "\u1248",
-    "/ethi:qwaa": "\u124B",
-    "/ethi:qwe": "\u124D",
-    "/ethi:qwee": "\u124C",
-    "/ethi:qwi": "\u124A",
-    "/ethi:ra": "\u1228",
-    "/ethi:raa": "\u122B",
-    "/ethi:re": "\u122D",
-    "/ethi:ree": "\u122C",
-    "/ethi:ri": "\u122A",
-    "/ethi:ro": "\u122E",
-    "/ethi:ru": "\u1229",
-    "/ethi:rwa": "\u122F",
-    "/ethi:rya": "\u1358",
-    "/ethi:sa": "\u1230",
-    "/ethi:saa": "\u1233",
-    "/ethi:se": "\u1235",
-    "/ethi:sectionmark": "\u1360",
-    "/ethi:see": "\u1234",
-    "/ethi:semicolon": "\u1364",
-    "/ethi:seven": "\u136F",
-    "/ethi:seventy": "\u1378",
-    "/ethi:sha": "\u1238",
-    "/ethi:shaa": "\u123B",
-    "/ethi:she": "\u123D",
-    "/ethi:shee": "\u123C",
-    "/ethi:shi": "\u123A",
-    "/ethi:sho": "\u123E",
-    "/ethi:shu": "\u1239",
-    "/ethi:shwa": "\u123F",
-    "/ethi:si": "\u1232",
-    "/ethi:six": "\u136E",
-    "/ethi:sixty": "\u1377",
-    "/ethi:so": "\u1236",
-    "/ethi:su": "\u1231",
-    "/ethi:swa": "\u1237",
-    "/ethi:sza": "\u1220",
-    "/ethi:szaa": "\u1223",
-    "/ethi:sze": "\u1225",
-    "/ethi:szee": "\u1224",
-    "/ethi:szi": "\u1222",
-    "/ethi:szo": "\u1226",
-    "/ethi:szu": "\u1221",
-    "/ethi:szwa": "\u1227",
-    "/ethi:ta": "\u1270",
-    "/ethi:taa": "\u1273",
-    "/ethi:te": "\u1275",
-    "/ethi:tee": "\u1274",
-    "/ethi:ten": "\u1372",
-    "/ethi:tenthousand": "\u137C",
-    "/ethi:tha": "\u1320",
-    "/ethi:thaa": "\u1323",
-    "/ethi:the": "\u1325",
-    "/ethi:thee": "\u1324",
-    "/ethi:thi": "\u1322",
-    "/ethi:thirty": "\u1374",
-    "/ethi:tho": "\u1326",
-    "/ethi:three": "\u136B",
-    "/ethi:thu": "\u1321",
-    "/ethi:thwa": "\u1327",
-    "/ethi:ti": "\u1272",
-    "/ethi:to": "\u1276",
-    "/ethi:tsa": "\u1338",
-    "/ethi:tsaa": "\u133B",
-    "/ethi:tse": "\u133D",
-    "/ethi:tsee": "\u133C",
-    "/ethi:tsi": "\u133A",
-    "/ethi:tso": "\u133E",
-    "/ethi:tsu": "\u1339",
-    "/ethi:tswa": "\u133F",
-    "/ethi:tu": "\u1271",
-    "/ethi:twa": "\u1277",
-    "/ethi:twenty": "\u1373",
-    "/ethi:two": "\u136A",
-    "/ethi:tza": "\u1340",
-    "/ethi:tzaa": "\u1343",
-    "/ethi:tze": "\u1345",
-    "/ethi:tzee": "\u1344",
-    "/ethi:tzi": "\u1342",
-    "/ethi:tzo": "\u1346",
-    "/ethi:tzoa": "\u1347",
-    "/ethi:tzu": "\u1341",
-    "/ethi:uglottal": "\u12A1",
-    "/ethi:va": "\u1268",
-    "/ethi:vaa": "\u126B",
-    "/ethi:ve": "\u126D",
-    "/ethi:vee": "\u126C",
-    "/ethi:vi": "\u126A",
-    "/ethi:vo": "\u126E",
-    "/ethi:vowellengthmarkcmb": "\u135E",
-    "/ethi:vu": "\u1269",
-    "/ethi:vwa": "\u126F",
-    "/ethi:wa": "\u12C8",
-    "/ethi:waa": "\u12CB",
-    "/ethi:waglottal": "\u12A7",
-    "/ethi:we": "\u12CD",
-    "/ethi:wee": "\u12CC",
-    "/ethi:wi": "\u12CA",
-    "/ethi:wo": "\u12CE",
-    "/ethi:woa": "\u12CF",
-    "/ethi:wordspace": "\u1361",
-    "/ethi:wu": "\u12C9",
-    "/ethi:xa": "\u1280",
-    "/ethi:xaa": "\u1283",
-    "/ethi:xe": "\u1285",
-    "/ethi:xee": "\u1284",
-    "/ethi:xi": "\u1282",
-    "/ethi:xo": "\u1286",
-    "/ethi:xoa": "\u1287",
-    "/ethi:xu": "\u1281",
-    "/ethi:xwa": "\u1288",
-    "/ethi:xwaa": "\u128B",
-    "/ethi:xwe": "\u128D",
-    "/ethi:xwee": "\u128C",
-    "/ethi:xwi": "\u128A",
-    "/ethi:ya": "\u12E8",
-    "/ethi:yaa": "\u12EB",
-    "/ethi:ye": "\u12ED",
-    "/ethi:yee": "\u12EC",
-    "/ethi:yi": "\u12EA",
-    "/ethi:yo": "\u12EE",
-    "/ethi:yoa": "\u12EF",
-    "/ethi:yu": "\u12E9",
-    "/ethi:za": "\u12D8",
-    "/ethi:zaa": "\u12DB",
-    "/ethi:ze": "\u12DD",
-    "/ethi:zee": "\u12DC",
-    "/ethi:zha": "\u12E0",
-    "/ethi:zhaa": "\u12E3",
-    "/ethi:zhe": "\u12E5",
-    "/ethi:zhee": "\u12E4",
-    "/ethi:zhi": "\u12E2",
-    "/ethi:zho": "\u12E6",
-    "/ethi:zhu": "\u12E1",
-    "/ethi:zhwa": "\u12E7",
-    "/ethi:zi": "\u12DA",
-    "/ethi:zo": "\u12DE",
-    "/ethi:zu": "\u12D9",
-    "/ethi:zwa": "\u12DF",
-    "/etilde": "\u1EBD",
-    "/etildebelow": "\u1E1B",
-    "/etnahta:hb": "\u0591",
-    "/etnahtafoukhhebrew": "\u0591",
-    "/etnahtafoukhlefthebrew": "\u0591",
-    "/etnahtahebrew": "\u0591",
-    "/etnahtalefthebrew": "\u0591",
-    "/eturned": "\u01DD",
-    "/eukorean": "\u3161",
-    "/eukrcyr": "\u0454",
-    "/euler": "\u2107",
-    "/euro": "\u20AC",
-    "/euroarchaic": "\u20A0",
-    "/europeanCastle": "\u1F3F0",
-    "/europeanPostOffice": "\u1F3E4",
-    "/evergreenTree": "\u1F332",
-    "/evowelsignbengali": "\u09C7",
-    "/evowelsigndeva": "\u0947",
-    "/evowelsigngujarati": "\u0AC7",
-    "/excellentideographiccircled": "\u329D",
-    "/excess": "\u2239",
-    "/exclam": "\u0021",
-    "/exclamarmenian": "\u055C",
-    "/exclamationquestion": "\u2049",
-    "/exclamdbl": "\u203C",
-    "/exclamdown": "\u00A1",
-    "/exclamdownsmall": "\uF7A1",
-    "/exclammonospace": "\uFF01",
-    "/exclamsmall": "\uF721",
-    "/existential": "\u2203",
-    "/expressionlessFace": "\u1F611",
-    "/extraterrestrialAlien": "\u1F47D",
-    "/eye": "\u1F441",
-    "/eyeglasses": "\u1F453",
-    "/eyes": "\u1F440",
-    "/ezh": "\u0292",
-    "/ezhcaron": "\u01EF",
-    "/ezhcurl": "\u0293",
-    "/ezhreversed": "\u01B9",
-    "/ezhtail": "\u01BA",
-    "/f": "\u0066",
-    "/f_f": "\uFB00",
-    "/f_f_i": "\uFB03",
-    "/f_f_l": "\uFB04",
-    "/faceMassage": "\u1F486",
-    "/faceSavouringDeliciousFood": "\u1F60B",
-    "/faceScreamingInFear": "\u1F631",
-    "/faceThrowingAKiss": "\u1F618",
-    "/faceWithColdSweat": "\u1F613",
-    "/faceWithLookOfTriumph": "\u1F624",
-    "/faceWithMedicalMask": "\u1F637",
-    "/faceWithNoGoodGesture": "\u1F645",
-    "/faceWithOkGesture": "\u1F646",
-    "/faceWithOpenMouth": "\u1F62E",
-    "/faceWithOpenMouthAndColdSweat": "\u1F630",
-    "/faceWithRollingEyes": "\u1F644",
-    "/faceWithStuckOutTongue": "\u1F61B",
-    "/faceWithStuckOutTongueAndTightlyClosedEyes": "\u1F61D",
-    "/faceWithStuckOutTongueAndWinkingEye": "\u1F61C",
-    "/faceWithTearsOfJoy": "\u1F602",
-    "/faceWithoutMouth": "\u1F636",
-    "/facsimile": "\u213B",
-    "/factory": "\u1F3ED",
-    "/fadeva": "\u095E",
-    "/fagurmukhi": "\u0A5E",
-    "/fahrenheit": "\u2109",
-    "/fallenLeaf": "\u1F342",
-    "/fallingdiagonal": "\u27CD",
-    "/fallingdiagonalincircleinsquareblackwhite": "\u26DE",
-    "/family": "\u1F46A",
-    "/farsi": "\u262B",
-    "/farsiYehDigitFourBelow": "\u0777",
-    "/farsiYehDigitThreeAbove": "\u0776",
-    "/farsiYehDigitTwoAbove": "\u0775",
-    "/fatha": "\u064E",
-    "/fathaIsol": "\uFE76",
-    "/fathaMedi": "\uFE77",
-    "/fathaarabic": "\u064E",
-    "/fathalowarabic": "\u064E",
-    "/fathasmall": "\u0618",
-    "/fathatan": "\u064B",
-    "/fathatanIsol": "\uFE70",
-    "/fathatanarabic": "\u064B",
-    "/fathatwodotsdots": "\u065E",
-    "/fatherChristmas": "\u1F385",
-    "/faxIcon": "\u1F5B7",
-    "/faxMachine": "\u1F4E0",
-    "/fbopomofo": "\u3108",
-    "/fcircle": "\u24D5",
-    "/fdot": "\u1E1F",
-    "/fdotaccent": "\u1E1F",
-    "/fearfulFace": "\u1F628",
-    "/februarytelegraph": "\u32C1",
-    "/feh.fina": "\uFED2",
-    "/feh.init": "\uFED3",
-    "/feh.init_alefmaksura.fina": "\uFC31",
-    "/feh.init_hah.fina": "\uFC2E",
-    "/feh.init_hah.medi": "\uFCBF",
-    "/feh.init_jeem.fina": "\uFC2D",
-    "/feh.init_jeem.medi": "\uFCBE",
-    "/feh.init_khah.fina": "\uFC2F",
-    "/feh.init_khah.medi": "\uFCC0",
-    "/feh.init_khah.medi_meem.medi": "\uFD7D",
-    "/feh.init_meem.fina": "\uFC30",
-    "/feh.init_meem.medi": "\uFCC1",
-    "/feh.init_yeh.fina": "\uFC32",
-    "/feh.isol": "\uFED1",
-    "/feh.medi": "\uFED4",
-    "/feh.medi_alefmaksura.fina": "\uFC7C",
-    "/feh.medi_khah.medi_meem.fina": "\uFD7C",
-    "/feh.medi_meem.medi_yeh.fina": "\uFDC1",
-    "/feh.medi_yeh.fina": "\uFC7D",
-    "/fehThreeDotsUpBelow": "\u0761",
-    "/fehTwoDotsBelow": "\u0760",
-    "/feharabic": "\u0641",
-    "/feharmenian": "\u0586",
-    "/fehdotbelow": "\u06A3",
-    "/fehdotbelowright": "\u06A2",
-    "/fehfinalarabic": "\uFED2",
-    "/fehinitialarabic": "\uFED3",
-    "/fehmedialarabic": "\uFED4",
-    "/fehthreedotsbelow": "\u06A5",
-    "/feicoptic": "\u03E5",
-    "/female": "\u2640",
-    "/femaleideographiccircled": "\u329B",
-    "/feng": "\u02A9",
-    "/ferrisWheel": "\u1F3A1",
-    "/ferry": "\u26F4",
-    "/festivalideographicparen": "\u3240",
-    "/ff": "\uFB00",
-    "/ffi": "\uFB03",
-    "/ffl": "\uFB04",
-    "/fhook": "\u0192",
-    "/fi": "\uFB01",  # ligature "fi"
-    "/fieldHockeyStickAndBall": "\u1F3D1",
-    "/fifteencircle": "\u246E",
-    "/fifteencircleblack": "\u24EF",
-    "/fifteenparen": "\u2482",
-    "/fifteenparenthesized": "\u2482",
-    "/fifteenperiod": "\u2496",
-    "/fifty.roman": "\u216C",
-    "/fifty.romansmall": "\u217C",
-    "/fiftycircle": "\u32BF",
-    "/fiftycirclesquare": "\u324C",
-    "/fiftyearlyform.roman": "\u2186",
-    "/fiftythousand.roman": "\u2187",
-    "/figuredash": "\u2012",
-    "/figurespace": "\u2007",
-    "/fileCabinet": "\u1F5C4",
-    "/fileFolder": "\u1F4C1",
-    "/filledbox": "\u25A0",
-    "/filledrect": "\u25AC",
-    "/filledstopabove": "\u06EC",
-    "/filmFrames": "\u1F39E",
-    "/filmProjector": "\u1F4FD",
-    "/finalkaf": "\u05DA",
-    "/finalkaf:hb": "\u05DA",
-    "/finalkafdagesh": "\uFB3A",
-    "/finalkafdageshhebrew": "\uFB3A",
-    "/finalkafhebrew": "\u05DA",
-    "/finalkafqamats": "\u05DA",
-    "/finalkafqamatshebrew": "\u05DA",
-    "/finalkafsheva": "\u05DA",
-    "/finalkafshevahebrew": "\u05DA",
-    "/finalkafwithdagesh:hb": "\uFB3A",
-    "/finalmem": "\u05DD",
-    "/finalmem:hb": "\u05DD",
-    "/finalmemhebrew": "\u05DD",
-    "/finalmemwide:hb": "\uFB26",
-    "/finalnun": "\u05DF",
-    "/finalnun:hb": "\u05DF",
-    "/finalnunhebrew": "\u05DF",
-    "/finalpe": "\u05E3",
-    "/finalpe:hb": "\u05E3",
-    "/finalpehebrew": "\u05E3",
-    "/finalpewithdagesh:hb": "\uFB43",
-    "/finalsigma": "\u03C2",
-    "/finaltsadi": "\u05E5",
-    "/finaltsadi:hb": "\u05E5",
-    "/finaltsadihebrew": "\u05E5",
-    "/financialideographiccircled": "\u3296",
-    "/financialideographicparen": "\u3236",
-    "/finsular": "\uA77C",
-    "/fire": "\u1F525",
-    "/fireEngine": "\u1F692",
-    "/fireideographiccircled": "\u328B",
-    "/fireideographicparen": "\u322B",
-    "/fireworkSparkler": "\u1F387",
-    "/fireworks": "\u1F386",
-    "/firstQuarterMoon": "\u1F313",
-    "/firstQuarterMoonFace": "\u1F31B",
-    "/firstquartermoon": "\u263D",
-    "/firststrongisolate": "\u2068",
-    "/firsttonechinese": "\u02C9",
-    "/fish": "\u1F41F",
-    "/fishCakeSwirlDesign": "\u1F365",
-    "/fisheye": "\u25C9",
-    "/fishingPoleAndFish": "\u1F3A3",
-    "/fistedHandSign": "\u1F44A",
-    "/fitacyr": "\u0473",
-    "/fitacyrillic": "\u0473",
-    "/five": "\u0035",
-    "/five.inferior": "\u2085",
-    "/five.roman": "\u2164",
-    "/five.romansmall": "\u2174",
-    "/five.superior": "\u2075",
-    "/fivearabic": "\u0665",
-    "/fivebengali": "\u09EB",
-    "/fivecircle": "\u2464",
-    "/fivecircledbl": "\u24F9",
-    "/fivecircleinversesansserif": "\u278E",
-    "/fivecomma": "\u1F106",
-    "/fivedeva": "\u096B",
-    "/fivedot": "\u2E2D",
-    "/fivedotpunctuation": "\u2059",
-    "/fiveeighths": "\u215D",
-    "/fivefar": "\u06F5",
-    "/fivegujarati": "\u0AEB",
-    "/fivegurmukhi": "\u0A6B",
-    "/fivehackarabic": "\u0665",
-    "/fivehangzhou": "\u3025",
-    "/fivehundred.roman": "\u216E",
-    "/fivehundred.romansmall": "\u217E",
-    "/fiveideographiccircled": "\u3284",
-    "/fiveideographicparen": "\u3224",
-    "/fiveinferior": "\u2085",
-    "/fivemonospace": "\uFF15",
-    "/fiveoldstyle": "\uF735",
-    "/fiveparen": "\u2478",
-    "/fiveparenthesized": "\u2478",
-    "/fiveperiod": "\u248C",
-    "/fivepersian": "\u06F5",
-    "/fivepointedstar": "\u066D",
-    "/fivepointonesquare": "\u1F1A0",
-    "/fiveroman": "\u2174",
-    "/fivesixths": "\u215A",
-    "/fivesuperior": "\u2075",
-    "/fivethai": "\u0E55",
-    "/fivethousand.roman": "\u2181",
-    "/fl": "\uFB02",
-    "/flagblack": "\u2691",
-    "/flaghorizontalmiddlestripeblackwhite": "\u26FF",
-    "/flaginhole": "\u26F3",
-    "/flagwhite": "\u2690",
-    "/flatness": "\u23E5",
-    "/fleurdelis": "\u269C",
-    "/flexedBiceps": "\u1F4AA",
-    "/floorleft": "\u230A",
-    "/floorright": "\u230B",
-    "/floppyDisk": "\u1F4BE",
-    "/floralheartbulletreversedrotated": "\u2619",
-    "/florin": "\u0192",
-    "/flower": "\u2698",
-    "/flowerPlayingCards": "\u1F3B4",
-    "/flowerpunctuationmark": "\u2055",
-    "/flushedFace": "\u1F633",
-    "/flyingEnvelope": "\u1F585",
-    "/flyingSaucer": "\u1F6F8",
-    "/fmfullwidth": "\u3399",
-    "/fmonospace": "\uFF46",
-    "/fmsquare": "\u3399",
-    "/fofanthai": "\u0E1F",
-    "/fofathai": "\u0E1D",
-    "/fog": "\u1F32B",
-    "/foggy": "\u1F301",
-    "/folder": "\u1F5C0",
-    "/fongmanthai": "\u0E4F",
-    "/footnote": "\u0602",
-    "/footprints": "\u1F463",
-    "/footsquare": "\u23CD",
-    "/forall": "\u2200",
-    "/forces": "\u22A9",
-    "/fork": "\u2442",
-    "/forkKnife": "\u1F374",
-    "/forkKnifePlate": "\u1F37D",
-    "/forsamaritan": "\u214F",
-    "/fortycircle": "\u32B5",
-    "/fortycirclesquare": "\u324B",
-    "/fortyeightcircle": "\u32BD",
-    "/fortyfivecircle": "\u32BA",
-    "/fortyfourcircle": "\u32B9",
-    "/fortyninecircle": "\u32BE",
-    "/fortyonecircle": "\u32B6",
-    "/fortysevencircle": "\u32BC",
-    "/fortysixcircle": "\u32BB",
-    "/fortythreecircle": "\u32B8",
-    "/fortytwocircle": "\u32B7",
-    "/fountain": "\u26F2",
-    "/four": "\u0034",
-    "/four.inferior": "\u2084",
-    "/four.roman": "\u2163",
-    "/four.romansmall": "\u2173",
-    "/four.superior": "\u2074",
-    "/fourLeafClover": "\u1F340",
-    "/fourarabic": "\u0664",
-    "/fourbengali": "\u09EA",
-    "/fourcircle": "\u2463",
-    "/fourcircledbl": "\u24F8",
-    "/fourcircleinversesansserif": "\u278D",
-    "/fourcomma": "\u1F105",
-    "/fourdeva": "\u096A",
-    "/fourdotmark": "\u205B",
-    "/fourdotpunctuation": "\u2058",
-    "/fourfar": "\u06F4",
-    "/fourfifths": "\u2158",
-    "/fourgujarati": "\u0AEA",
-    "/fourgurmukhi": "\u0A6A",
-    "/fourhackarabic": "\u0664",
-    "/fourhangzhou": "\u3024",
-    "/fourideographiccircled": "\u3283",
-    "/fourideographicparen": "\u3223",
-    "/fourinferior": "\u2084",
-    "/fourksquare": "\u1F19E",
-    "/fourmonospace": "\uFF14",
-    "/fournumeratorbengali": "\u09F7",
-    "/fouroldstyle": "\uF734",
-    "/fourparen": "\u2477",
-    "/fourparenthesized": "\u2477",
-    "/fourperemspace": "\u2005",
-    "/fourperiod": "\u248B",
-    "/fourpersian": "\u06F4",
-    "/fourroman": "\u2173",
-    "/foursuperior": "\u2074",
-    "/fourteencircle": "\u246D",
-    "/fourteencircleblack": "\u24EE",
-    "/fourteenparen": "\u2481",
-    "/fourteenparenthesized": "\u2481",
-    "/fourteenperiod": "\u2495",
-    "/fourthai": "\u0E54",
-    "/fourthtonechinese": "\u02CB",
-    "/fparen": "\u24A1",
-    "/fparenthesized": "\u24A1",
-    "/fraction": "\u2044",
-    "/frameAnX": "\u1F5BE",
-    "/framePicture": "\u1F5BC",
-    "/frameTiles": "\u1F5BD",
-    "/franc": "\u20A3",
-    "/freesquare": "\u1F193",
-    "/frenchFries": "\u1F35F",
-    "/freversedepigraphic": "\uA7FB",
-    "/friedShrimp": "\u1F364",
-    "/frogFace": "\u1F438",
-    "/front-facingBabyChick": "\u1F425",
-    "/frown": "\u2322",
-    "/frowningFaceWithOpenMouth": "\u1F626",
-    "/frowningfacewhite": "\u2639",
-    "/fstroke": "\uA799",
-    "/fturned": "\u214E",
-    "/fuelpump": "\u26FD",
-    "/fullBlock": "\u2588",
-    "/fullMoon": "\u1F315",
-    "/fullMoonFace": "\u1F31D",
-    "/functionapplication": "\u2061",
-    "/funeralurn": "\u26B1",
-    "/fuse": "\u23DB",
-    "/fwd:A": "\uFF21",
-    "/fwd:B": "\uFF22",
-    "/fwd:C": "\uFF23",
-    "/fwd:D": "\uFF24",
-    "/fwd:E": "\uFF25",
-    "/fwd:F": "\uFF26",
-    "/fwd:G": "\uFF27",
-    "/fwd:H": "\uFF28",
-    "/fwd:I": "\uFF29",
-    "/fwd:J": "\uFF2A",
-    "/fwd:K": "\uFF2B",
-    "/fwd:L": "\uFF2C",
-    "/fwd:M": "\uFF2D",
-    "/fwd:N": "\uFF2E",
-    "/fwd:O": "\uFF2F",
-    "/fwd:P": "\uFF30",
-    "/fwd:Q": "\uFF31",
-    "/fwd:R": "\uFF32",
-    "/fwd:S": "\uFF33",
-    "/fwd:T": "\uFF34",
-    "/fwd:U": "\uFF35",
-    "/fwd:V": "\uFF36",
-    "/fwd:W": "\uFF37",
-    "/fwd:X": "\uFF38",
-    "/fwd:Y": "\uFF39",
-    "/fwd:Z": "\uFF3A",
-    "/fwd:a": "\uFF41",
-    "/fwd:ampersand": "\uFF06",
-    "/fwd:asciicircum": "\uFF3E",
-    "/fwd:asciitilde": "\uFF5E",
-    "/fwd:asterisk": "\uFF0A",
-    "/fwd:at": "\uFF20",
-    "/fwd:b": "\uFF42",
-    "/fwd:backslash": "\uFF3C",
-    "/fwd:bar": "\uFF5C",
-    "/fwd:braceleft": "\uFF5B",
-    "/fwd:braceright": "\uFF5D",
-    "/fwd:bracketleft": "\uFF3B",
-    "/fwd:bracketright": "\uFF3D",
-    "/fwd:brokenbar": "\uFFE4",
-    "/fwd:c": "\uFF43",
-    "/fwd:centsign": "\uFFE0",
-    "/fwd:colon": "\uFF1A",
-    "/fwd:comma": "\uFF0C",
-    "/fwd:d": "\uFF44",
-    "/fwd:dollar": "\uFF04",
-    "/fwd:e": "\uFF45",
-    "/fwd:eight": "\uFF18",
-    "/fwd:equal": "\uFF1D",
-    "/fwd:exclam": "\uFF01",
-    "/fwd:f": "\uFF46",
-    "/fwd:five": "\uFF15",
-    "/fwd:four": "\uFF14",
-    "/fwd:g": "\uFF47",
-    "/fwd:grave": "\uFF40",
-    "/fwd:greater": "\uFF1E",
-    "/fwd:h": "\uFF48",
-    "/fwd:hyphen": "\uFF0D",
-    "/fwd:i": "\uFF49",
-    "/fwd:j": "\uFF4A",
-    "/fwd:k": "\uFF4B",
-    "/fwd:l": "\uFF4C",
-    "/fwd:leftwhiteparenthesis": "\uFF5F",
-    "/fwd:less": "\uFF1C",
-    "/fwd:m": "\uFF4D",
-    "/fwd:macron": "\uFFE3",
-    "/fwd:n": "\uFF4E",
-    "/fwd:nine": "\uFF19",
-    "/fwd:notsign": "\uFFE2",
-    "/fwd:numbersign": "\uFF03",
-    "/fwd:o": "\uFF4F",
-    "/fwd:one": "\uFF11",
-    "/fwd:p": "\uFF50",
-    "/fwd:parenthesisleft": "\uFF08",
-    "/fwd:parenthesisright": "\uFF09",
-    "/fwd:percent": "\uFF05",
-    "/fwd:period": "\uFF0E",
-    "/fwd:plus": "\uFF0B",
-    "/fwd:poundsign": "\uFFE1",
-    "/fwd:q": "\uFF51",
-    "/fwd:question": "\uFF1F",
-    "/fwd:quotedbl": "\uFF02",
-    "/fwd:quotesingle": "\uFF07",
-    "/fwd:r": "\uFF52",
-    "/fwd:rightwhiteparenthesis": "\uFF60",
-    "/fwd:s": "\uFF53",
-    "/fwd:semicolon": "\uFF1B",
-    "/fwd:seven": "\uFF17",
-    "/fwd:six": "\uFF16",
-    "/fwd:slash": "\uFF0F",
-    "/fwd:t": "\uFF54",
-    "/fwd:three": "\uFF13",
-    "/fwd:two": "\uFF12",
-    "/fwd:u": "\uFF55",
-    "/fwd:underscore": "\uFF3F",
-    "/fwd:v": "\uFF56",
-    "/fwd:w": "\uFF57",
-    "/fwd:wonsign": "\uFFE6",
-    "/fwd:x": "\uFF58",
-    "/fwd:y": "\uFF59",
-    "/fwd:yensign": "\uFFE5",
-    "/fwd:z": "\uFF5A",
-    "/fwd:zero": "\uFF10",
-    "/g": "\u0067",
-    "/gabengali": "\u0997",
-    "/gacute": "\u01F5",
-    "/gadeva": "\u0917",
-    "/gaf": "\u06AF",
-    "/gaf.fina": "\uFB93",
-    "/gaf.init": "\uFB94",
-    "/gaf.isol": "\uFB92",
-    "/gaf.medi": "\uFB95",
-    "/gafarabic": "\u06AF",
-    "/gaffinalarabic": "\uFB93",
-    "/gafinitialarabic": "\uFB94",
-    "/gafmedialarabic": "\uFB95",
-    "/gafring": "\u06B0",
-    "/gafthreedotsabove": "\u06B4",
-    "/gaftwodotsbelow": "\u06B2",
-    "/gagujarati": "\u0A97",
-    "/gagurmukhi": "\u0A17",
-    "/gahiragana": "\u304C",
-    "/gakatakana": "\u30AC",
-    "/galsquare": "\u33FF",
-    "/gameDie": "\u1F3B2",
-    "/gamma": "\u03B3",
-    "/gammadblstruck": "\u213D",
-    "/gammalatinsmall": "\u0263",
-    "/gammasuperior": "\u02E0",
-    "/gammasupmod": "\u02E0",
-    "/gamurda": "\uA993",
-    "/gangiacoptic": "\u03EB",
-    "/ganmasquare": "\u330F",
-    "/garonsquare": "\u330E",
-    "/gbfullwidth": "\u3387",
-    "/gbopomofo": "\u310D",
-    "/gbreve": "\u011F",
-    "/gcaron": "\u01E7",
-    "/gcedilla": "\u0123",
-    "/gcircle": "\u24D6",
-    "/gcircumflex": "\u011D",
-    "/gcommaaccent": "\u0123",
-    "/gdot": "\u0121",
-    "/gdotaccent": "\u0121",
-    "/gear": "\u2699",
-    "/gearhles": "\u26EE",
-    "/gearouthub": "\u26ED",
-    "/gecyr": "\u0433",
-    "/gecyrillic": "\u0433",
-    "/gehiragana": "\u3052",
-    "/gehookcyr": "\u0495",
-    "/gehookstrokecyr": "\u04FB",
-    "/gekatakana": "\u30B2",
-    "/gemStone": "\u1F48E",
-    "/gemini": "\u264A",
-    "/geometricallyequal": "\u2251",
-    "/geometricallyequivalent": "\u224E",
-    "/geometricproportion": "\u223A",
-    "/geresh:hb": "\u05F3",
-    "/gereshMuqdam:hb": "\u059D",
-    "/gereshaccenthebrew": "\u059C",
-    "/gereshhebrew": "\u05F3",
-    "/gereshmuqdamhebrew": "\u059D",
-    "/germandbls": "\u00DF",
-    "/germanpenny": "\u20B0",
-    "/gershayim:hb": "\u05F4",
-    "/gershayimaccenthebrew": "\u059E",
-    "/gershayimhebrew": "\u05F4",
-    "/gestrokecyr": "\u0493",
-    "/getailcyr": "\u04F7",
-    "/getamark": "\u3013",
-    "/geupcyr": "\u0491",
-    "/ghabengali": "\u0998",
-    "/ghadarmenian": "\u0572",
-    "/ghadeva": "\u0918",
-    "/ghagujarati": "\u0A98",
-    "/ghagurmukhi": "\u0A18",
-    "/ghain": "\u063A",
-    "/ghain.fina": "\uFECE",
-    "/ghain.init": "\uFECF",
-    "/ghain.init_alefmaksura.fina": "\uFCF9",
-    "/ghain.init_jeem.fina": "\uFC2B",
-    "/ghain.init_jeem.medi": "\uFCBC",
-    "/ghain.init_meem.fina": "\uFC2C",
-    "/ghain.init_meem.medi": "\uFCBD",
-    "/ghain.init_yeh.fina": "\uFCFA",
-    "/ghain.isol": "\uFECD",
-    "/ghain.medi": "\uFED0",
-    "/ghain.medi_alefmaksura.fina": "\uFD15",
-    "/ghain.medi_meem.medi_alefmaksura.fina": "\uFD7B",
-    "/ghain.medi_meem.medi_meem.fina": "\uFD79",
-    "/ghain.medi_meem.medi_yeh.fina": "\uFD7A",
-    "/ghain.medi_yeh.fina": "\uFD16",
-    "/ghainarabic": "\u063A",
-    "/ghaindotbelow": "\u06FC",
-    "/ghainfinalarabic": "\uFECE",
-    "/ghaininitialarabic": "\uFECF",
-    "/ghainmedialarabic": "\uFED0",
-    "/ghemiddlehookcyrillic": "\u0495",
-    "/ghestrokecyrillic": "\u0493",
-    "/gheupturncyrillic": "\u0491",
-    "/ghhadeva": "\u095A",
-    "/ghhagurmukhi": "\u0A5A",
-    "/ghook": "\u0260",
-    "/ghost": "\u1F47B",
-    "/ghzfullwidth": "\u3393",
-    "/ghzsquare": "\u3393",
-    "/gigasquare": "\u3310",
-    "/gihiragana": "\u304E",
-    "/gikatakana": "\u30AE",
-    "/gimarmenian": "\u0563",
-    "/gimel": "\u05D2",
-    "/gimel:hb": "\u05D2",
-    "/gimeldagesh": "\uFB32",
-    "/gimeldageshhebrew": "\uFB32",
-    "/gimelhebrew": "\u05D2",
-    "/gimelwithdagesh:hb": "\uFB32",
-    "/giniisquare": "\u3311",
-    "/ginsularturned": "\uA77F",
-    "/girl": "\u1F467",
-    "/girls": "\u1F6CA",
-    "/girudaasquare": "\u3313",
-    "/gjecyr": "\u0453",
-    "/gjecyrillic": "\u0453",
-    "/globeMeridians": "\u1F310",
-    "/glottalinvertedstroke": "\u01BE",
-    "/glottalstop": "\u0294",
-    "/glottalstopinverted": "\u0296",
-    "/glottalstopmod": "\u02C0",
-    "/glottalstopreversed": "\u0295",
-    "/glottalstopreversedmod": "\u02C1",
-    "/glottalstopreversedsuperior": "\u02E4",
-    "/glottalstopstroke": "\u02A1",
-    "/glottalstopstrokereversed": "\u02A2",
-    "/glottalstopsupreversedmod": "\u02E4",
-    "/glowingStar": "\u1F31F",
-    "/gmacron": "\u1E21",
-    "/gmonospace": "\uFF47",
-    "/gmtr:diamondblack": "\u25C6",
-    "/gmtr:diamondwhite": "\u25C7",
-    "/gnrl:hyphen": "\u2010",
-    "/goat": "\u1F410",
-    "/gobliquestroke": "\uA7A1",
-    "/gohiragana": "\u3054",
-    "/gokatakana": "\u30B4",
-    "/golfer": "\u1F3CC",
-    "/gpafullwidth": "\u33AC",
-    "/gparen": "\u24A2",
-    "/gparenthesized": "\u24A2",
-    "/gpasquare": "\u33AC",
-    "/gr:acute": "\u1FFD",
-    "/gr:grave": "\u1FEF",
-    "/gr:question": "\u037E",
-    "/gr:tilde": "\u1FC0",
-    "/gradient": "\u2207",
-    "/graduationCap": "\u1F393",
-    "/grapes": "\u1F347",
-    "/grave": "\u0060",
-    "/gravebelowcmb": "\u0316",
-    "/gravecmb": "\u0300",
-    "/gravecomb": "\u0300",
-    "/gravedblmiddlemod": "\u02F5",
-    "/gravedeva": "\u0953",
-    "/gravelowmod": "\u02CE",
-    "/gravemiddlemod": "\u02F4",
-    "/gravemod": "\u02CB",
-    "/gravemonospace": "\uFF40",
-    "/gravetonecmb": "\u0340",
-    "/greater": "\u003E",
-    "/greaterbutnotequal": "\u2269",
-    "/greaterbutnotequivalent": "\u22E7",
-    "/greaterdot": "\u22D7",
-    "/greaterequal": "\u2265",
-    "/greaterequalorless": "\u22DB",
-    "/greatermonospace": "\uFF1E",
-    "/greaterorequivalent": "\u2273",
-    "/greaterorless": "\u2277",
-    "/greateroverequal": "\u2267",
-    "/greatersmall": "\uFE65",
-    "/greenApple": "\u1F34F",
-    "/greenBook": "\u1F4D7",
-    "/greenHeart": "\u1F49A",
-    "/grimacingFace": "\u1F62C",
-    "/grinningCatFaceWithSmilingEyes": "\u1F638",
-    "/grinningFace": "\u1F600",
-    "/grinningFaceWithSmilingEyes": "\u1F601",
-    "/growingHeart": "\u1F497",
-    "/gscript": "\u0261",
-    "/gstroke": "\u01E5",
-    "/guarani": "\u20B2",
-    "/guardsman": "\u1F482",
-    "/gueh": "\u06B3",
-    "/gueh.fina": "\uFB97",
-    "/gueh.init": "\uFB98",
-    "/gueh.isol": "\uFB96",
-    "/gueh.medi": "\uFB99",
-    "/guhiragana": "\u3050",
-    "/guillemetleft": "\u00AB",
-    "/guillemetright": "\u00BB",
-    "/guillemotleft": "\u00AB",
-    "/guillemotright": "\u00BB",
-    "/guilsinglleft": "\u2039",
-    "/guilsinglright": "\u203A",
-    "/guitar": "\u1F3B8",
-    "/gujr:a": "\u0A85",
-    "/gujr:aa": "\u0A86",
-    "/gujr:aasign": "\u0ABE",
-    "/gujr:abbreviation": "\u0AF0",
-    "/gujr:ai": "\u0A90",
-    "/gujr:aisign": "\u0AC8",
-    "/gujr:anusvara": "\u0A82",
-    "/gujr:au": "\u0A94",
-    "/gujr:ausign": "\u0ACC",
-    "/gujr:avagraha": "\u0ABD",
-    "/gujr:ba": "\u0AAC",
-    "/gujr:bha": "\u0AAD",
-    "/gujr:binducandra": "\u0A81",
-    "/gujr:ca": "\u0A9A",
-    "/gujr:cha": "\u0A9B",
-    "/gujr:circlenuktaabove": "\u0AFE",
-    "/gujr:da": "\u0AA6",
-    "/gujr:dda": "\u0AA1",
-    "/gujr:ddha": "\u0AA2",
-    "/gujr:dha": "\u0AA7",
-    "/gujr:e": "\u0A8F",
-    "/gujr:ecandra": "\u0A8D",
-    "/gujr:eight": "\u0AEE",
-    "/gujr:esign": "\u0AC7",
-    "/gujr:esigncandra": "\u0AC5",
-    "/gujr:five": "\u0AEB",
-    "/gujr:four": "\u0AEA",
-    "/gujr:ga": "\u0A97",
-    "/gujr:gha": "\u0A98",
-    "/gujr:ha": "\u0AB9",
-    "/gujr:i": "\u0A87",
-    "/gujr:ii": "\u0A88",
-    "/gujr:iisign": "\u0AC0",
-    "/gujr:isign": "\u0ABF",
-    "/gujr:ja": "\u0A9C",
-    "/gujr:jha": "\u0A9D",
-    "/gujr:ka": "\u0A95",
-    "/gujr:kha": "\u0A96",
-    "/gujr:la": "\u0AB2",
-    "/gujr:lla": "\u0AB3",
-    "/gujr:llvocal": "\u0AE1",
-    "/gujr:llvocalsign": "\u0AE3",
-    "/gujr:lvocal": "\u0A8C",
-    "/gujr:lvocalsign": "\u0AE2",
-    "/gujr:ma": "\u0AAE",
-    "/gujr:maddah": "\u0AFC",
-    "/gujr:na": "\u0AA8",
-    "/gujr:nga": "\u0A99",
-    "/gujr:nine": "\u0AEF",
-    "/gujr:nna": "\u0AA3",
-    "/gujr:nukta": "\u0ABC",
-    "/gujr:nya": "\u0A9E",
-    "/gujr:o": "\u0A93",
-    "/gujr:ocandra": "\u0A91",
-    "/gujr:om": "\u0AD0",
-    "/gujr:one": "\u0AE7",
-    "/gujr:osign": "\u0ACB",
-    "/gujr:osigncandra": "\u0AC9",
-    "/gujr:pa": "\u0AAA",
-    "/gujr:pha": "\u0AAB",
-    "/gujr:ra": "\u0AB0",
-    "/gujr:rrvocal": "\u0AE0",
-    "/gujr:rrvocalsign": "\u0AC4",
-    "/gujr:rupee": "\u0AF1",
-    "/gujr:rvocal": "\u0A8B",
-    "/gujr:rvocalsign": "\u0AC3",
-    "/gujr:sa": "\u0AB8",
-    "/gujr:seven": "\u0AED",
-    "/gujr:sha": "\u0AB6",
-    "/gujr:shadda": "\u0AFB",
-    "/gujr:six": "\u0AEC",
-    "/gujr:ssa": "\u0AB7",
-    "/gujr:sukun": "\u0AFA",
-    "/gujr:ta": "\u0AA4",
-    "/gujr:tha": "\u0AA5",
-    "/gujr:three": "\u0AE9",
-    "/gujr:three-dotnuktaabove": "\u0AFD",
-    "/gujr:tta": "\u0A9F",
-    "/gujr:ttha": "\u0AA0",
-    "/gujr:two": "\u0AE8",
-    "/gujr:two-circlenuktaabove": "\u0AFF",
-    "/gujr:u": "\u0A89",
-    "/gujr:usign": "\u0AC1",
-    "/gujr:uu": "\u0A8A",
-    "/gujr:uusign": "\u0AC2",
-    "/gujr:va": "\u0AB5",
-    "/gujr:virama": "\u0ACD",
-    "/gujr:visarga": "\u0A83",
-    "/gujr:ya": "\u0AAF",
-    "/gujr:zero": "\u0AE6",
-    "/gujr:zha": "\u0AF9",
-    "/gukatakana": "\u30B0",
-    "/guramusquare": "\u3318",
-    "/guramutonsquare": "\u3319",
-    "/guru:a": "\u0A05",
-    "/guru:aa": "\u0A06",
-    "/guru:aasign": "\u0A3E",
-    "/guru:adakbindisign": "\u0A01",
-    "/guru:addak": "\u0A71",
-    "/guru:ai": "\u0A10",
-    "/guru:aisign": "\u0A48",
-    "/guru:au": "\u0A14",
-    "/guru:ausign": "\u0A4C",
-    "/guru:ba": "\u0A2C",
-    "/guru:bha": "\u0A2D",
-    "/guru:bindisign": "\u0A02",
-    "/guru:ca": "\u0A1A",
-    "/guru:cha": "\u0A1B",
-    "/guru:da": "\u0A26",
-    "/guru:dda": "\u0A21",
-    "/guru:ddha": "\u0A22",
-    "/guru:dha": "\u0A27",
-    "/guru:ee": "\u0A0F",
-    "/guru:eesign": "\u0A47",
-    "/guru:eight": "\u0A6E",
-    "/guru:ekonkar": "\u0A74",
-    "/guru:fa": "\u0A5E",
-    "/guru:five": "\u0A6B",
-    "/guru:four": "\u0A6A",
-    "/guru:ga": "\u0A17",
-    "/guru:gha": "\u0A18",
-    "/guru:ghha": "\u0A5A",
-    "/guru:ha": "\u0A39",
-    "/guru:i": "\u0A07",
-    "/guru:ii": "\u0A08",
-    "/guru:iisign": "\u0A40",
-    "/guru:iri": "\u0A72",
-    "/guru:isign": "\u0A3F",
-    "/guru:ja": "\u0A1C",
-    "/guru:jha": "\u0A1D",
-    "/guru:ka": "\u0A15",
-    "/guru:kha": "\u0A16",
-    "/guru:khha": "\u0A59",
-    "/guru:la": "\u0A32",
-    "/guru:lla": "\u0A33",
-    "/guru:ma": "\u0A2E",
-    "/guru:na": "\u0A28",
-    "/guru:nga": "\u0A19",
-    "/guru:nine": "\u0A6F",
-    "/guru:nna": "\u0A23",
-    "/guru:nukta": "\u0A3C",
-    "/guru:nya": "\u0A1E",
-    "/guru:one": "\u0A67",
-    "/guru:oo": "\u0A13",
-    "/guru:oosign": "\u0A4B",
-    "/guru:pa": "\u0A2A",
-    "/guru:pha": "\u0A2B",
-    "/guru:ra": "\u0A30",
-    "/guru:rra": "\u0A5C",
-    "/guru:sa": "\u0A38",
-    "/guru:seven": "\u0A6D",
-    "/guru:sha": "\u0A36",
-    "/guru:six": "\u0A6C",
-    "/guru:ta": "\u0A24",
-    "/guru:tha": "\u0A25",
-    "/guru:three": "\u0A69",
-    "/guru:tippi": "\u0A70",
-    "/guru:tta": "\u0A1F",
-    "/guru:ttha": "\u0A20",
-    "/guru:two": "\u0A68",
-    "/guru:u": "\u0A09",
-    "/guru:udaatsign": "\u0A51",
-    "/guru:ura": "\u0A73",
-    "/guru:usign": "\u0A41",
-    "/guru:uu": "\u0A0A",
-    "/guru:uusign": "\u0A42",
-    "/guru:va": "\u0A35",
-    "/guru:virama": "\u0A4D",
-    "/guru:visarga": "\u0A03",
-    "/guru:ya": "\u0A2F",
-    "/guru:yakashsign": "\u0A75",
-    "/guru:za": "\u0A5B",
-    "/guru:zero": "\u0A66",
-    "/gyfullwidth": "\u33C9",
-    "/gysquare": "\u33C9",
-    "/h": "\u0068",
-    "/h.inferior": "\u2095",
-    "/haabkhasiancyrillic": "\u04A9",
-    "/haabkhcyr": "\u04A9",
-    "/haaltonearabic": "\u06C1",
-    "/habengali": "\u09B9",
-    "/hacirclekatakana": "\u32E9",
-    "/hacyr": "\u0445",
-    "/hadescendercyrillic": "\u04B3",
-    "/hadeva": "\u0939",
-    "/hafullwidth": "\u33CA",
-    "/hagujarati": "\u0AB9",
-    "/hagurmukhi": "\u0A39",
-    "/hah": "\u062D",
-    "/hah.fina": "\uFEA2",
-    "/hah.init": "\uFEA3",
-    "/hah.init_alefmaksura.fina": "\uFCFF",
-    "/hah.init_jeem.fina": "\uFC17",
-    "/hah.init_jeem.medi": "\uFCA9",
-    "/hah.init_meem.fina": "\uFC18",
-    "/hah.init_meem.medi": "\uFCAA",
-    "/hah.init_yeh.fina": "\uFD00",
-    "/hah.isol": "\uFEA1",
-    "/hah.medi": "\uFEA4",
-    "/hah.medi_alefmaksura.fina": "\uFD1B",
-    "/hah.medi_jeem.medi_yeh.fina": "\uFDBF",
-    "/hah.medi_meem.medi_alefmaksura.fina": "\uFD5B",
-    "/hah.medi_meem.medi_yeh.fina": "\uFD5A",
-    "/hah.medi_yeh.fina": "\uFD1C",
-    "/hahDigitFourBelow": "\u077C",
-    "/hahSmallTahAbove": "\u0772",
-    "/hahSmallTahBelow": "\u076E",
-    "/hahSmallTahTwoDots": "\u076F",
-    "/hahThreeDotsUpBelow": "\u0758",
-    "/hahTwoDotsAbove": "\u0757",
-    "/haharabic": "\u062D",
-    "/hahfinalarabic": "\uFEA2",
-    "/hahhamza": "\u0681",
-    "/hahinitialarabic": "\uFEA3",
-    "/hahiragana": "\u306F",
-    "/hahmedialarabic": "\uFEA4",
-    "/hahookcyr": "\u04FD",
-    "/hahthreedotsabove": "\u0685",
-    "/hahtwodotsvertical": "\u0682",
-    "/haircut": "\u1F487",
-    "/hairspace": "\u200A",
-    "/haitusquare": "\u332A",
-    "/hakatakana": "\u30CF",
-    "/hakatakanahalfwidth": "\uFF8A",
-    "/halantgurmukhi": "\u0A4D",
-    "/halfcircleleftblack": "\u25D6",
-    "/halfcirclerightblack": "\u25D7",
-    "/hamburger": "\u1F354",
-    "/hammer": "\u1F528",
-    "/hammerAndWrench": "\u1F6E0",
-    "/hammerpick": "\u2692",
-    "/hammersickle": "\u262D",
-    "/hamsterFace": "\u1F439",
-    "/hamza": "\u0621",
-    "/hamzaIsol": "\uFE80",
-    "/hamzaabove": "\u0654",
-    "/hamzaarabic": "\u0621",
-    "/hamzabelow": "\u0655",
-    "/hamzadammaarabic": "\u0621",
-    "/hamzadammatanarabic": "\u0621",
-    "/hamzafathaarabic": "\u0621",
-    "/hamzafathatanarabic": "\u0621",
-    "/hamzalowarabic": "\u0621",
-    "/hamzalowkasraarabic": "\u0621",
-    "/hamzalowkasratanarabic": "\u0621",
-    "/hamzasukunarabic": "\u0621",
-    "/handbag": "\u1F45C",
-    "/handtailfishhookturned": "\u02AF",
-    "/hangulchieuchaparen": "\u3217",
-    "/hangulchieuchparen": "\u3209",
-    "/hangulcieucaparen": "\u3216",
-    "/hangulcieucparen": "\u3208",
-    "/hangulcieucuparen": "\u321C",
-    "/hanguldottonemarkdbl": "\u302F",
-    "/hangulfiller": "\u3164",
-    "/hangulhieuhaparen": "\u321B",
-    "/hangulhieuhparen": "\u320D",
-    "/hangulieungaparen": "\u3215",
-    "/hangulieungparen": "\u3207",
-    "/hangulkhieukhaparen": "\u3218",
-    "/hangulkhieukhparen": "\u320A",
-    "/hangulkiyeokaparen": "\u320E",
-    "/hangulkiyeokparen": "\u3200",
-    "/hangulmieumaparen": "\u3212",
-    "/hangulmieumparen": "\u3204",
-    "/hangulnieunaparen": "\u320F",
-    "/hangulnieunparen": "\u3201",
-    "/hangulphieuphaparen": "\u321A",
-    "/hangulphieuphparen": "\u320C",
-    "/hangulpieupaparen": "\u3213",
-    "/hangulpieupparen": "\u3205",
-    "/hangulrieulaparen": "\u3211",
-    "/hangulrieulparen": "\u3203",
-    "/hangulsingledottonemark": "\u302E",
-    "/hangulsiosaparen": "\u3214",
-    "/hangulsiosparen": "\u3206",
-    "/hangulthieuthaparen": "\u3219",
-    "/hangulthieuthparen": "\u320B",
-    "/hangultikeutaparen": "\u3210",
-    "/hangultikeutparen": "\u3202",
-    "/happyPersonRaisingOneHand": "\u1F64B",
-    "/hardDisk": "\u1F5B4",
-    "/hardcyr": "\u044A",
-    "/hardsigncyrillic": "\u044A",
-    "/harpoondownbarbleft": "\u21C3",
-    "/harpoondownbarbright": "\u21C2",
-    "/harpoonleftbarbdown": "\u21BD",
-    "/harpoonleftbarbup": "\u21BC",
-    "/harpoonrightbarbdown": "\u21C1",
-    "/harpoonrightbarbup": "\u21C0",
-    "/harpoonupbarbleft": "\u21BF",
-    "/harpoonupbarbright": "\u21BE",
-    "/hasquare": "\u33CA",
-    "/hastrokecyr": "\u04FF",
-    "/hatafPatah:hb": "\u05B2",
-    "/hatafQamats:hb": "\u05B3",
-    "/hatafSegol:hb": "\u05B1",
-    "/hatafpatah": "\u05B2",
-    "/hatafpatah16": "\u05B2",
-    "/hatafpatah23": "\u05B2",
-    "/hatafpatah2f": "\u05B2",
-    "/hatafpatahhebrew": "\u05B2",
-    "/hatafpatahnarrowhebrew": "\u05B2",
-    "/hatafpatahquarterhebrew": "\u05B2",
-    "/hatafpatahwidehebrew": "\u05B2",
-    "/hatafqamats": "\u05B3",
-    "/hatafqamats1b": "\u05B3",
-    "/hatafqamats28": "\u05B3",
-    "/hatafqamats34": "\u05B3",
-    "/hatafqamatshebrew": "\u05B3",
-    "/hatafqamatsnarrowhebrew": "\u05B3",
-    "/hatafqamatsquarterhebrew": "\u05B3",
-    "/hatafqamatswidehebrew": "\u05B3",
-    "/hatafsegol": "\u05B1",
-    "/hatafsegol17": "\u05B1",
-    "/hatafsegol24": "\u05B1",
-    "/hatafsegol30": "\u05B1",
-    "/hatafsegolhebrew": "\u05B1",
-    "/hatafsegolnarrowhebrew": "\u05B1",
-    "/hatafsegolquarterhebrew": "\u05B1",
-    "/hatafsegolwidehebrew": "\u05B1",
-    "/hatchingChick": "\u1F423",
-    "/haveideographiccircled": "\u3292",
-    "/haveideographicparen": "\u3232",
-    "/hbar": "\u0127",
-    "/hbopomofo": "\u310F",
-    "/hbrevebelow": "\u1E2B",
-    "/hcaron": "\u021F",
-    "/hcedilla": "\u1E29",
-    "/hcircle": "\u24D7",
-    "/hcircumflex": "\u0125",
-    "/hcsquare": "\u1F1A6",
-    "/hdescender": "\u2C68",
-    "/hdieresis": "\u1E27",
-    "/hdot": "\u1E23",
-    "/hdotaccent": "\u1E23",
-    "/hdotbelow": "\u1E25",
-    "/hdrsquare": "\u1F1A7",
-    "/he": "\u05D4",
-    "/he:hb": "\u05D4",
-    "/headphone": "\u1F3A7",
-    "/headstonegraveyard": "\u26FC",
-    "/hearNoEvilMonkey": "\u1F649",
-    "/heart": "\u2665",
-    "/heartArrow": "\u1F498",
-    "/heartDecoration": "\u1F49F",
-    "/heartRibbon": "\u1F49D",
-    "/heartTipOnTheLeft": "\u1F394",
-    "/heartblack": "\u2665",
-    "/heartsuitblack": "\u2665",
-    "/heartsuitwhite": "\u2661",
-    "/heartwhite": "\u2661",
-    "/heavyDollarSign": "\u1F4B2",
-    "/heavyLatinCross": "\u1F547",
-    "/heavydbldashhorz": "\u254D",
-    "/heavydbldashvert": "\u254F",
-    "/heavydn": "\u257B",
-    "/heavydnhorz": "\u2533",
-    "/heavydnleft": "\u2513",
-    "/heavydnright": "\u250F",
-    "/heavyhorz": "\u2501",
-    "/heavyleft": "\u2578",
-    "/heavyleftlightright": "\u257E",
-    "/heavyquaddashhorz": "\u2509",
-    "/heavyquaddashvert": "\u250B",
-    "/heavyright": "\u257A",
-    "/heavytrpldashhorz": "\u2505",
-    "/heavytrpldashvert": "\u2507",
-    "/heavyup": "\u2579",
-    "/heavyuphorz": "\u253B",
-    "/heavyupleft": "\u251B",
-    "/heavyuplightdn": "\u257F",
-    "/heavyupright": "\u2517",
-    "/heavyvert": "\u2503",
-    "/heavyverthorz": "\u254B",
-    "/heavyvertleft": "\u252B",
-    "/heavyvertright": "\u2523",
-    "/hecirclekatakana": "\u32EC",
-    "/hedagesh": "\uFB34",
-    "/hedageshhebrew": "\uFB34",
-    "/hedinterlacedpentagramleft": "\u26E6",
-    "/hedinterlacedpentagramright": "\u26E5",
-    "/heh": "\u0647",
-    "/heh.fina": "\uFEEA",
-    "/heh.init": "\uFEEB",
-    "/heh.init_alefmaksura.fina": "\uFC53",
-    "/heh.init_jeem.fina": "\uFC51",
-    "/heh.init_jeem.medi": "\uFCD7",
-    "/heh.init_meem.fina": "\uFC52",
-    "/heh.init_meem.medi": "\uFCD8",
-    "/heh.init_meem.medi_jeem.medi": "\uFD93",
-    "/heh.init_meem.medi_meem.medi": "\uFD94",
-    "/heh.init_superscriptalef.medi": "\uFCD9",
-    "/heh.init_yeh.fina": "\uFC54",
-    "/heh.isol": "\uFEE9",
-    "/heh.medi": "\uFEEC",
-    "/hehaltonearabic": "\u06C1",
-    "/heharabic": "\u0647",
-    "/hehdoachashmee": "\u06BE",
-    "/hehdoachashmee.fina": "\uFBAB",
-    "/hehdoachashmee.init": "\uFBAC",
-    "/hehdoachashmee.isol": "\uFBAA",
-    "/hehdoachashmee.medi": "\uFBAD",
-    "/hehebrew": "\u05D4",
-    "/hehfinalaltonearabic": "\uFBA7",
-    "/hehfinalalttwoarabic": "\uFEEA",
-    "/hehfinalarabic": "\uFEEA",
-    "/hehgoal": "\u06C1",
-    "/hehgoal.fina": "\uFBA7",
-    "/hehgoal.init": "\uFBA8",
-    "/hehgoal.isol": "\uFBA6",
-    "/hehgoal.medi": "\uFBA9",
-    "/hehgoalhamza": "\u06C2",
-    "/hehhamzaabovefinalarabic": "\uFBA5",
-    "/hehhamzaaboveisolatedarabic": "\uFBA4",
-    "/hehinitialaltonearabic": "\uFBA8",
-    "/hehinitialarabic": "\uFEEB",
-    "/hehinvertedV": "\u06FF",
-    "/hehiragana": "\u3078",
-    "/hehmedialaltonearabic": "\uFBA9",
-    "/hehmedialarabic": "\uFEEC",
-    "/hehyeh": "\u06C0",
-    "/hehyeh.fina": "\uFBA5",
-    "/hehyeh.isol": "\uFBA4",
-    "/heiseierasquare": "\u337B",
-    "/hekatakana": "\u30D8",
-    "/hekatakanahalfwidth": "\uFF8D",
-    "/hekutaarusquare": "\u3336",
-    "/helicopter": "\u1F681",
-    "/helm": "\u2388",
-    "/helmetcrosswhite": "\u26D1",
-    "/heng": "\uA727",
-    "/henghook": "\u0267",
-    "/herb": "\u1F33F",
-    "/hermitianconjugatematrix": "\u22B9",
-    "/herutusquare": "\u3339",
-    "/het": "\u05D7",
-    "/het:hb": "\u05D7",
-    "/heta": "\u0371",
-    "/hethebrew": "\u05D7",
-    "/hewide:hb": "\uFB23",
-    "/hewithmapiq:hb": "\uFB34",
-    "/hfishhookturned": "\u02AE",
-    "/hhalf": "\u2C76",
-    "/hhook": "\u0266",
-    "/hhooksuperior": "\u02B1",
-    "/hhooksupmod": "\u02B1",
-    "/hi-ressquare": "\u1F1A8",
-    "/hibiscus": "\u1F33A",
-    "/hicirclekatakana": "\u32EA",
-    "/hieuhacirclekorean": "\u327B",
-    "/hieuhaparenkorean": "\u321B",
-    "/hieuhcirclekorean": "\u326D",
-    "/hieuhkorean": "\u314E",
-    "/hieuhparenkorean": "\u320D",
-    "/high-heeledShoe": "\u1F460",
-    "/highBrightness": "\u1F506",
-    "/highSpeedTrain": "\u1F684",
-    "/highSpeedTrainWithBulletNose": "\u1F685",
-    "/highhamza": "\u0674",
-    "/highideographiccircled": "\u32A4",
-    "/highvoltage": "\u26A1",
-    "/hihiragana": "\u3072",
-    "/hikatakana": "\u30D2",
-    "/hikatakanahalfwidth": "\uFF8B",
-    "/hira:a": "\u3042",
-    "/hira:asmall": "\u3041",
-    "/hira:ba": "\u3070",
-    "/hira:be": "\u3079",
-    "/hira:bi": "\u3073",
-    "/hira:bo": "\u307C",
-    "/hira:bu": "\u3076",
-    "/hira:da": "\u3060",
-    "/hira:de": "\u3067",
-    "/hira:di": "\u3062",
-    "/hira:digraphyori": "\u309F",
-    "/hira:do": "\u3069",
-    "/hira:du": "\u3065",
-    "/hira:e": "\u3048",
-    "/hira:esmall": "\u3047",
-    "/hira:ga": "\u304C",
-    "/hira:ge": "\u3052",
-    "/hira:gi": "\u304E",
-    "/hira:go": "\u3054",
-    "/hira:gu": "\u3050",
-    "/hira:ha": "\u306F",
-    "/hira:he": "\u3078",
-    "/hira:hi": "\u3072",
-    "/hira:ho": "\u307B",
-    "/hira:hu": "\u3075",
-    "/hira:i": "\u3044",
-    "/hira:ismall": "\u3043",
-    "/hira:iterationhiragana": "\u309D",
-    "/hira:ka": "\u304B",
-    "/hira:kasmall": "\u3095",
-    "/hira:ke": "\u3051",
-    "/hira:kesmall": "\u3096",
-    "/hira:ki": "\u304D",
-    "/hira:ko": "\u3053",
-    "/hira:ku": "\u304F",
-    "/hira:ma": "\u307E",
-    "/hira:me": "\u3081",
-    "/hira:mi": "\u307F",
-    "/hira:mo": "\u3082",
-    "/hira:mu": "\u3080",
-    "/hira:n": "\u3093",
-    "/hira:na": "\u306A",
-    "/hira:ne": "\u306D",
-    "/hira:ni": "\u306B",
-    "/hira:no": "\u306E",
-    "/hira:nu": "\u306C",
-    "/hira:o": "\u304A",
-    "/hira:osmall": "\u3049",
-    "/hira:pa": "\u3071",
-    "/hira:pe": "\u307A",
-    "/hira:pi": "\u3074",
-    "/hira:po": "\u307D",
-    "/hira:pu": "\u3077",
-    "/hira:ra": "\u3089",
-    "/hira:re": "\u308C",
-    "/hira:ri": "\u308A",
-    "/hira:ro": "\u308D",
-    "/hira:ru": "\u308B",
-    "/hira:sa": "\u3055",
-    "/hira:se": "\u305B",
-    "/hira:semivoicedmarkkana": "\u309C",
-    "/hira:semivoicedmarkkanacmb": "\u309A",
-    "/hira:si": "\u3057",
-    "/hira:so": "\u305D",
-    "/hira:su": "\u3059",
-    "/hira:ta": "\u305F",
-    "/hira:te": "\u3066",
-    "/hira:ti": "\u3061",
-    "/hira:to": "\u3068",
-    "/hira:tu": "\u3064",
-    "/hira:tusmall": "\u3063",
-    "/hira:u": "\u3046",
-    "/hira:usmall": "\u3045",
-    "/hira:voicediterationhiragana": "\u309E",
-    "/hira:voicedmarkkana": "\u309B",
-    "/hira:voicedmarkkanacmb": "\u3099",
-    "/hira:vu": "\u3094",
-    "/hira:wa": "\u308F",
-    "/hira:wasmall": "\u308E",
-    "/hira:we": "\u3091",
-    "/hira:wi": "\u3090",
-    "/hira:wo": "\u3092",
-    "/hira:ya": "\u3084",
-    "/hira:yasmall": "\u3083",
-    "/hira:yo": "\u3088",
-    "/hira:yosmall": "\u3087",
-    "/hira:yu": "\u3086",
-    "/hira:yusmall": "\u3085",
-    "/hira:za": "\u3056",
-    "/hira:ze": "\u305C",
-    "/hira:zi": "\u3058",
-    "/hira:zo": "\u305E",
-    "/hira:zu": "\u305A",
-    "/hiriq": "\u05B4",
-    "/hiriq14": "\u05B4",
-    "/hiriq21": "\u05B4",
-    "/hiriq2d": "\u05B4",
-    "/hiriq:hb": "\u05B4",
-    "/hiriqhebrew": "\u05B4",
-    "/hiriqnarrowhebrew": "\u05B4",
-    "/hiriqquarterhebrew": "\u05B4",
-    "/hiriqwidehebrew": "\u05B4",
-    "/historicsite": "\u26EC",
-    "/hlinebelow": "\u1E96",
-    "/hmonospace": "\uFF48",
-    "/hoarmenian": "\u0570",
-    "/hocho": "\u1F52A",
-    "/hocirclekatakana": "\u32ED",
-    "/hohipthai": "\u0E2B",
-    "/hohiragana": "\u307B",
-    "/hokatakana": "\u30DB",
-    "/hokatakanahalfwidth": "\uFF8E",
-    "/holam": "\u05B9",
-    "/holam19": "\u05B9",
-    "/holam26": "\u05B9",
-    "/holam32": "\u05B9",
-    "/holam:hb": "\u05B9",
-    "/holamHaser:hb": "\u05BA",
-    "/holamhebrew": "\u05B9",
-    "/holamnarrowhebrew": "\u05B9",
-    "/holamquarterhebrew": "\u05B9",
-    "/holamwidehebrew": "\u05B9",
-    "/hole": "\u1F573",
-    "/homotic": "\u223B",
-    "/honeyPot": "\u1F36F",
-    "/honeybee": "\u1F41D",
-    "/honokhukthai": "\u0E2E",
-    "/honsquare": "\u333F",
-    "/hook": "\u2440",
-    "/hookabovecomb": "\u0309",
-    "/hookcmb": "\u0309",
-    "/hookpalatalizedbelowcmb": "\u0321",
-    "/hookretroflexbelowcmb": "\u0322",
-    "/hoonsquare": "\u3342",
-    "/hoorusquare": "\u3341",
-    "/horicoptic": "\u03E9",
-    "/horizontalTrafficLight": "\u1F6A5",
-    "/horizontalbar": "\u2015",
-    "/horizontalbarwhitearrowonpedestalup": "\u21EC",
-    "/horizontalmalestroke": "\u26A9",
-    "/horncmb": "\u031B",
-    "/horse": "\u1F40E",
-    "/horseFace": "\u1F434",
-    "/horseRacing": "\u1F3C7",
-    "/hospital": "\u1F3E5",
-    "/hotDog": "\u1F32D",
-    "/hotPepper": "\u1F336",
-    "/hotbeverage": "\u2615",
-    "/hotel": "\u1F3E8",
-    "/hotsprings": "\u2668",
-    "/hourglass": "\u231B",
-    "/hourglassflowings": "\u23F3",
-    "/house": "\u2302",
-    "/houseBuilding": "\u1F3E0",
-    "/houseBuildings": "\u1F3D8",
-    "/houseGarden": "\u1F3E1",
-    "/hpafullwidth": "\u3371",
-    "/hpalatalhook": "\uA795",
-    "/hparen": "\u24A3",
-    "/hparenthesized": "\u24A3",
-    "/hpfullwidth": "\u33CB",
-    "/hryvnia": "\u20B4",
-    "/hsuperior": "\u02B0",
-    "/hsupmod": "\u02B0",
-    "/hturned": "\u0265",
-    "/htypeopencircuit": "\u238F",
-    "/huaraddosquare": "\u3332",
-    "/hucirclekatakana": "\u32EB",
-    "/huhiragana": "\u3075",
-    "/huiitosquare": "\u3333",
-    "/hukatakana": "\u30D5",
-    "/hukatakanahalfwidth": "\uFF8C",
-    "/hundredPoints": "\u1F4AF",
-    "/hundredthousandscmbcyr": "\u0488",
-    "/hungarumlaut": "\u02DD",
-    "/hungarumlautcmb": "\u030B",
-    "/huransquare": "\u3335",
-    "/hushedFace": "\u1F62F",
-    "/hv": "\u0195",
-    "/hwd:a": "\uFFC2",
-    "/hwd:ae": "\uFFC3",
-    "/hwd:blacksquare": "\uFFED",
-    "/hwd:chieuch": "\uFFBA",
-    "/hwd:cieuc": "\uFFB8",
-    "/hwd:downwardsarrow": "\uFFEC",
-    "/hwd:e": "\uFFC7",
-    "/hwd:eo": "\uFFC6",
-    "/hwd:eu": "\uFFDA",
-    "/hwd:formslightvertical": "\uFFE8",
-    "/hwd:hangulfiller": "\uFFA0",
-    "/hwd:hieuh": "\uFFBE",
-    "/hwd:i": "\uFFDC",
-    "/hwd:ideographiccomma": "\uFF64",
-    "/hwd:ideographicfullstop": "\uFF61",
-    "/hwd:ieung": "\uFFB7",
-    "/hwd:kata:a": "\uFF71",
-    "/hwd:kata:asmall": "\uFF67",
-    "/hwd:kata:e": "\uFF74",
-    "/hwd:kata:esmall": "\uFF6A",
-    "/hwd:kata:ha": "\uFF8A",
-    "/hwd:kata:he": "\uFF8D",
-    "/hwd:kata:hi": "\uFF8B",
-    "/hwd:kata:ho": "\uFF8E",
-    "/hwd:kata:hu": "\uFF8C",
-    "/hwd:kata:i": "\uFF72",
-    "/hwd:kata:ismall": "\uFF68",
-    "/hwd:kata:ka": "\uFF76",
-    "/hwd:kata:ke": "\uFF79",
-    "/hwd:kata:ki": "\uFF77",
-    "/hwd:kata:ko": "\uFF7A",
-    "/hwd:kata:ku": "\uFF78",
-    "/hwd:kata:ma": "\uFF8F",
-    "/hwd:kata:me": "\uFF92",
-    "/hwd:kata:mi": "\uFF90",
-    "/hwd:kata:middledot": "\uFF65",
-    "/hwd:kata:mo": "\uFF93",
-    "/hwd:kata:mu": "\uFF91",
-    "/hwd:kata:n": "\uFF9D",
-    "/hwd:kata:na": "\uFF85",
-    "/hwd:kata:ne": "\uFF88",
-    "/hwd:kata:ni": "\uFF86",
-    "/hwd:kata:no": "\uFF89",
-    "/hwd:kata:nu": "\uFF87",
-    "/hwd:kata:o": "\uFF75",
-    "/hwd:kata:osmall": "\uFF6B",
-    "/hwd:kata:prolongedkana": "\uFF70",
-    "/hwd:kata:ra": "\uFF97",
-    "/hwd:kata:re": "\uFF9A",
-    "/hwd:kata:ri": "\uFF98",
-    "/hwd:kata:ro": "\uFF9B",
-    "/hwd:kata:ru": "\uFF99",
-    "/hwd:kata:sa": "\uFF7B",
-    "/hwd:kata:se": "\uFF7E",
-    "/hwd:kata:semi-voiced": "\uFF9F",
-    "/hwd:kata:si": "\uFF7C",
-    "/hwd:kata:so": "\uFF7F",
-    "/hwd:kata:su": "\uFF7D",
-    "/hwd:kata:ta": "\uFF80",
-    "/hwd:kata:te": "\uFF83",
-    "/hwd:kata:ti": "\uFF81",
-    "/hwd:kata:to": "\uFF84",
-    "/hwd:kata:tu": "\uFF82",
-    "/hwd:kata:tusmall": "\uFF6F",
-    "/hwd:kata:u": "\uFF73",
-    "/hwd:kata:usmall": "\uFF69",
-    "/hwd:kata:voiced": "\uFF9E",
-    "/hwd:kata:wa": "\uFF9C",
-    "/hwd:kata:wo": "\uFF66",
-    "/hwd:kata:ya": "\uFF94",
-    "/hwd:kata:yasmall": "\uFF6C",
-    "/hwd:kata:yo": "\uFF96",
-    "/hwd:kata:yosmall": "\uFF6E",
-    "/hwd:kata:yu": "\uFF95",
-    "/hwd:kata:yusmall": "\uFF6D",
-    "/hwd:khieukh": "\uFFBB",
-    "/hwd:kiyeok": "\uFFA1",
-    "/hwd:kiyeoksios": "\uFFA3",
-    "/hwd:leftcornerbracket": "\uFF62",
-    "/hwd:leftwardsarrow": "\uFFE9",
-    "/hwd:mieum": "\uFFB1",
-    "/hwd:nieun": "\uFFA4",
-    "/hwd:nieuncieuc": "\uFFA5",
-    "/hwd:nieunhieuh": "\uFFA6",
-    "/hwd:o": "\uFFCC",
-    "/hwd:oe": "\uFFCF",
-    "/hwd:phieuph": "\uFFBD",
-    "/hwd:pieup": "\uFFB2",
-    "/hwd:pieupsios": "\uFFB4",
-    "/hwd:rieul": "\uFFA9",
-    "/hwd:rieulhieuh": "\uFFB0",
-    "/hwd:rieulkiyeok": "\uFFAA",
-    "/hwd:rieulmieum": "\uFFAB",
-    "/hwd:rieulphieuph": "\uFFAF",
-    "/hwd:rieulpieup": "\uFFAC",
-    "/hwd:rieulsios": "\uFFAD",
-    "/hwd:rieulthieuth": "\uFFAE",
-    "/hwd:rightcornerbracket": "\uFF63",
-    "/hwd:rightwardsarrow": "\uFFEB",
-    "/hwd:sios": "\uFFB5",
-    "/hwd:ssangcieuc": "\uFFB9",
-    "/hwd:ssangkiyeok": "\uFFA2",
-    "/hwd:ssangpieup": "\uFFB3",
-    "/hwd:ssangsios": "\uFFB6",
-    "/hwd:ssangtikeut": "\uFFA8",
-    "/hwd:thieuth": "\uFFBC",
-    "/hwd:tikeut": "\uFFA7",
-    "/hwd:u": "\uFFD3",
-    "/hwd:upwardsarrow": "\uFFEA",
-    "/hwd:wa": "\uFFCD",
-    "/hwd:wae": "\uFFCE",
-    "/hwd:we": "\uFFD5",
-    "/hwd:weo": "\uFFD4",
-    "/hwd:whitecircle": "\uFFEE",
-    "/hwd:wi": "\uFFD6",
-    "/hwd:ya": "\uFFC4",
-    "/hwd:yae": "\uFFC5",
-    "/hwd:ye": "\uFFCB",
-    "/hwd:yeo": "\uFFCA",
-    "/hwd:yi": "\uFFDB",
-    "/hwd:yo": "\uFFD2",
-    "/hwd:yu": "\uFFD7",
-    "/hyphen": "\u002D",
-    "/hyphenationpoint": "\u2027",
-    "/hyphenbullet": "\u2043",
-    "/hyphendbl": "\u2E40",
-    "/hyphendbloblique": "\u2E17",
-    "/hyphendieresis": "\u2E1A",
-    "/hypheninferior": "\uF6E5",
-    "/hyphenminus": "\u002D",
-    "/hyphenmonospace": "\uFF0D",
-    "/hyphensmall": "\uFE63",
-    "/hyphensoft": "\u00AD",
-    "/hyphensuperior": "\uF6E6",
-    "/hyphentwo": "\u2010",
-    "/hypodiastole": "\u2E12",
-    "/hysteresis": "\u238E",
-    "/hzfullwidth": "\u3390",
-    "/i": "\u0069",
-    "/i.superior": "\u2071",
-    "/iacute": "\u00ED",
-    "/iacyrillic": "\u044F",
-    "/iaepigraphic": "\uA7FE",
-    "/ibengali": "\u0987",
-    "/ibopomofo": "\u3127",
-    "/ibreve": "\u012D",
-    "/icaron": "\u01D0",
-    "/iceCream": "\u1F368",
-    "/iceHockeyStickAndPuck": "\u1F3D2",
-    "/iceskate": "\u26F8",
-    "/icircle": "\u24D8",
-    "/icirclekatakana": "\u32D1",
-    "/icircumflex": "\u00EE",
-    "/icyr": "\u0438",
-    "/icyrillic": "\u0456",
-    "/idblgrave": "\u0209",
-    "/idblstruckitalic": "\u2148",
-    "/ideographearthcircle": "\u328F",
-    "/ideographfirecircle": "\u328B",
-    "/ideographicallianceparen": "\u323F",
-    "/ideographiccallparen": "\u323A",
-    "/ideographiccentrecircle": "\u32A5",
-    "/ideographicclose": "\u3006",
-    "/ideographiccomma": "\u3001",
-    "/ideographiccommaleft": "\uFF64",
-    "/ideographiccongratulationparen": "\u3237",
-    "/ideographiccorrectcircle": "\u32A3",
-    "/ideographicdepartingtonemark": "\u302C",
-    "/ideographicearthparen": "\u322F",
-    "/ideographicenteringtonemark": "\u302D",
-    "/ideographicenterpriseparen": "\u323D",
-    "/ideographicexcellentcircle": "\u329D",
-    "/ideographicfestivalparen": "\u3240",
-    "/ideographicfinancialcircle": "\u3296",
-    "/ideographicfinancialparen": "\u3236",
-    "/ideographicfireparen": "\u322B",
-    "/ideographichalffillspace": "\u303F",
-    "/ideographichaveparen": "\u3232",
-    "/ideographichighcircle": "\u32A4",
-    "/ideographiciterationmark": "\u3005",
-    "/ideographiclaborcircle": "\u3298",
-    "/ideographiclaborparen": "\u3238",
-    "/ideographicleftcircle": "\u32A7",
-    "/ideographicleveltonemark": "\u302A",
-    "/ideographiclowcircle": "\u32A6",
-    "/ideographicmedicinecircle": "\u32A9",
-    "/ideographicmetalparen": "\u322E",
-    "/ideographicmoonparen": "\u322A",
-    "/ideographicnameparen": "\u3234",
-    "/ideographicperiod": "\u3002",
-    "/ideographicprintcircle": "\u329E",
-    "/ideographicreachparen": "\u3243",
-    "/ideographicrepresentparen": "\u3239",
-    "/ideographicresourceparen": "\u323E",
-    "/ideographicrightcircle": "\u32A8",
-    "/ideographicrisingtonemark": "\u302B",
-    "/ideographicsecretcircle": "\u3299",
-    "/ideographicselfparen": "\u3242",
-    "/ideographicsocietyparen": "\u3233",
-    "/ideographicspace": "\u3000",
-    "/ideographicspecialparen": "\u3235",
-    "/ideographicstockparen": "\u3231",
-    "/ideographicstudyparen": "\u323B",
-    "/ideographicsunparen": "\u3230",
-    "/ideographicsuperviseparen": "\u323C",
-    "/ideographictelegraphlinefeedseparatorsymbol": "\u3037",
-    "/ideographictelegraphsymbolforhoureight": "\u3360",
-    "/ideographictelegraphsymbolforhoureighteen": "\u336A",
-    "/ideographictelegraphsymbolforhoureleven": "\u3363",
-    "/ideographictelegraphsymbolforhourfifteen": "\u3367",
-    "/ideographictelegraphsymbolforhourfive": "\u335D",
-    "/ideographictelegraphsymbolforhourfour": "\u335C",
-    "/ideographictelegraphsymbolforhourfourteen": "\u3366",
-    "/ideographictelegraphsymbolforhournine": "\u3361",
-    "/ideographictelegraphsymbolforhournineteen": "\u336B",
-    "/ideographictelegraphsymbolforhourone": "\u3359",
-    "/ideographictelegraphsymbolforhourseven": "\u335F",
-    "/ideographictelegraphsymbolforhourseventeen": "\u3369",
-    "/ideographictelegraphsymbolforhoursix": "\u335E",
-    "/ideographictelegraphsymbolforhoursixteen": "\u3368",
-    "/ideographictelegraphsymbolforhourten": "\u3362",
-    "/ideographictelegraphsymbolforhourthirteen": "\u3365",
-    "/ideographictelegraphsymbolforhourthree": "\u335B",
-    "/ideographictelegraphsymbolforhourtwelve": "\u3364",
-    "/ideographictelegraphsymbolforhourtwenty": "\u336C",
-    "/ideographictelegraphsymbolforhourtwentyfour": "\u3370",
-    "/ideographictelegraphsymbolforhourtwentyone": "\u336D",
-    "/ideographictelegraphsymbolforhourtwentythree": "\u336F",
-    "/ideographictelegraphsymbolforhourtwentytwo": "\u336E",
-    "/ideographictelegraphsymbolforhourtwo": "\u335A",
-    "/ideographictelegraphsymbolforhourzero": "\u3358",
-    "/ideographicvariationindicator": "\u303E",
-    "/ideographicwaterparen": "\u322C",
-    "/ideographicwoodparen": "\u322D",
-    "/ideographiczero": "\u3007",
-    "/ideographmetalcircle": "\u328E",
-    "/ideographmooncircle": "\u328A",
-    "/ideographnamecircle": "\u3294",
-    "/ideographsuncircle": "\u3290",
-    "/ideographwatercircle": "\u328C",
-    "/ideographwoodcircle": "\u328D",
-    "/ideva": "\u0907",
-    "/idieresis": "\u00EF",
-    "/idieresisacute": "\u1E2F",
-    "/idieresiscyr": "\u04E5",
-    "/idieresiscyrillic": "\u04E5",
-    "/idotbelow": "\u1ECB",
-    "/idsquare": "\u1F194",
-    "/iebrevecyr": "\u04D7",
-    "/iebrevecyrillic": "\u04D7",
-    "/iecyr": "\u0435",
-    "/iecyrillic": "\u0435",
-    "/iegravecyr": "\u0450",
-    "/iepigraphicsideways": "\uA7F7",
-    "/ieungacirclekorean": "\u3275",
-    "/ieungaparenkorean": "\u3215",
-    "/ieungcirclekorean": "\u3267",
-    "/ieungkorean": "\u3147",
-    "/ieungparenkorean": "\u3207",
-    "/ieungucirclekorean": "\u327E",
-    "/igrave": "\u00EC",
-    "/igravecyr": "\u045D",
-    "/igravedbl": "\u0209",
-    "/igujarati": "\u0A87",
-    "/igurmukhi": "\u0A07",
-    "/ihiragana": "\u3044",
-    "/ihoi": "\u1EC9",
-    "/ihookabove": "\u1EC9",
-    "/iibengali": "\u0988",
-    "/iicyrillic": "\u0438",
-    "/iideva": "\u0908",
-    "/iigujarati": "\u0A88",
-    "/iigurmukhi": "\u0A08",
-    "/iimatragurmukhi": "\u0A40",
-    "/iinvertedbreve": "\u020B",
-    "/iishortcyrillic": "\u0439",
-    "/iivowelsignbengali": "\u09C0",
-    "/iivowelsigndeva": "\u0940",
-    "/iivowelsigngujarati": "\u0AC0",
-    "/ij": "\u0133",
-    "/ikatakana": "\u30A4",
-    "/ikatakanahalfwidth": "\uFF72",
-    "/ikawi": "\uA985",
-    "/ikorean": "\u3163",
-    "/ilde": "\u02DC",
-    "/iluy:hb": "\u05AC",
-    "/iluyhebrew": "\u05AC",
-    "/imacron": "\u012B",
-    "/imacroncyr": "\u04E3",
-    "/imacroncyrillic": "\u04E3",
-    "/image": "\u22B7",
-    "/imageorapproximatelyequal": "\u2253",
-    "/imatragurmukhi": "\u0A3F",
-    "/imonospace": "\uFF49",
-    "/imp": "\u1F47F",
-    "/inboxTray": "\u1F4E5",
-    "/incomingEnvelope": "\u1F4E8",
-    "/increaseFontSize": "\u1F5DA",
-    "/increment": "\u2206",
-    "/indianrupee": "\u20B9",
-    "/infinity": "\u221E",
-    "/information": "\u2139",
-    "/infullwidth": "\u33CC",
-    "/inhibitarabicformshaping": "\u206C",
-    "/inhibitsymmetricswapping": "\u206A",
-    "/iniarmenian": "\u056B",
-    "/iningusquare": "\u3304",
-    "/inmationDeskPerson": "\u1F481",
-    "/inputLatinCapitalLetters": "\u1F520",
-    "/inputLatinLetters": "\u1F524",
-    "/inputLatinSmallLetters": "\u1F521",
-    "/inputNumbers": "\u1F522",
-    "/inputS": "\u1F523",
-    "/insertion": "\u2380",
-    "/integral": "\u222B",
-    "/integralbottom": "\u2321",
-    "/integralbt": "\u2321",
-    "/integralclockwise": "\u2231",
-    "/integralcontour": "\u222E",
-    "/integralcontouranticlockwise": "\u2233",
-    "/integralcontourclockwise": "\u2232",
-    "/integraldbl": "\u222C",
-    "/integralex": "\uF8F5",
-    "/integralextension": "\u23AE",
-    "/integralsurface": "\u222F",
-    "/integraltop": "\u2320",
-    "/integraltp": "\u2320",
-    "/integraltpl": "\u222D",
-    "/integralvolume": "\u2230",
-    "/intercalate": "\u22BA",
-    "/interlinearanchor": "\uFFF9",
-    "/interlinearseparator": "\uFFFA",
-    "/interlinearterminator": "\uFFFB",
-    "/interlockedfemalemale": "\u26A4",
-    "/interrobang": "\u203D",
-    "/interrobanginverted": "\u2E18",
-    "/intersection": "\u2229",
-    "/intersectionarray": "\u22C2",
-    "/intersectiondbl": "\u22D2",
-    "/intisquare": "\u3305",
-    "/invbullet": "\u25D8",
-    "/invcircle": "\u25D9",
-    "/inverteddamma": "\u0657",
-    "/invertedfork": "\u2443",
-    "/invertedpentagram": "\u26E7",
-    "/invertedundertie": "\u2054",
-    "/invisibleplus": "\u2064",
-    "/invisibleseparator": "\u2063",
-    "/invisibletimes": "\u2062",
-    "/invsmileface": "\u263B",
-    "/iocyr": "\u0451",
-    "/iocyrillic": "\u0451",
-    "/iogonek": "\u012F",
-    "/iota": "\u03B9",
-    "/iotaacute": "\u1F77",
-    "/iotaadscript": "\u1FBE",
-    "/iotaasper": "\u1F31",
-    "/iotaasperacute": "\u1F35",
-    "/iotaaspergrave": "\u1F33",
-    "/iotaaspertilde": "\u1F37",
-    "/iotabreve": "\u1FD0",
-    "/iotadieresis": "\u03CA",
-    "/iotadieresisacute": "\u1FD3",
-    "/iotadieresisgrave": "\u1FD2",
-    "/iotadieresistilde": "\u1FD7",
-    "/iotadieresistonos": "\u0390",
-    "/iotafunc": "\u2373",
-    "/iotagrave": "\u1F76",
-    "/iotalatin": "\u0269",
-    "/iotalenis": "\u1F30",
-    "/iotalenisacute": "\u1F34",
-    "/iotalenisgrave": "\u1F32",
-    "/iotalenistilde": "\u1F36",
-    "/iotasub": "\u037A",
-    "/iotatilde": "\u1FD6",
-    "/iotatonos": "\u03AF",
-    "/iotaturned": "\u2129",
-    "/iotaunderlinefunc": "\u2378",
-    "/iotawithmacron": "\u1FD1",
-    "/ipa:Ismall": "\u026A",
-    "/ipa:alpha": "\u0251",
-    "/ipa:ereversed": "\u0258",
-    "/ipa:esh": "\u0283",
-    "/ipa:gamma": "\u0263",
-    "/ipa:glottalstop": "\u0294",
-    "/ipa:gscript": "\u0261",
-    "/ipa:iota": "\u0269",
-    "/ipa:phi": "\u0278",
-    "/ipa:rtail": "\u027D",
-    "/ipa:schwa": "\u0259",
-    "/ipa:upsilon": "\u028A",
-    "/iparen": "\u24A4",
-    "/iparenthesized": "\u24A4",
-    "/irigurmukhi": "\u0A72",
-    "/is": "\uA76D",
-    "/isen-isenpada": "\uA9DF",
-    "/ishortcyr": "\u0439",
-    "/ishortsharptailcyr": "\u048B",
-    "/ismallhiragana": "\u3043",
-    "/ismallkatakana": "\u30A3",
-    "/ismallkatakanahalfwidth": "\uFF68",
-    "/issharbengali": "\u09FA",
-    "/istroke": "\u0268",
-    "/isuperior": "\uF6ED",
-    "/itemideographiccircled": "\u32A0",
-    "/iterationhiragana": "\u309D",
-    "/iterationkatakana": "\u30FD",
-    "/itilde": "\u0129",
-    "/itildebelow": "\u1E2D",
-    "/iubopomofo": "\u3129",
-    "/iucyrillic": "\u044E",
-    "/iufullwidth": "\u337A",
-    "/iukrcyr": "\u0456",
-    "/ivowelsignbengali": "\u09BF",
-    "/ivowelsigndeva": "\u093F",
-    "/ivowelsigngujarati": "\u0ABF",
-    "/izakayaLantern": "\u1F3EE",
-    "/izhitsacyr": "\u0475",
-    "/izhitsacyrillic": "\u0475",
-    "/izhitsadblgravecyrillic": "\u0477",
-    "/izhitsagravedblcyr": "\u0477",
-    "/j": "\u006A",
-    "/j.inferior": "\u2C7C",
-    "/jaarmenian": "\u0571",
-    "/jabengali": "\u099C",
-    "/jackOLantern": "\u1F383",
-    "/jadeva": "\u091C",
-    "/jagujarati": "\u0A9C",
-    "/jagurmukhi": "\u0A1C",
-    "/jamahaprana": "\uA999",
-    "/januarytelegraph": "\u32C0",
-    "/japaneseBeginner": "\u1F530",
-    "/japaneseCastle": "\u1F3EF",
-    "/japaneseDolls": "\u1F38E",
-    "/japaneseGoblin": "\u1F47A",
-    "/japaneseOgre": "\u1F479",
-    "/japanesePostOffice": "\u1F3E3",
-    "/japanesebank": "\u26FB",
-    "/java:a": "\uA984",
-    "/java:ai": "\uA98D",
-    "/java:ba": "\uA9A7",
-    "/java:ca": "\uA995",
-    "/java:da": "\uA9A2",
-    "/java:dda": "\uA99D",
-    "/java:e": "\uA98C",
-    "/java:eight": "\uA9D8",
-    "/java:five": "\uA9D5",
-    "/java:four": "\uA9D4",
-    "/java:ga": "\uA992",
-    "/java:ha": "\uA9B2",
-    "/java:i": "\uA986",
-    "/java:ii": "\uA987",
-    "/java:ja": "\uA997",
-    "/java:ka": "\uA98F",
-    "/java:la": "\uA9AD",
-    "/java:ma": "\uA9A9",
-    "/java:na": "\uA9A4",
-    "/java:nga": "\uA994",
-    "/java:nine": "\uA9D9",
-    "/java:nya": "\uA99A",
-    "/java:o": "\uA98E",
-    "/java:one": "\uA9D1",
-    "/java:pa": "\uA9A5",
-    "/java:ra": "\uA9AB",
-    "/java:sa": "\uA9B1",
-    "/java:seven": "\uA9D7",
-    "/java:six": "\uA9D6",
-    "/java:ta": "\uA9A0",
-    "/java:three": "\uA9D3",
-    "/java:tta": "\uA99B",
-    "/java:two": "\uA9D2",
-    "/java:u": "\uA988",
-    "/java:wa": "\uA9AE",
-    "/java:ya": "\uA9AA",
-    "/java:zero": "\uA9D0",
-    "/jbopomofo": "\u3110",
-    "/jcaron": "\u01F0",
-    "/jcircle": "\u24D9",
-    "/jcircumflex": "\u0135",
-    "/jcrossedtail": "\u029D",
-    "/jdblstruckitalic": "\u2149",
-    "/jdotlessstroke": "\u025F",
-    "/jeans": "\u1F456",
-    "/jecyr": "\u0458",
-    "/jecyrillic": "\u0458",
-    "/jeem": "\u062C",
-    "/jeem.fina": "\uFE9E",
-    "/jeem.init": "\uFE9F",
-    "/jeem.init_alefmaksura.fina": "\uFD01",
-    "/jeem.init_hah.fina": "\uFC15",
-    "/jeem.init_hah.medi": "\uFCA7",
-    "/jeem.init_meem.fina": "\uFC16",
-    "/jeem.init_meem.medi": "\uFCA8",
-    "/jeem.init_meem.medi_hah.medi": "\uFD59",
-    "/jeem.init_yeh.fina": "\uFD02",
-    "/jeem.isol": "\uFE9D",
-    "/jeem.medi": "\uFEA0",
-    "/jeem.medi_alefmaksura.fina": "\uFD1D",
-    "/jeem.medi_hah.medi_alefmaksura.fina": "\uFDA6",
-    "/jeem.medi_hah.medi_yeh.fina": "\uFDBE",
-    "/jeem.medi_meem.medi_alefmaksura.fina": "\uFDA7",
-    "/jeem.medi_meem.medi_hah.fina": "\uFD58",
-    "/jeem.medi_meem.medi_yeh.fina": "\uFDA5",
-    "/jeem.medi_yeh.fina": "\uFD1E",
-    "/jeemabove": "\u06DA",
-    "/jeemarabic": "\u062C",
-    "/jeemfinalarabic": "\uFE9E",
-    "/jeeminitialarabic": "\uFE9F",
-    "/jeemmedialarabic": "\uFEA0",
-    "/jeh": "\u0698",
-    "/jeh.fina": "\uFB8B",
-    "/jeh.isol": "\uFB8A",
-    "/jeharabic": "\u0698",
-    "/jehfinalarabic": "\uFB8B",
-    "/jhabengali": "\u099D",
-    "/jhadeva": "\u091D",
-    "/jhagujarati": "\u0A9D",
-    "/jhagurmukhi": "\u0A1D",
-    "/jheharmenian": "\u057B",
-    "/jis": "\u3004",
-    "/jiterup": "\u2643",
-    "/jmonospace": "\uFF4A",
-    "/jotdiaeresisfunc": "\u2364",
-    "/jotunderlinefunc": "\u235B",
-    "/joystick": "\u1F579",
-    "/jparen": "\u24A5",
-    "/jparenthesized": "\u24A5",
-    "/jstroke": "\u0249",
-    "/jsuperior": "\u02B2",
-    "/jsupmod": "\u02B2",
-    "/jueuicircle": "\u327D",
-    "/julytelegraph": "\u32C6",
-    "/junetelegraph": "\u32C5",
-    "/juno": "\u26B5",
-    "/k": "\u006B",
-    "/k.inferior": "\u2096",
-    "/kaaba": "\u1F54B",
-    "/kaaleutcyr": "\u051F",
-    "/kabashkcyr": "\u04A1",
-    "/kabashkircyrillic": "\u04A1",
-    "/kabengali": "\u0995",
-    "/kacirclekatakana": "\u32D5",
-    "/kacute": "\u1E31",
-    "/kacyr": "\u043A",
-    "/kacyrillic": "\u043A",
-    "/kadescendercyrillic": "\u049B",
-    "/kadeva": "\u0915",
-    "/kaf": "\u05DB",
-    "/kaf.fina": "\uFEDA",
-    "/kaf.init": "\uFEDB",
-    "/kaf.init_alef.fina": "\uFC37",
-    "/kaf.init_alefmaksura.fina": "\uFC3D",
-    "/kaf.init_hah.fina": "\uFC39",
-    "/kaf.init_hah.medi": "\uFCC5",
-    "/kaf.init_jeem.fina": "\uFC38",
-    "/kaf.init_jeem.medi": "\uFCC4",
-    "/kaf.init_khah.fina": "\uFC3A",
-    "/kaf.init_khah.medi": "\uFCC6",
-    "/kaf.init_lam.fina": "\uFC3B",
-    "/kaf.init_lam.medi": "\uFCC7",
-    "/kaf.init_meem.fina": "\uFC3C",
-    "/kaf.init_meem.medi": "\uFCC8",
-    "/kaf.init_meem.medi_meem.medi": "\uFDC3",
-    "/kaf.init_yeh.fina": "\uFC3E",
-    "/kaf.isol": "\uFED9",
-    "/kaf.medi": "\uFEDC",
-    "/kaf.medi_alef.fina": "\uFC80",
-    "/kaf.medi_alefmaksura.fina": "\uFC83",
-    "/kaf.medi_lam.fina": "\uFC81",
-    "/kaf.medi_lam.medi": "\uFCEB",
-    "/kaf.medi_meem.fina": "\uFC82",
-    "/kaf.medi_meem.medi": "\uFCEC",
-    "/kaf.medi_meem.medi_meem.fina": "\uFDBB",
-    "/kaf.medi_meem.medi_yeh.fina": "\uFDB7",
-    "/kaf.medi_yeh.fina": "\uFC84",
-    "/kaf:hb": "\u05DB",
-    "/kafTwoDotsAbove": "\u077F",
-    "/kafarabic": "\u0643",
-    "/kafdagesh": "\uFB3B",
-    "/kafdageshhebrew": "\uFB3B",
-    "/kafdotabove": "\u06AC",
-    "/kaffinalarabic": "\uFEDA",
-    "/kafhebrew": "\u05DB",
-    "/kafinitialarabic": "\uFEDB",
-    "/kafmedialarabic": "\uFEDC",
-    "/kafrafehebrew": "\uFB4D",
-    "/kafring": "\u06AB",
-    "/kafswash": "\u06AA",
-    "/kafthreedotsbelow": "\u06AE",
-    "/kafullwidth": "\u3384",
-    "/kafwide:hb": "\uFB24",
-    "/kafwithdagesh:hb": "\uFB3B",
-    "/kafwithrafe:hb": "\uFB4D",
-    "/kagujarati": "\u0A95",
-    "/kagurmukhi": "\u0A15",
-    "/kahiragana": "\u304B",
-    "/kahookcyr": "\u04C4",
-    "/kahookcyrillic": "\u04C4",
-    "/kairisquare": "\u330B",
-    "/kaisymbol": "\u03D7",
-    "/kakatakana": "\u30AB",
-    "/kakatakanahalfwidth": "\uFF76",
-    "/kamurda": "\uA991",
-    "/kappa": "\u03BA",
-    "/kappa.math": "\u03F0",
-    "/kappasymbolgreek": "\u03F0",
-    "/kapyeounmieumkorean": "\u3171",
-    "/kapyeounphieuphkorean": "\u3184",
-    "/kapyeounpieupkorean": "\u3178",
-    "/kapyeounssangpieupkorean": "\u3179",
-    "/karattosquare": "\u330C",
-    "/karoriisquare": "\u330D",
-    "/kasasak": "\uA990",
-    "/kashida": "\u0640",
-    "/kashidaFina": "\uFE73",
-    "/kashidaautoarabic": "\u0640",
-    "/kashidaautonosidebearingarabic": "\u0640",
-    "/kashmiriyeh": "\u0620",
-    "/kasmallkatakana": "\u30F5",
-    "/kasquare": "\u3384",
-    "/kasra": "\u0650",
-    "/kasraIsol": "\uFE7A",
-    "/kasraMedi": "\uFE7B",
-    "/kasraarabic": "\u0650",
-    "/kasrasmall": "\u061A",
-    "/kasratan": "\u064D",
-    "/kasratanIsol": "\uFE74",
-    "/kasratanarabic": "\u064D",
-    "/kastrokecyr": "\u049F",
-    "/kastrokecyrillic": "\u049F",
-    "/kata:a": "\u30A2",
-    "/kata:asmall": "\u30A1",
-    "/kata:ba": "\u30D0",
-    "/kata:be": "\u30D9",
-    "/kata:bi": "\u30D3",
-    "/kata:bo": "\u30DC",
-    "/kata:bu": "\u30D6",
-    "/kata:da": "\u30C0",
-    "/kata:de": "\u30C7",
-    "/kata:di": "\u30C2",
-    "/kata:digraphkoto": "\u30FF",
-    "/kata:do": "\u30C9",
-    "/kata:doublehyphenkana": "\u30A0",
-    "/kata:du": "\u30C5",
-    "/kata:e": "\u30A8",
-    "/kata:esmall": "\u30A7",
-    "/kata:ga": "\u30AC",
-    "/kata:ge": "\u30B2",
-    "/kata:gi": "\u30AE",
-    "/kata:go": "\u30B4",
-    "/kata:gu": "\u30B0",
-    "/kata:ha": "\u30CF",
-    "/kata:he": "\u30D8",
-    "/kata:hi": "\u30D2",
-    "/kata:ho": "\u30DB",
-    "/kata:hu": "\u30D5",
-    "/kata:i": "\u30A4",
-    "/kata:ismall": "\u30A3",
-    "/kata:iteration": "\u30FD",
-    "/kata:ka": "\u30AB",
-    "/kata:kasmall": "\u30F5",
-    "/kata:ke": "\u30B1",
-    "/kata:kesmall": "\u30F6",
-    "/kata:ki": "\u30AD",
-    "/kata:ko": "\u30B3",
-    "/kata:ku": "\u30AF",
-    "/kata:ma": "\u30DE",
-    "/kata:me": "\u30E1",
-    "/kata:mi": "\u30DF",
-    "/kata:middledot": "\u30FB",
-    "/kata:mo": "\u30E2",
-    "/kata:mu": "\u30E0",
-    "/kata:n": "\u30F3",
-    "/kata:na": "\u30CA",
-    "/kata:ne": "\u30CD",
-    "/kata:ni": "\u30CB",
-    "/kata:no": "\u30CE",
-    "/kata:nu": "\u30CC",
-    "/kata:o": "\u30AA",
-    "/kata:osmall": "\u30A9",
-    "/kata:pa": "\u30D1",
-    "/kata:pe": "\u30DA",
-    "/kata:pi": "\u30D4",
-    "/kata:po": "\u30DD",
-    "/kata:prolongedkana": "\u30FC",
-    "/kata:pu": "\u30D7",
-    "/kata:ra": "\u30E9",
-    "/kata:re": "\u30EC",
-    "/kata:ri": "\u30EA",
-    "/kata:ro": "\u30ED",
-    "/kata:ru": "\u30EB",
-    "/kata:sa": "\u30B5",
-    "/kata:se": "\u30BB",
-    "/kata:si": "\u30B7",
-    "/kata:so": "\u30BD",
-    "/kata:su": "\u30B9",
-    "/kata:ta": "\u30BF",
-    "/kata:te": "\u30C6",
-    "/kata:ti": "\u30C1",
-    "/kata:to": "\u30C8",
-    "/kata:tu": "\u30C4",
-    "/kata:tusmall": "\u30C3",
-    "/kata:u": "\u30A6",
-    "/kata:usmall": "\u30A5",
-    "/kata:va": "\u30F7",
-    "/kata:ve": "\u30F9",
-    "/kata:vi": "\u30F8",
-    "/kata:vo": "\u30FA",
-    "/kata:voicediteration": "\u30FE",
-    "/kata:vu": "\u30F4",
-    "/kata:wa": "\u30EF",
-    "/kata:wasmall": "\u30EE",
-    "/kata:we": "\u30F1",
-    "/kata:wi": "\u30F0",
-    "/kata:wo": "\u30F2",
-    "/kata:ya": "\u30E4",
-    "/kata:yasmall": "\u30E3",
-    "/kata:yo": "\u30E8",
-    "/kata:yosmall": "\u30E7",
-    "/kata:yu": "\u30E6",
-    "/kata:yusmall": "\u30E5",
-    "/kata:za": "\u30B6",
-    "/kata:ze": "\u30BC",
-    "/kata:zi": "\u30B8",
-    "/kata:zo": "\u30BE",
-    "/kata:zu": "\u30BA",
-    "/katahiraprolongmarkhalfwidth": "\uFF70",
-    "/katailcyr": "\u049B",
-    "/kaverticalstrokecyr": "\u049D",
-    "/kaverticalstrokecyrillic": "\u049D",
-    "/kavykainvertedlow": "\u2E45",
-    "/kavykalow": "\u2E47",
-    "/kavykawithdotlow": "\u2E48",
-    "/kavykawithkavykaaboveinvertedlow": "\u2E46",
-    "/kbfullwidth": "\u3385",
-    "/kbopomofo": "\u310E",
-    "/kcalfullwidth": "\u3389",
-    "/kcalsquare": "\u3389",
-    "/kcaron": "\u01E9",
-    "/kcedilla": "\u0137",
-    "/kcircle": "\u24DA",
-    "/kcommaaccent": "\u0137",
-    "/kdescender": "\u2C6A",
-    "/kdiagonalstroke": "\uA743",
-    "/kdotbelow": "\u1E33",
-    "/kecirclekatakana": "\u32D8",
-    "/keesusquare": "\u331C",
-    "/keharmenian": "\u0584",
-    "/keheh": "\u06A9",
-    "/keheh.fina": "\uFB8F",
-    "/keheh.init": "\uFB90",
-    "/keheh.isol": "\uFB8E",
-    "/keheh.medi": "\uFB91",
-    "/kehehDotAbove": "\u0762",
-    "/kehehThreeDotsAbove": "\u0763",
-    "/kehehThreeDotsUpBelow": "\u0764",
-    "/kehehthreedotsbelow": "\u063C",
-    "/kehehtwodotsabove": "\u063B",
-    "/kehiragana": "\u3051",
-    "/kekatakana": "\u30B1",
-    "/kekatakanahalfwidth": "\uFF79",
-    "/kelvin": "\u212A",
-    "/kenarmenian": "\u056F",
-    "/keretconsonant": "\uA9BD",
-    "/kesmallkatakana": "\u30F6",
-    "/key": "\u1F511",
-    "/keyboardAndMouse": "\u1F5A6",
-    "/keycapTen": "\u1F51F",
-    "/kgfullwidth": "\u338F",
-    "/kgreenlandic": "\u0138",
-    "/khabengali": "\u0996",
-    "/khacyrillic": "\u0445",
-    "/khadeva": "\u0916",
-    "/khagujarati": "\u0A96",
-    "/khagurmukhi": "\u0A16",
-    "/khah": "\u062E",
-    "/khah.fina": "\uFEA6",
-    "/khah.init": "\uFEA7",
-    "/khah.init_alefmaksura.fina": "\uFD03",
-    "/khah.init_hah.fina": "\uFC1A",
-    "/khah.init_jeem.fina": "\uFC19",
-    "/khah.init_jeem.medi": "\uFCAB",
-    "/khah.init_meem.fina": "\uFC1B",
-    "/khah.init_meem.medi": "\uFCAC",
-    "/khah.init_yeh.fina": "\uFD04",
-    "/khah.isol": "\uFEA5",
-    "/khah.medi": "\uFEA8",
-    "/khah.medi_alefmaksura.fina": "\uFD1F",
-    "/khah.medi_yeh.fina": "\uFD20",
-    "/khaharabic": "\u062E",
-    "/khahfinalarabic": "\uFEA6",
-    "/khahinitialarabic": "\uFEA7",
-    "/khahmedialarabic": "\uFEA8",
-    "/kheicoptic": "\u03E7",
-    "/khhadeva": "\u0959",
-    "/khhagurmukhi": "\u0A59",
-    "/khieukhacirclekorean": "\u3278",
-    "/khieukhaparenkorean": "\u3218",
-    "/khieukhcirclekorean": "\u326A",
-    "/khieukhkorean": "\u314B",
-    "/khieukhparenkorean": "\u320A",
-    "/khokhaithai": "\u0E02",
-    "/khokhonthai": "\u0E05",
-    "/khokhuatthai": "\u0E03",
-    "/khokhwaithai": "\u0E04",
-    "/khomutthai": "\u0E5B",
-    "/khook": "\u0199",
-    "/khorakhangthai": "\u0E06",
-    "/khzfullwidth": "\u3391",
-    "/khzsquare": "\u3391",
-    "/kicirclekatakana": "\u32D6",
-    "/kihiragana": "\u304D",
-    "/kikatakana": "\u30AD",
-    "/kikatakanahalfwidth": "\uFF77",
-    "/kimono": "\u1F458",
-    "/kindergartenideographiccircled": "\u3245",
-    "/kingblack": "\u265A",
-    "/kingwhite": "\u2654",
-    "/kip": "\u20AD",
-    "/kiroguramusquare": "\u3315",
-    "/kiromeetorusquare": "\u3316",
-    "/kirosquare": "\u3314",
-    "/kirowattosquare": "\u3317",
-    "/kiss": "\u1F48F",
-    "/kissMark": "\u1F48B",
-    "/kissingCatFaceWithClosedEyes": "\u1F63D",
-    "/kissingFace": "\u1F617",
-    "/kissingFaceWithClosedEyes": "\u1F61A",
-    "/kissingFaceWithSmilingEyes": "\u1F619",
-    "/kiyeokacirclekorean": "\u326E",
-    "/kiyeokaparenkorean": "\u320E",
-    "/kiyeokcirclekorean": "\u3260",
-    "/kiyeokkorean": "\u3131",
-    "/kiyeokparenkorean": "\u3200",
-    "/kiyeoksioskorean": "\u3133",
-    "/kjecyr": "\u045C",
-    "/kjecyrillic": "\u045C",
-    "/kkfullwidth": "\u33CD",
-    "/klfullwidth": "\u3398",
-    "/klinebelow": "\u1E35",
-    "/klsquare": "\u3398",
-    "/km2fullwidth": "\u33A2",
-    "/km3fullwidth": "\u33A6",
-    "/kmcapitalfullwidth": "\u33CE",
-    "/kmcubedsquare": "\u33A6",
-    "/kmfullwidth": "\u339E",
-    "/kmonospace": "\uFF4B",
-    "/kmsquaredsquare": "\u33A2",
-    "/knda:a": "\u0C85",
-    "/knda:aa": "\u0C86",
-    "/knda:aasign": "\u0CBE",
-    "/knda:ai": "\u0C90",
-    "/knda:ailength": "\u0CD6",
-    "/knda:aisign": "\u0CC8",
-    "/knda:anusvara": "\u0C82",
-    "/knda:au": "\u0C94",
-    "/knda:ausign": "\u0CCC",
-    "/knda:avagraha": "\u0CBD",
-    "/knda:ba": "\u0CAC",
-    "/knda:bha": "\u0CAD",
-    "/knda:ca": "\u0C9A",
-    "/knda:cha": "\u0C9B",
-    "/knda:da": "\u0CA6",
-    "/knda:dda": "\u0CA1",
-    "/knda:ddha": "\u0CA2",
-    "/knda:dha": "\u0CA7",
-    "/knda:e": "\u0C8E",
-    "/knda:ee": "\u0C8F",
-    "/knda:eesign": "\u0CC7",
-    "/knda:eight": "\u0CEE",
-    "/knda:esign": "\u0CC6",
-    "/knda:fa": "\u0CDE",
-    "/knda:five": "\u0CEB",
-    "/knda:four": "\u0CEA",
-    "/knda:ga": "\u0C97",
-    "/knda:gha": "\u0C98",
-    "/knda:ha": "\u0CB9",
-    "/knda:i": "\u0C87",
-    "/knda:ii": "\u0C88",
-    "/knda:iisign": "\u0CC0",
-    "/knda:isign": "\u0CBF",
-    "/knda:ja": "\u0C9C",
-    "/knda:jha": "\u0C9D",
-    "/knda:jihvamuliya": "\u0CF1",
-    "/knda:ka": "\u0C95",
-    "/knda:kha": "\u0C96",
-    "/knda:la": "\u0CB2",
-    "/knda:length": "\u0CD5",
-    "/knda:lla": "\u0CB3",
-    "/knda:llvocal": "\u0CE1",
-    "/knda:llvocalsign": "\u0CE3",
-    "/knda:lvocal": "\u0C8C",
-    "/knda:lvocalsign": "\u0CE2",
-    "/knda:ma": "\u0CAE",
-    "/knda:na": "\u0CA8",
-    "/knda:nga": "\u0C99",
-    "/knda:nine": "\u0CEF",
-    "/knda:nna": "\u0CA3",
-    "/knda:nukta": "\u0CBC",
-    "/knda:nya": "\u0C9E",
-    "/knda:o": "\u0C92",
-    "/knda:one": "\u0CE7",
-    "/knda:oo": "\u0C93",
-    "/knda:oosign": "\u0CCB",
-    "/knda:osign": "\u0CCA",
-    "/knda:pa": "\u0CAA",
-    "/knda:pha": "\u0CAB",
-    "/knda:ra": "\u0CB0",
-    "/knda:rra": "\u0CB1",
-    "/knda:rrvocal": "\u0CE0",
-    "/knda:rrvocalsign": "\u0CC4",
-    "/knda:rvocal": "\u0C8B",
-    "/knda:rvocalsign": "\u0CC3",
-    "/knda:sa": "\u0CB8",
-    "/knda:seven": "\u0CED",
-    "/knda:sha": "\u0CB6",
-    "/knda:signcandrabindu": "\u0C81",
-    "/knda:signspacingcandrabindu": "\u0C80",
-    "/knda:six": "\u0CEC",
-    "/knda:ssa": "\u0CB7",
-    "/knda:ta": "\u0CA4",
-    "/knda:tha": "\u0CA5",
-    "/knda:three": "\u0CE9",
-    "/knda:tta": "\u0C9F",
-    "/knda:ttha": "\u0CA0",
-    "/knda:two": "\u0CE8",
-    "/knda:u": "\u0C89",
-    "/knda:upadhmaniya": "\u0CF2",
-    "/knda:usign": "\u0CC1",
-    "/knda:uu": "\u0C8A",
-    "/knda:uusign": "\u0CC2",
-    "/knda:va": "\u0CB5",
-    "/knda:virama": "\u0CCD",
-    "/knda:visarga": "\u0C83",
-    "/knda:ya": "\u0CAF",
-    "/knda:zero": "\u0CE6",
-    "/knightblack": "\u265E",
-    "/knightwhite": "\u2658",
-    "/ko:a": "\u314F",
-    "/ko:ae": "\u3150",
-    "/ko:aejungseong": "\u1162",
-    "/ko:aeujungseong": "\u11A3",
-    "/ko:ajungseong": "\u1161",
-    "/ko:aojungseong": "\u1176",
-    "/ko:araea": "\u318D",
-    "/ko:araeae": "\u318E",
-    "/ko:araeaeojungseong": "\u119F",
-    "/ko:araeaijungseong": "\u11A1",
-    "/ko:araeajungseong": "\u119E",
-    "/ko:araeaujungseong": "\u11A0",
-    "/ko:aujungseong": "\u1177",
-    "/ko:ceongchieumchieuchchoseong": "\u1155",
-    "/ko:ceongchieumcieucchoseong": "\u1150",
-    "/ko:ceongchieumsioschoseong": "\u113E",
-    "/ko:ceongchieumssangcieucchoseong": "\u1151",
-    "/ko:ceongchieumssangsioschoseong": "\u113F",
-    "/ko:chieuch": "\u314A",
-    "/ko:chieuchchoseong": "\u110E",
-    "/ko:chieuchhieuhchoseong": "\u1153",
-    "/ko:chieuchjongseong": "\u11BE",
-    "/ko:chieuchkhieukhchoseong": "\u1152",
-    "/ko:chitueumchieuchchoseong": "\u1154",
-    "/ko:chitueumcieucchoseong": "\u114E",
-    "/ko:chitueumsioschoseong": "\u113C",
-    "/ko:chitueumssangcieucchoseong": "\u114F",
-    "/ko:chitueumssangsioschoseong": "\u113D",
-    "/ko:cieuc": "\u3148",
-    "/ko:cieucchoseong": "\u110C",
-    "/ko:cieucieungchoseong": "\u114D",
-    "/ko:cieucjongseong": "\u11BD",
-    "/ko:e": "\u3154",
-    "/ko:ejungseong": "\u1166",
-    "/ko:eo": "\u3153",
-    "/ko:eo_eujungseong": "\u117C",
-    "/ko:eojungseong": "\u1165",
-    "/ko:eoojungseong": "\u117A",
-    "/ko:eoujungseong": "\u117B",
-    "/ko:eu": "\u3161",
-    "/ko:eueujungseong": "\u1196",
-    "/ko:eujungseong": "\u1173",
-    "/ko:euujungseong": "\u1195",
-    "/ko:filler": "\u3164",
-    "/ko:fillerchoseong": "\u115F",
-    "/ko:fillerjungseong": "\u1160",
-    "/ko:hieuh": "\u314E",
-    "/ko:hieuhchoseong": "\u1112",
-    "/ko:hieuhjongseong": "\u11C2",
-    "/ko:hieuhmieumjongseong": "\u11F7",
-    "/ko:hieuhnieunjongseong": "\u11F5",
-    "/ko:hieuhpieupjongseong": "\u11F8",
-    "/ko:hieuhrieuljongseong": "\u11F6",
-    "/ko:i": "\u3163",
-    "/ko:iajungseong": "\u1198",
-    "/ko:iaraeajungseong": "\u119D",
-    "/ko:ieujungseong": "\u119C",
-    "/ko:ieung": "\u3147",
-    "/ko:ieungchieuchchoseong": "\u1149",
-    "/ko:ieungchoseong": "\u110B",
-    "/ko:ieungcieucchoseong": "\u1148",
-    "/ko:ieungjongseong": "\u11BC",
-    "/ko:ieungkhieukhjongseong": "\u11EF",
-    "/ko:ieungkiyeokchoseong": "\u1141",
-    "/ko:ieungkiyeokjongseong": "\u11EC",
-    "/ko:ieungmieumchoseong": "\u1143",
-    "/ko:ieungpansioschoseong": "\u1146",
-    "/ko:ieungphieuphchoseong": "\u114B",
-    "/ko:ieungpieupchoseong": "\u1144",
-    "/ko:ieungsioschoseong": "\u1145",
-    "/ko:ieungssangkiyeokjongseong": "\u11ED",
-    "/ko:ieungthieuthchoseong": "\u114A",
-    "/ko:ieungtikeutchoseong": "\u1142",
-    "/ko:ijungseong": "\u1175",
-    "/ko:iojungseong": "\u119A",
-    "/ko:iujungseong": "\u119B",
-    "/ko:iyajungseong": "\u1199",
-    "/ko:kapyeounmieum": "\u3171",
-    "/ko:kapyeounmieumchoseong": "\u111D",
-    "/ko:kapyeounmieumjongseong": "\u11E2",
-    "/ko:kapyeounphieuph": "\u3184",
-    "/ko:kapyeounphieuphchoseong": "\u1157",
-    "/ko:kapyeounphieuphjongseong": "\u11F4",
-    "/ko:kapyeounpieup": "\u3178",
-    "/ko:kapyeounpieupchoseong": "\u112B",
-    "/ko:kapyeounpieupjongseong": "\u11E6",
-    "/ko:kapyeounrieulchoseong": "\u111B",
-    "/ko:kapyeounssangpieup": "\u3179",
-    "/ko:kapyeounssangpieupchoseong": "\u112C",
-    "/ko:khieukh": "\u314B",
-    "/ko:khieukhchoseong": "\u110F",
-    "/ko:khieukhjongseong": "\u11BF",
-    "/ko:kiyeok": "\u3131",
-    "/ko:kiyeokchieuchjongseong": "\u11FC",
-    "/ko:kiyeokchoseong": "\u1100",
-    "/ko:kiyeokhieuhjongseong": "\u11FE",
-    "/ko:kiyeokjongseong": "\u11A8",
-    "/ko:kiyeokkhieukhjongseong": "\u11FD",
-    "/ko:kiyeoknieunjongseong": "\u11FA",
-    "/ko:kiyeokpieupjongseong": "\u11FB",
-    "/ko:kiyeokrieuljongseong": "\u11C3",
-    "/ko:kiyeoksios": "\u3133",
-    "/ko:kiyeoksiosjongseong": "\u11AA",
-    "/ko:kiyeoksioskiyeokjongseong": "\u11C4",
-    "/ko:kiyeoktikeutchoseong": "\u115A",
-    "/ko:mieum": "\u3141",
-    "/ko:mieumchieuchjongseong": "\u11E0",
-    "/ko:mieumchoseong": "\u1106",
-    "/ko:mieumhieuhjongseong": "\u11E1",
-    "/ko:mieumjongseong": "\u11B7",
-    "/ko:mieumkiyeokjongseong": "\u11DA",
-    "/ko:mieumpansios": "\u3170",
-    "/ko:mieumpansiosjongseong": "\u11DF",
-    "/ko:mieumpieup": "\u316E",
-    "/ko:mieumpieupchoseong": "\u111C",
-    "/ko:mieumpieupjongseong": "\u11DC",
-    "/ko:mieumrieuljongseong": "\u11DB",
-    "/ko:mieumsios": "\u316F",
-    "/ko:mieumsiosjongseong": "\u11DD",
-    "/ko:mieumssangsiosjongseong": "\u11DE",
-    "/ko:nieun": "\u3134",
-    "/ko:nieunchoseong": "\u1102",
-    "/ko:nieuncieuc": "\u3135",
-    "/ko:nieuncieucchoseong": "\u115C",
-    "/ko:nieuncieucjongseong": "\u11AC",
-    "/ko:nieunhieuh": "\u3136",
-    "/ko:nieunhieuhchoseong": "\u115D",
-    "/ko:nieunhieuhjongseong": "\u11AD",
-    "/ko:nieunjongseong": "\u11AB",
-    "/ko:nieunkiyeokchoseong": "\u1113",
-    "/ko:nieunkiyeokjongseong": "\u11C5",
-    "/ko:nieunpansios": "\u3168",
-    "/ko:nieunpansiosjongseong": "\u11C8",
-    "/ko:nieunpieupchoseong": "\u1116",
-    "/ko:nieunsios": "\u3167",
-    "/ko:nieunsioschoseong": "\u115B",
-    "/ko:nieunsiosjongseong": "\u11C7",
-    "/ko:nieunthieuthjongseong": "\u11C9",
-    "/ko:nieuntikeut": "\u3166",
-    "/ko:nieuntikeutchoseong": "\u1115",
-    "/ko:nieuntikeutjongseong": "\u11C6",
-    "/ko:o": "\u3157",
-    "/ko:o_ejungseong": "\u1180",
-    "/ko:o_eojungseong": "\u117F",
-    "/ko:oe": "\u315A",
-    "/ko:oejungseong": "\u116C",
-    "/ko:ojungseong": "\u1169",
-    "/ko:oojungseong": "\u1182",
-    "/ko:oujungseong": "\u1183",
-    "/ko:oyaejungseong": "\u11A7",
-    "/ko:oyajungseong": "\u11A6",
-    "/ko:oyejungseong": "\u1181",
-    "/ko:pansios": "\u317F",
-    "/ko:pansioschoseong": "\u1140",
-    "/ko:pansiosjongseong": "\u11EB",
-    "/ko:phieuph": "\u314D",
-    "/ko:phieuphchoseong": "\u1111",
-    "/ko:phieuphjongseong": "\u11C1",
-    "/ko:phieuphpieupchoseong": "\u1156",
-    "/ko:phieuphpieupjongseong": "\u11F3",
-    "/ko:pieup": "\u3142",
-    "/ko:pieupchieuchchoseong": "\u1128",
-    "/ko:pieupchoseong": "\u1107",
-    "/ko:pieupcieuc": "\u3176",
-    "/ko:pieupcieucchoseong": "\u1127",
-    "/ko:pieuphieuhjongseong": "\u11E5",
-    "/ko:pieupjongseong": "\u11B8",
-    "/ko:pieupkiyeok": "\u3172",
-    "/ko:pieupkiyeokchoseong": "\u111E",
-    "/ko:pieupnieunchoseong": "\u111F",
-    "/ko:pieupphieuphchoseong": "\u112A",
-    "/ko:pieupphieuphjongseong": "\u11E4",
-    "/ko:pieuprieuljongseong": "\u11E3",
-    "/ko:pieupsios": "\u3144",
-    "/ko:pieupsioschoseong": "\u1121",
-    "/ko:pieupsioscieucchoseong": "\u1126",
-    "/ko:pieupsiosjongseong": "\u11B9",
-    "/ko:pieupsioskiyeok": "\u3174",
-    "/ko:pieupsioskiyeokchoseong": "\u1122",
-    "/ko:pieupsiospieupchoseong": "\u1124",
-    "/ko:pieupsiostikeut": "\u3175",
-    "/ko:pieupsiostikeutchoseong": "\u1123",
-    "/ko:pieupssangsioschoseong": "\u1125",
-    "/ko:pieupthieuth": "\u3177",
-    "/ko:pieupthieuthchoseong": "\u1129",
-    "/ko:pieuptikeut": "\u3173",
-    "/ko:pieuptikeutchoseong": "\u1120",
-    "/ko:rieul": "\u3139",
-    "/ko:rieulchoseong": "\u1105",
-    "/ko:rieulhieuh": "\u3140",
-    "/ko:rieulhieuhchoseong": "\u111A",
-    "/ko:rieulhieuhjongseong": "\u11B6",
-    "/ko:rieuljongseong": "\u11AF",
-    "/ko:rieulkapyeounpieupjongseong": "\u11D5",
-    "/ko:rieulkhieukhjongseong": "\u11D8",
-    "/ko:rieulkiyeok": "\u313A",
-    "/ko:rieulkiyeokjongseong": "\u11B0",
-    "/ko:rieulkiyeoksios": "\u3169",
-    "/ko:rieulkiyeoksiosjongseong": "\u11CC",
-    "/ko:rieulmieum": "\u313B",
-    "/ko:rieulmieumjongseong": "\u11B1",
-    "/ko:rieulmieumkiyeokjongseong": "\u11D1",
-    "/ko:rieulmieumsiosjongseong": "\u11D2",
-    "/ko:rieulnieunchoseong": "\u1118",
-    "/ko:rieulnieunjongseong": "\u11CD",
-    "/ko:rieulpansios": "\u316C",
-    "/ko:rieulpansiosjongseong": "\u11D7",
-    "/ko:rieulphieuph": "\u313F",
-    "/ko:rieulphieuphjongseong": "\u11B5",
-    "/ko:rieulpieup": "\u313C",
-    "/ko:rieulpieuphieuhjongseong": "\u11D4",
-    "/ko:rieulpieupjongseong": "\u11B2",
-    "/ko:rieulpieupsios": "\u316B",
-    "/ko:rieulpieupsiosjongseong": "\u11D3",
-    "/ko:rieulsios": "\u313D",
-    "/ko:rieulsiosjongseong": "\u11B3",
-    "/ko:rieulssangsiosjongseong": "\u11D6",
-    "/ko:rieulthieuth": "\u313E",
-    "/ko:rieulthieuthjongseong": "\u11B4",
-    "/ko:rieultikeut": "\u316A",
-    "/ko:rieultikeuthieuhjongseong": "\u11CF",
-    "/ko:rieultikeutjongseong": "\u11CE",
-    "/ko:rieulyeorinhieuh": "\u316D",
-    "/ko:rieulyeorinhieuhjongseong": "\u11D9",
-    "/ko:sios": "\u3145",
-    "/ko:sioschieuchchoseong": "\u1137",
-    "/ko:sioschoseong": "\u1109",
-    "/ko:sioscieuc": "\u317E",
-    "/ko:sioscieucchoseong": "\u1136",
-    "/ko:sioshieuhchoseong": "\u113B",
-    "/ko:siosieungchoseong": "\u1135",
-    "/ko:siosjongseong": "\u11BA",
-    "/ko:sioskhieukhchoseong": "\u1138",
-    "/ko:sioskiyeok": "\u317A",
-    "/ko:sioskiyeokchoseong": "\u112D",
-    "/ko:sioskiyeokjongseong": "\u11E7",
-    "/ko:siosmieumchoseong": "\u1131",
-    "/ko:siosnieun": "\u317B",
-    "/ko:siosnieunchoseong": "\u112E",
-    "/ko:siosphieuphchoseong": "\u113A",
-    "/ko:siospieup": "\u317D",
-    "/ko:siospieupchoseong": "\u1132",
-    "/ko:siospieupjongseong": "\u11EA",
-    "/ko:siospieupkiyeokchoseong": "\u1133",
-    "/ko:siosrieulchoseong": "\u1130",
-    "/ko:siosrieuljongseong": "\u11E9",
-    "/ko:siosssangsioschoseong": "\u1134",
-    "/ko:siosthieuthchoseong": "\u1139",
-    "/ko:siostikeut": "\u317C",
-    "/ko:siostikeutchoseong": "\u112F",
-    "/ko:siostikeutjongseong": "\u11E8",
-    "/ko:ssangaraeajungseong": "\u11A2",
-    "/ko:ssangcieuc": "\u3149",
-    "/ko:ssangcieucchoseong": "\u110D",
-    "/ko:ssanghieuh": "\u3185",
-    "/ko:ssanghieuhchoseong": "\u1158",
-    "/ko:ssangieung": "\u3180",
-    "/ko:ssangieungchoseong": "\u1147",
-    "/ko:ssangieungjongseong": "\u11EE",
-    "/ko:ssangkiyeok": "\u3132",
-    "/ko:ssangkiyeokchoseong": "\u1101",
-    "/ko:ssangkiyeokjongseong": "\u11A9",
-    "/ko:ssangnieun": "\u3165",
-    "/ko:ssangnieunchoseong": "\u1114",
-    "/ko:ssangnieunjongseong": "\u11FF",
-    "/ko:ssangpieup": "\u3143",
-    "/ko:ssangpieupchoseong": "\u1108",
-    "/ko:ssangrieulchoseong": "\u1119",
-    "/ko:ssangrieuljongseong": "\u11D0",
-    "/ko:ssangsios": "\u3146",
-    "/ko:ssangsioschoseong": "\u110A",
-    "/ko:ssangsiosjongseong": "\u11BB",
-    "/ko:ssangtikeut": "\u3138",
-    "/ko:ssangtikeutchoseong": "\u1104",
-    "/ko:thieuth": "\u314C",
-    "/ko:thieuthchoseong": "\u1110",
-    "/ko:thieuthjongseong": "\u11C0",
-    "/ko:tikeut": "\u3137",
-    "/ko:tikeutchoseong": "\u1103",
-    "/ko:tikeutjongseong": "\u11AE",
-    "/ko:tikeutkiyeokchoseong": "\u1117",
-    "/ko:tikeutkiyeokjongseong": "\u11CA",
-    "/ko:tikeutrieulchoseong": "\u115E",
-    "/ko:tikeutrieuljongseong": "\u11CB",
-    "/ko:u": "\u315C",
-    "/ko:uaejungseong": "\u118A",
-    "/ko:uajungseong": "\u1189",
-    "/ko:ueo_eujungseong": "\u118B",
-    "/ko:ujungseong": "\u116E",
-    "/ko:uujungseong": "\u118D",
-    "/ko:uyejungseong": "\u118C",
-    "/ko:wa": "\u3158",
-    "/ko:wae": "\u3159",
-    "/ko:waejungseong": "\u116B",
-    "/ko:wajungseong": "\u116A",
-    "/ko:we": "\u315E",
-    "/ko:wejungseong": "\u1170",
-    "/ko:weo": "\u315D",
-    "/ko:weojungseong": "\u116F",
-    "/ko:wi": "\u315F",
-    "/ko:wijungseong": "\u1171",
-    "/ko:ya": "\u3151",
-    "/ko:yae": "\u3152",
-    "/ko:yaejungseong": "\u1164",
-    "/ko:yajungseong": "\u1163",
-    "/ko:yaojungseong": "\u1178",
-    "/ko:yaujungseong": "\u11A4",
-    "/ko:yayojungseong": "\u1179",
-    "/ko:ye": "\u3156",
-    "/ko:yejungseong": "\u1168",
-    "/ko:yeo": "\u3155",
-    "/ko:yeojungseong": "\u1167",
-    "/ko:yeoojungseong": "\u117D",
-    "/ko:yeorinhieuh": "\u3186",
-    "/ko:yeorinhieuhchoseong": "\u1159",
-    "/ko:yeorinhieuhjongseong": "\u11F9",
-    "/ko:yeoujungseong": "\u117E",
-    "/ko:yeoyajungseong": "\u11A5",
-    "/ko:yesieung": "\u3181",
-    "/ko:yesieungchoseong": "\u114C",
-    "/ko:yesieungjongseong": "\u11F0",
-    "/ko:yesieungpansios": "\u3183",
-    "/ko:yesieungpansiosjongseong": "\u11F2",
-    "/ko:yesieungsios": "\u3182",
-    "/ko:yesieungsiosjongseong": "\u11F1",
-    "/ko:yi": "\u3162",
-    "/ko:yijungseong": "\u1174",
-    "/ko:yiujungseong": "\u1197",
-    "/ko:yo": "\u315B",
-    "/ko:yoi": "\u3189",
-    "/ko:yoijungseong": "\u1188",
-    "/ko:yojungseong": "\u116D",
-    "/ko:yoojungseong": "\u1187",
-    "/ko:yoya": "\u3187",
-    "/ko:yoyae": "\u3188",
-    "/ko:yoyaejungseong": "\u1185",
-    "/ko:yoyajungseong": "\u1184",
-    "/ko:yoyeojungseong": "\u1186",
-    "/ko:yu": "\u3160",
-    "/ko:yuajungseong": "\u118E",
-    "/ko:yuejungseong": "\u1190",
-    "/ko:yueojungseong": "\u118F",
-    "/ko:yui": "\u318C",
-    "/ko:yuijungseong": "\u1194",
-    "/ko:yujungseong": "\u1172",
-    "/ko:yuujungseong": "\u1193",
-    "/ko:yuye": "\u318B",
-    "/ko:yuyejungseong": "\u1192",
-    "/ko:yuyeo": "\u318A",
-    "/ko:yuyeojungseong": "\u1191",
-    "/koala": "\u1F428",
-    "/kobliquestroke": "\uA7A3",
-    "/kocirclekatakana": "\u32D9",
-    "/kohiragana": "\u3053",
-    "/kohmfullwidth": "\u33C0",
-    "/kohmsquare": "\u33C0",
-    "/kokaithai": "\u0E01",
-    "/kokatakana": "\u30B3",
-    "/kokatakanahalfwidth": "\uFF7A",
-    "/kooposquare": "\u331E",
-    "/koppa": "\u03DF",
-    "/koppaarchaic": "\u03D9",
-    "/koppacyr": "\u0481",
-    "/koppacyrillic": "\u0481",
-    "/koreanstandardsymbol": "\u327F",
-    "/koroniscmb": "\u0343",
-    "/korunasquare": "\u331D",
-    "/kotoideographiccircled": "\u3247",
-    "/kpafullwidth": "\u33AA",
-    "/kparen": "\u24A6",
-    "/kparenthesized": "\u24A6",
-    "/kpasquare": "\u33AA",
-    "/kra": "\u0138",
-    "/ksicyr": "\u046F",
-    "/ksicyrillic": "\u046F",
-    "/kstroke": "\uA741",
-    "/kstrokediagonalstroke": "\uA745",
-    "/ktfullwidth": "\u33CF",
-    "/ktsquare": "\u33CF",
-    "/kturned": "\u029E",
-    "/kucirclekatakana": "\u32D7",
-    "/kuhiragana": "\u304F",
-    "/kukatakana": "\u30AF",
-    "/kukatakanahalfwidth": "\uFF78",
-    "/kuroonesquare": "\u331B",
-    "/kuruzeirosquare": "\u331A",
-    "/kvfullwidth": "\u33B8",
-    "/kvsquare": "\u33B8",
-    "/kwfullwidth": "\u33BE",
-    "/kwsquare": "\u33BE",
-    "/kyuriisquare": "\u3312",
-    "/l": "\u006C",
-    "/l.inferior": "\u2097",
-    "/label": "\u1F3F7",
-    "/labengali": "\u09B2",
-    "/laborideographiccircled": "\u3298",
-    "/laborideographicparen": "\u3238",
-    "/lacute": "\u013A",
-    "/ladeva": "\u0932",
-    "/ladyBeetle": "\u1F41E",
-    "/lagujarati": "\u0AB2",
-    "/lagurmukhi": "\u0A32",
-    "/lakkhangyaothai": "\u0E45",
-    "/lam": "\u0644",
-    "/lam.fina": "\uFEDE",
-    "/lam.init": "\uFEDF",
-    "/lam.init_alef.fina": "\uFEFB",
-    "/lam.init_alef.medi_hamzaabove.fina": "\uFEF7",
-    "/lam.init_alef.medi_hamzabelow.fina": "\uFEF9",
-    "/lam.init_alef.medi_maddaabove.fina": "\uFEF5",
-    "/lam.init_alefmaksura.fina": "\uFC43",
-    "/lam.init_hah.fina": "\uFC40",
-    "/lam.init_hah.medi": "\uFCCA",
-    "/lam.init_hah.medi_meem.medi": "\uFDB5",
-    "/lam.init_heh.medi": "\uFCCD",
-    "/lam.init_jeem.fina": "\uFC3F",
-    "/lam.init_jeem.medi": "\uFCC9",
-    "/lam.init_jeem.medi_jeem.medi": "\uFD83",
-    "/lam.init_jeem.medi_meem.medi": "\uFDBA",
-    "/lam.init_khah.fina": "\uFC41",
-    "/lam.init_khah.medi": "\uFCCB",
-    "/lam.init_khah.medi_meem.medi": "\uFD86",
-    "/lam.init_meem.fina": "\uFC42",
-    "/lam.init_meem.medi": "\uFCCC",
-    "/lam.init_meem.medi_hah.medi": "\uFD88",
-    "/lam.init_yeh.fina": "\uFC44",
-    "/lam.isol": "\uFEDD",
-    "/lam.medi": "\uFEE0",
-    "/lam.medi_alef.fina": "\uFEFC",
-    "/lam.medi_alef.medi_hamzaabove.fina": "\uFEF8",
-    "/lam.medi_alef.medi_hamzabelow.fina": "\uFEFA",
-    "/lam.medi_alef.medi_maddaabove.fina": "\uFEF6",
-    "/lam.medi_alefmaksura.fina": "\uFC86",
-    "/lam.medi_hah.medi_alefmaksura.fina": "\uFD82",
-    "/lam.medi_hah.medi_meem.fina": "\uFD80",
-    "/lam.medi_hah.medi_yeh.fina": "\uFD81",
-    "/lam.medi_jeem.medi_jeem.fina": "\uFD84",
-    "/lam.medi_jeem.medi_meem.fina": "\uFDBC",
-    "/lam.medi_jeem.medi_yeh.fina": "\uFDAC",
-    "/lam.medi_khah.medi_meem.fina": "\uFD85",
-    "/lam.medi_meem.fina": "\uFC85",
-    "/lam.medi_meem.medi": "\uFCED",
-    "/lam.medi_meem.medi_hah.fina": "\uFD87",
-    "/lam.medi_meem.medi_yeh.fina": "\uFDAD",
-    "/lam.medi_yeh.fina": "\uFC87",
-    "/lamBar": "\u076A",
-    "/lamVabove": "\u06B5",
-    "/lamalefabove": "\u06D9",
-    "/lamaleffinalarabic": "\uFEFC",
-    "/lamalefhamzaabovefinalarabic": "\uFEF8",
-    "/lamalefhamzaaboveisolatedarabic": "\uFEF7",
-    "/lamalefhamzabelowfinalarabic": "\uFEFA",
-    "/lamalefhamzabelowisolatedarabic": "\uFEF9",
-    "/lamalefisolatedarabic": "\uFEFB",
-    "/lamalefmaddaabovefinalarabic": "\uFEF6",
-    "/lamalefmaddaaboveisolatedarabic": "\uFEF5",
-    "/lamarabic": "\u0644",
-    "/lambda": "\u03BB",
-    "/lambdastroke": "\u019B",
-    "/lamdotabove": "\u06B6",
-    "/lamed": "\u05DC",
-    "/lamed:hb": "\u05DC",
-    "/lameddagesh": "\uFB3C",
-    "/lameddageshhebrew": "\uFB3C",
-    "/lamedhebrew": "\u05DC",
-    "/lamedholam": "\u05DC",
-    "/lamedholamdagesh": "\u05DC",
-    "/lamedholamdageshhebrew": "\u05DC",
-    "/lamedholamhebrew": "\u05DC",
-    "/lamedwide:hb": "\uFB25",
-    "/lamedwithdagesh:hb": "\uFB3C",
-    "/lamfinalarabic": "\uFEDE",
-    "/lamhahinitialarabic": "\uFCCA",
-    "/laminitialarabic": "\uFEDF",
-    "/lamjeeminitialarabic": "\uFCC9",
-    "/lamkhahinitialarabic": "\uFCCB",
-    "/lamlamhehisolatedarabic": "\uFDF2",
-    "/lammedialarabic": "\uFEE0",
-    "/lammeemhahinitialarabic": "\uFD88",
-    "/lammeeminitialarabic": "\uFCCC",
-    "/lammeemjeeminitialarabic": "\uFEDF",
-    "/lammeemkhahinitialarabic": "\uFEDF",
-    "/lamthreedotsabove": "\u06B7",
-    "/lamthreedotsbelow": "\u06B8",
-    "/lanemergeleftblack": "\u26D8",
-    "/lanemergeleftwhite": "\u26D9",
-    "/largeBlueCircle": "\u1F535",
-    "/largeBlueDiamond": "\u1F537",
-    "/largeOrangeDiamond": "\u1F536",
-    "/largeRedCircle": "\u1F534",
-    "/largecircle": "\u25EF",
-    "/largetackdown": "\u27D9",
-    "/largetackup": "\u27D8",
-    "/lari": "\u20BE",
-    "/lastQuarterMoon": "\u1F317",
-    "/lastQuarterMoonFace": "\u1F31C",
-    "/lastquartermoon": "\u263E",
-    "/layar": "\uA982",
-    "/lazysinverted": "\u223E",
-    "/lbar": "\u019A",
-    "/lbbar": "\u2114",
-    "/lbelt": "\u026C",
-    "/lbeltretroflex": "\uA78E",
-    "/lbopomofo": "\u310C",
-    "/lbroken": "\uA747",
-    "/lcaron": "\u013E",
-    "/lcedilla": "\u013C",
-    "/lcircle": "\u24DB",
-    "/lcircumflexbelow": "\u1E3D",
-    "/lcommaaccent": "\u013C",
-    "/lcurl": "\u0234",
-    "/ldblbar": "\u2C61",
-    "/ldot": "\u0140",
-    "/ldotaccent": "\u0140",
-    "/ldotbelow": "\u1E37",
-    "/ldotbelowmacron": "\u1E39",
-    "/leafFlutteringInWind": "\u1F343",
-    "/ledger": "\u1F4D2",
-    "/left-pointingMagnifyingGlass": "\u1F50D",
-    "/leftAngerBubble": "\u1F5EE",
-    "/leftFiveEighthsBlock": "\u258B",
-    "/leftHalfBlock": "\u258C",
-    "/leftHandTelephoneReceiver": "\u1F57B",
-    "/leftLuggage": "\u1F6C5",
-    "/leftOneEighthBlock": "\u258F",
-    "/leftOneQuarterBlock": "\u258E",
-    "/leftSevenEighthsBlock": "\u2589",
-    "/leftSpeechBubble": "\u1F5E8",
-    "/leftThoughtBubble": "\u1F5EC",
-    "/leftThreeEighthsBlock": "\u258D",
-    "/leftThreeQuartersBlock": "\u258A",
-    "/leftWritingHand": "\u1F58E",
-    "/leftangleabovecmb": "\u031A",
-    "/leftarrowoverrightarrow": "\u21C6",
-    "/leftdnheavyrightuplight": "\u2545",
-    "/leftharpoonoverrightharpoon": "\u21CB",
-    "/leftheavyrightdnlight": "\u252D",
-    "/leftheavyrightuplight": "\u2535",
-    "/leftheavyrightvertlight": "\u253D",
-    "/leftideographiccircled": "\u32A7",
-    "/leftlightrightdnheavy": "\u2532",
-    "/leftlightrightupheavy": "\u253A",
-    "/leftlightrightvertheavy": "\u254A",
-    "/lefttackbelowcmb": "\u0318",
-    "/lefttorightembed": "\u202A",
-    "/lefttorightisolate": "\u2066",
-    "/lefttorightmark": "\u200E",
-    "/lefttorightoverride": "\u202D",
-    "/leftupheavyrightdnlight": "\u2543",
-    "/lemon": "\u1F34B",
-    "/lenis": "\u1FBF",
-    "/lenisacute": "\u1FCE",
-    "/lenisgrave": "\u1FCD",
-    "/lenistilde": "\u1FCF",
-    "/leo": "\u264C",
-    "/leopard": "\u1F406",
-    "/less": "\u003C",
-    "/lessbutnotequal": "\u2268",
-    "/lessbutnotequivalent": "\u22E6",
-    "/lessdot": "\u22D6",
-    "/lessequal": "\u2264",
-    "/lessequalorgreater": "\u22DA",
-    "/lessmonospace": "\uFF1C",
-    "/lessorequivalent": "\u2272",
-    "/lessorgreater": "\u2276",
-    "/lessoverequal": "\u2266",
-    "/lesssmall": "\uFE64",
-    "/levelSlider": "\u1F39A",
-    "/lezh": "\u026E",
-    "/lfblock": "\u258C",
-    "/lhacyr": "\u0515",
-    "/lhookretroflex": "\u026D",
-    "/libra": "\u264E",
-    "/ligaturealeflamed:hb": "\uFB4F",
-    "/ligatureoemod": "\uA7F9",
-    "/lightCheckMark": "\u1F5F8",
-    "/lightRail": "\u1F688",
-    "/lightShade": "\u2591",
-    "/lightarcdnleft": "\u256E",
-    "/lightarcdnright": "\u256D",
-    "/lightarcupleft": "\u256F",
-    "/lightarcupright": "\u2570",
-    "/lightdbldashhorz": "\u254C",
-    "/lightdbldashvert": "\u254E",
-    "/lightdiagcross": "\u2573",
-    "/lightdiagupleftdnright": "\u2572",
-    "/lightdiaguprightdnleft": "\u2571",
-    "/lightdn": "\u2577",
-    "/lightdnhorz": "\u252C",
-    "/lightdnleft": "\u2510",
-    "/lightdnright": "\u250C",
-    "/lighthorz": "\u2500",
-    "/lightleft": "\u2574",
-    "/lightleftheavyright": "\u257C",
-    "/lightning": "\u2607",
-    "/lightningMood": "\u1F5F2",
-    "/lightningMoodBubble": "\u1F5F1",
-    "/lightquaddashhorz": "\u2508",
-    "/lightquaddashvert": "\u250A",
-    "/lightright": "\u2576",
-    "/lighttrpldashhorz": "\u2504",
-    "/lighttrpldashvert": "\u2506",
-    "/lightup": "\u2575",
-    "/lightupheavydn": "\u257D",
-    "/lightuphorz": "\u2534",
-    "/lightupleft": "\u2518",
-    "/lightupright": "\u2514",
-    "/lightvert": "\u2502",
-    "/lightverthorz": "\u253C",
-    "/lightvertleft": "\u2524",
-    "/lightvertright": "\u251C",
-    "/lineextensionhorizontal": "\u23AF",
-    "/lineextensionvertical": "\u23D0",
-    "/linemiddledotvertical": "\u237F",
-    "/lineseparator": "\u2028",
-    "/lingsapada": "\uA9C8",
-    "/link": "\u1F517",
-    "/linkedPaperclips": "\u1F587",
-    "/lips": "\u1F5E2",
-    "/lipstick": "\u1F484",
-    "/lira": "\u20A4",
-    "/litre": "\u2113",
-    "/livretournois": "\u20B6",
-    "/liwnarmenian": "\u056C",
-    "/lj": "\u01C9",
-    "/ljecyr": "\u0459",
-    "/ljecyrillic": "\u0459",
-    "/ljekomicyr": "\u0509",
-    "/ll": "\uF6C0",
-    "/lladeva": "\u0933",
-    "/llagujarati": "\u0AB3",
-    "/llinebelow": "\u1E3B",
-    "/llladeva": "\u0934",
-    "/llvocalicbengali": "\u09E1",
-    "/llvocalicdeva": "\u0961",
-    "/llvocalicvowelsignbengali": "\u09E3",
-    "/llvocalicvowelsigndeva": "\u0963",
-    "/llwelsh": "\u1EFB",
-    "/lmacrondot": "\u1E39",
-    "/lmfullwidth": "\u33D0",
-    "/lmiddletilde": "\u026B",
-    "/lmonospace": "\uFF4C",
-    "/lmsquare": "\u33D0",
-    "/lnfullwidth": "\u33D1",
-    "/lochulathai": "\u0E2C",
-    "/lock": "\u1F512",
-    "/lockInkPen": "\u1F50F",
-    "/logfullwidth": "\u33D2",
-    "/logicaland": "\u2227",
-    "/logicalandarray": "\u22C0",
-    "/logicalnot": "\u00AC",
-    "/logicalnotreversed": "\u2310",
-    "/logicalor": "\u2228",
-    "/logicalorarray": "\u22C1",
-    "/lolingthai": "\u0E25",
-    "/lollipop": "\u1F36D",
-    "/longdivision": "\u27CC",
-    "/longovershortmetrical": "\u23D2",
-    "/longovertwoshortsmetrical": "\u23D4",
-    "/longs": "\u017F",
-    "/longs_t": "\uFB05",
-    "/longsdot": "\u1E9B",
-    "/longswithdiagonalstroke": "\u1E9C",
-    "/longswithhighstroke": "\u1E9D",
-    "/longtackleft": "\u27DE",
-    "/longtackright": "\u27DD",
-    "/losslesssquare": "\u1F1A9",
-    "/loudlyCryingFace": "\u1F62D",
-    "/loveHotel": "\u1F3E9",
-    "/loveLetter": "\u1F48C",
-    "/lowBrightness": "\u1F505",
-    "/lowasterisk": "\u204E",
-    "/lowerFiveEighthsBlock": "\u2585",
-    "/lowerHalfBlock": "\u2584",
-    "/lowerLeftBallpointPen": "\u1F58A",
-    "/lowerLeftCrayon": "\u1F58D",
-    "/lowerLeftFountainPen": "\u1F58B",
-    "/lowerLeftPaintbrush": "\u1F58C",
-    "/lowerLeftPencil": "\u1F589",
-    "/lowerOneEighthBlock": "\u2581",
-    "/lowerOneQuarterBlock": "\u2582",
-    "/lowerRightShadowedWhiteCircle": "\u1F53E",
-    "/lowerSevenEighthsBlock": "\u2587",
-    "/lowerThreeEighthsBlock": "\u2583",
-    "/lowerThreeQuartersBlock": "\u2586",
-    "/lowercornerdotright": "\u27D3",
-    "/lowerhalfcircle": "\u25E1",
-    "/lowerhalfcircleinversewhite": "\u25DB",
-    "/lowerquadrantcirculararcleft": "\u25DF",
-    "/lowerquadrantcirculararcright": "\u25DE",
-    "/lowertriangleleft": "\u25FA",
-    "/lowertriangleleftblack": "\u25E3",
-    "/lowertriangleright": "\u25FF",
-    "/lowertrianglerightblack": "\u25E2",
-    "/lowideographiccircled": "\u32A6",
-    "/lowlinecenterline": "\uFE4E",
-    "/lowlinecmb": "\u0332",
-    "/lowlinedashed": "\uFE4D",
-    "/lownumeralsign": "\u0375",
-    "/lowquotedblprime": "\u301F",
-    "/lozenge": "\u25CA",
-    "/lozengedividedbyrulehorizontal": "\u27E0",
-    "/lozengesquare": "\u2311",
-    "/lparen": "\u24A7",
-    "/lparenthesized": "\u24A7",
-    "/lretroflex": "\u026D",
-    "/ls": "\u02AA",
-    "/lslash": "\u0142",
-    "/lsquare": "\u2113",
-    "/lstroke": "\uA749",
-    "/lsuperior": "\uF6EE",
-    "/lsupmod": "\u02E1",
-    "/lt:Alpha": "\u2C6D",
-    "/lt:Alphaturned": "\u2C70",
-    "/lt:Beta": "\uA7B4",
-    "/lt:Chi": "\uA7B3",
-    "/lt:Gamma": "\u0194",
-    "/lt:Iota": "\u0196",
-    "/lt:Omega": "\uA7B6",
-    "/lt:Upsilon": "\u01B1",
-    "/lt:beta": "\uA7B5",
-    "/lt:delta": "\u1E9F",
-    "/lt:omega": "\uA7B7",
-    "/ltshade": "\u2591",
-    "/lttr:bet": "\u2136",
-    "/lttr:dalet": "\u2138",
-    "/lttr:gimel": "\u2137",
-    "/lttr:gscript": "\u210A",
-    "/lturned": "\uA781",
-    "/ltypeopencircuit": "\u2390",
-    "/luhurpada": "\uA9C5",
-    "/lum": "\uA772",
-    "/lungsipada": "\uA9C9",
-    "/luthai": "\u0E26",
-    "/lvocalicbengali": "\u098C",
-    "/lvocalicdeva": "\u090C",
-    "/lvocalicvowelsignbengali": "\u09E2",
-    "/lvocalicvowelsigndeva": "\u0962",
-    "/lxfullwidth": "\u33D3",
-    "/lxsquare": "\u33D3",
-    "/lzed": "\u02AB",
-    "/m": "\u006D",
-    "/m.inferior": "\u2098",
-    "/m2fullwidth": "\u33A1",
-    "/m3fullwidth": "\u33A5",
-    "/mabengali": "\u09AE",
-    "/macirclekatakana": "\u32EE",
-    "/macron": "\u00AF",
-    "/macronbelowcmb": "\u0331",
-    "/macroncmb": "\u0304",
-    "/macronlowmod": "\u02CD",
-    "/macronmod": "\u02C9",
-    "/macronmonospace": "\uFFE3",
-    "/macute": "\u1E3F",
-    "/madda": "\u0653",
-    "/maddaabove": "\u06E4",
-    "/madeva": "\u092E",
-    "/madyapada": "\uA9C4",
-    "/mafullwidth": "\u3383",
-    "/magujarati": "\u0AAE",
-    "/magurmukhi": "\u0A2E",
-    "/mahapakhhebrew": "\u05A4",
-    "/mahapakhlefthebrew": "\u05A4",
-    "/mahhasquare": "\u3345",
-    "/mahiragana": "\u307E",
-    "/mahpach:hb": "\u05A4",
-    "/maichattawalowleftthai": "\uF895",
-    "/maichattawalowrightthai": "\uF894",
-    "/maichattawathai": "\u0E4B",
-    "/maichattawaupperleftthai": "\uF893",
-    "/maieklowleftthai": "\uF88C",
-    "/maieklowrightthai": "\uF88B",
-    "/maiekthai": "\u0E48",
-    "/maiekupperleftthai": "\uF88A",
-    "/maihanakatleftthai": "\uF884",
-    "/maihanakatthai": "\u0E31",
-    "/maikurosquare": "\u3343",
-    "/mairusquare": "\u3344",
-    "/maitaikhuleftthai": "\uF889",
-    "/maitaikhuthai": "\u0E47",
-    "/maitholowleftthai": "\uF88F",
-    "/maitholowrightthai": "\uF88E",
-    "/maithothai": "\u0E49",
-    "/maithoupperleftthai": "\uF88D",
-    "/maitrilowleftthai": "\uF892",
-    "/maitrilowrightthai": "\uF891",
-    "/maitrithai": "\u0E4A",
-    "/maitriupperleftthai": "\uF890",
-    "/maiyamokthai": "\u0E46",
-    "/makatakana": "\u30DE",
-    "/makatakanahalfwidth": "\uFF8F",
-    "/male": "\u2642",
-    "/malefemale": "\u26A5",
-    "/maleideographiccircled": "\u329A",
-    "/malestroke": "\u26A6",
-    "/malestrokemalefemale": "\u26A7",
-    "/man": "\u1F468",
-    "/manAndWomanHoldingHands": "\u1F46B",
-    "/manDancing": "\u1F57A",
-    "/manGuaPiMao": "\u1F472",
-    "/manInBusinessSuitLevitating": "\u1F574",
-    "/manTurban": "\u1F473",
-    "/manat": "\u20BC",
-    "/mansShoe": "\u1F45E",
-    "/mansyonsquare": "\u3347",
-    "/mantelpieceClock": "\u1F570",
-    "/mapleLeaf": "\u1F341",
-    "/maplighthouse": "\u26EF",
-    "/maqaf:hb": "\u05BE",
-    "/maqafhebrew": "\u05BE",
-    "/marchtelegraph": "\u32C2",
-    "/mark": "\u061C",
-    "/markerdottedraisedinterpolation": "\u2E07",
-    "/markerdottedtransposition": "\u2E08",
-    "/markerraisedinterpolation": "\u2E06",
-    "/marknoonghunna": "\u0658",
-    "/marksChapter": "\u1F545",
-    "/marriage": "\u26AD",
-    "/mars": "\u2642",
-    "/marukusquare": "\u3346",
-    "/masoraCircle:hb": "\u05AF",
-    "/masoracirclehebrew": "\u05AF",
-    "/masquare": "\u3383",
-    "/masumark": "\u303C",
-    "/math:bowtie": "\u22C8",
-    "/math:cuberoot": "\u221B",
-    "/math:fourthroot": "\u221C",
-    "/maximize": "\u1F5D6",
-    "/maytelegraph": "\u32C4",
-    "/mbfullwidth": "\u3386",
-    "/mbopomofo": "\u3107",
-    "/mbsmallfullwidth": "\u33D4",
-    "/mbsquare": "\u33D4",
-    "/mcircle": "\u24DC",
-    "/mcubedsquare": "\u33A5",
-    "/mdot": "\u1E41",
-    "/mdotaccent": "\u1E41",
-    "/mdotbelow": "\u1E43",
-    "/measuredangle": "\u2221",
-    "/measuredby": "\u225E",
-    "/meatOnBone": "\u1F356",
-    "/mecirclekatakana": "\u32F1",
-    "/medicineideographiccircled": "\u32A9",
-    "/mediumShade": "\u2592",
-    "/mediumcircleblack": "\u26AB",
-    "/mediumcirclewhite": "\u26AA",
-    "/mediummathematicalspace": "\u205F",
-    "/mediumsmallcirclewhite": "\u26AC",
-    "/meem": "\u0645",
-    "/meem.fina": "\uFEE2",
-    "/meem.init": "\uFEE3",
-    "/meem.init_alefmaksura.fina": "\uFC49",
-    "/meem.init_hah.fina": "\uFC46",
-    "/meem.init_hah.medi": "\uFCCF",
-    "/meem.init_hah.medi_jeem.medi": "\uFD89",
-    "/meem.init_hah.medi_meem.medi": "\uFD8A",
-    "/meem.init_jeem.fina": "\uFC45",
-    "/meem.init_jeem.medi": "\uFCCE",
-    "/meem.init_jeem.medi_hah.medi": "\uFD8C",
-    "/meem.init_jeem.medi_khah.medi": "\uFD92",
-    "/meem.init_jeem.medi_meem.medi": "\uFD8D",
-    "/meem.init_khah.fina": "\uFC47",
-    "/meem.init_khah.medi": "\uFCD0",
-    "/meem.init_khah.medi_jeem.medi": "\uFD8E",
-    "/meem.init_khah.medi_meem.medi": "\uFD8F",
-    "/meem.init_meem.fina": "\uFC48",
-    "/meem.init_meem.medi": "\uFCD1",
-    "/meem.init_yeh.fina": "\uFC4A",
-    "/meem.isol": "\uFEE1",
-    "/meem.medi": "\uFEE4",
-    "/meem.medi_alef.fina": "\uFC88",
-    "/meem.medi_hah.medi_yeh.fina": "\uFD8B",
-    "/meem.medi_jeem.medi_yeh.fina": "\uFDC0",
-    "/meem.medi_khah.medi_yeh.fina": "\uFDB9",
-    "/meem.medi_meem.fina": "\uFC89",
-    "/meem.medi_meem.medi_yeh.fina": "\uFDB1",
-    "/meemDotAbove": "\u0765",
-    "/meemDotBelow": "\u0766",
-    "/meemabove": "\u06E2",
-    "/meemabove.init": "\u06D8",
-    "/meemarabic": "\u0645",
-    "/meembelow": "\u06ED",
-    "/meemfinalarabic": "\uFEE2",
-    "/meeminitialarabic": "\uFEE3",
-    "/meemmedialarabic": "\uFEE4",
-    "/meemmeeminitialarabic": "\uFCD1",
-    "/meemmeemisolatedarabic": "\uFC48",
-    "/meetorusquare": "\u334D",
-    "/megasquare": "\u334B",
-    "/megatonsquare": "\u334C",
-    "/mehiragana": "\u3081",
-    "/meizierasquare": "\u337E",
-    "/mekatakana": "\u30E1",
-    "/mekatakanahalfwidth": "\uFF92",
-    "/melon": "\u1F348",
-    "/mem": "\u05DE",
-    "/mem:hb": "\u05DE",
-    "/memdagesh": "\uFB3E",
-    "/memdageshhebrew": "\uFB3E",
-    "/memhebrew": "\u05DE",
-    "/memo": "\u1F4DD",
-    "/memwithdagesh:hb": "\uFB3E",
-    "/menarmenian": "\u0574",
-    "/menorahNineBranches": "\u1F54E",
-    "/menpostSindhi": "\u06FE",
-    "/mens": "\u1F6B9",
-    "/mepigraphicinverted": "\uA7FD",
-    "/mercha:hb": "\u05A5",
-    "/merchaKefulah:hb": "\u05A6",
-    "/mercury": "\u263F",
-    "/merkhahebrew": "\u05A5",
-    "/merkhakefulahebrew": "\u05A6",
-    "/merkhakefulalefthebrew": "\u05A6",
-    "/merkhalefthebrew": "\u05A5",
-    "/metalideographiccircled": "\u328E",
-    "/metalideographicparen": "\u322E",
-    "/meteg:hb": "\u05BD",
-    "/metro": "\u1F687",
-    "/mgfullwidth": "\u338E",
-    "/mhook": "\u0271",
-    "/mhzfullwidth": "\u3392",
-    "/mhzsquare": "\u3392",
-    "/micirclekatakana": "\u32EF",
-    "/microphone": "\u1F3A4",
-    "/microscope": "\u1F52C",
-    "/middledotkatakanahalfwidth": "\uFF65",
-    "/middot": "\u00B7",
-    "/mieumacirclekorean": "\u3272",
-    "/mieumaparenkorean": "\u3212",
-    "/mieumcirclekorean": "\u3264",
-    "/mieumkorean": "\u3141",
-    "/mieumpansioskorean": "\u3170",
-    "/mieumparenkorean": "\u3204",
-    "/mieumpieupkorean": "\u316E",
-    "/mieumsioskorean": "\u316F",
-    "/mihiragana": "\u307F",
-    "/mikatakana": "\u30DF",
-    "/mikatakanahalfwidth": "\uFF90",
-    "/mikuronsquare": "\u3348",
-    "/milfullwidth": "\u33D5",
-    "/militaryMedal": "\u1F396",
-    "/milkyWay": "\u1F30C",
-    "/mill": "\u20A5",
-    "/millionscmbcyr": "\u0489",
-    "/millisecond": "\u2034",
-    "/millisecondreversed": "\u2037",
-    "/minibus": "\u1F690",
-    "/minidisc": "\u1F4BD",
-    "/minimize": "\u1F5D5",
-    "/minus": "\u2212",
-    "/minus.inferior": "\u208B",
-    "/minus.superior": "\u207B",
-    "/minusbelowcmb": "\u0320",
-    "/minuscircle": "\u2296",
-    "/minusmod": "\u02D7",
-    "/minusplus": "\u2213",
-    "/minussignmod": "\u02D7",
-    "/minustilde": "\u2242",
-    "/minute": "\u2032",
-    "/minutereversed": "\u2035",
-    "/miribaarusquare": "\u334A",
-    "/mirisquare": "\u3349",
-    "/misc:baby": "\u1F476",
-    "/misc:bell": "\u1F514",
-    "/misc:dash": "\u1F4A8",
-    "/misc:decimalseparator": "\u2396",
-    "/misc:diamondblack": "\u2666",
-    "/misc:diamondwhite": "\u2662",
-    "/misc:ear": "\u1F442",
-    "/misc:om": "\u1F549",
-    "/misc:ring": "\u1F48D",
-    "/misra": "\u060F",
-    "/mlfullwidth": "\u3396",
-    "/mlonglegturned": "\u0270",
-    "/mlsquare": "\u3396",
-    "/mlym:a": "\u0D05",
-    "/mlym:aa": "\u0D06",
-    "/mlym:aasign": "\u0D3E",
-    "/mlym:ai": "\u0D10",
-    "/mlym:aisign": "\u0D48",
-    "/mlym:anusvarasign": "\u0D02",
-    "/mlym:archaicii": "\u0D5F",
-    "/mlym:au": "\u0D14",
-    "/mlym:aulength": "\u0D57",
-    "/mlym:ausign": "\u0D4C",
-    "/mlym:avagrahasign": "\u0D3D",
-    "/mlym:ba": "\u0D2C",
-    "/mlym:bha": "\u0D2D",
-    "/mlym:ca": "\u0D1A",
-    "/mlym:candrabindusign": "\u0D01",
-    "/mlym:cha": "\u0D1B",
-    "/mlym:circularviramasign": "\u0D3C",
-    "/mlym:combininganusvaraabovesign": "\u0D00",
-    "/mlym:da": "\u0D26",
-    "/mlym:date": "\u0D79",
-    "/mlym:dda": "\u0D21",
-    "/mlym:ddha": "\u0D22",
-    "/mlym:dha": "\u0D27",
-    "/mlym:dotreph": "\u0D4E",
-    "/mlym:e": "\u0D0E",
-    "/mlym:ee": "\u0D0F",
-    "/mlym:eesign": "\u0D47",
-    "/mlym:eight": "\u0D6E",
-    "/mlym:esign": "\u0D46",
-    "/mlym:five": "\u0D6B",
-    "/mlym:four": "\u0D6A",
-    "/mlym:ga": "\u0D17",
-    "/mlym:gha": "\u0D18",
-    "/mlym:ha": "\u0D39",
-    "/mlym:i": "\u0D07",
-    "/mlym:ii": "\u0D08",
-    "/mlym:iisign": "\u0D40",
-    "/mlym:isign": "\u0D3F",
-    "/mlym:ja": "\u0D1C",
-    "/mlym:jha": "\u0D1D",
-    "/mlym:ka": "\u0D15",
-    "/mlym:kchillu": "\u0D7F",
-    "/mlym:kha": "\u0D16",
-    "/mlym:la": "\u0D32",
-    "/mlym:lchillu": "\u0D7D",
-    "/mlym:lla": "\u0D33",
-    "/mlym:llchillu": "\u0D7E",
-    "/mlym:llla": "\u0D34",
-    "/mlym:lllchillu": "\u0D56",
-    "/mlym:llvocal": "\u0D61",
-    "/mlym:llvocalsign": "\u0D63",
-    "/mlym:lvocal": "\u0D0C",
-    "/mlym:lvocalsign": "\u0D62",
-    "/mlym:ma": "\u0D2E",
-    "/mlym:mchillu": "\u0D54",
-    "/mlym:na": "\u0D28",
-    "/mlym:nchillu": "\u0D7B",
-    "/mlym:nga": "\u0D19",
-    "/mlym:nine": "\u0D6F",
-    "/mlym:nna": "\u0D23",
-    "/mlym:nnchillu": "\u0D7A",
-    "/mlym:nnna": "\u0D29",
-    "/mlym:nya": "\u0D1E",
-    "/mlym:o": "\u0D12",
-    "/mlym:one": "\u0D67",
-    "/mlym:oneeighth": "\u0D77",
-    "/mlym:onefifth": "\u0D5E",
-    "/mlym:onefortieth": "\u0D59",
-    "/mlym:onehalf": "\u0D74",
-    "/mlym:onehundred": "\u0D71",
-    "/mlym:oneone-hundred-and-sixtieth": "\u0D58",
-    "/mlym:onequarter": "\u0D73",
-    "/mlym:onesixteenth": "\u0D76",
-    "/mlym:onetenth": "\u0D5C",
-    "/mlym:onethousand": "\u0D72",
-    "/mlym:onetwentieth": "\u0D5B",
-    "/mlym:oo": "\u0D13",
-    "/mlym:oosign": "\u0D4B",
-    "/mlym:osign": "\u0D4A",
-    "/mlym:pa": "\u0D2A",
-    "/mlym:parasign": "\u0D4F",
-    "/mlym:pha": "\u0D2B",
-    "/mlym:ra": "\u0D30",
-    "/mlym:rra": "\u0D31",
-    "/mlym:rrchillu": "\u0D7C",
-    "/mlym:rrvocal": "\u0D60",
-    "/mlym:rrvocalsign": "\u0D44",
-    "/mlym:rvocal": "\u0D0B",
-    "/mlym:rvocalsign": "\u0D43",
-    "/mlym:sa": "\u0D38",
-    "/mlym:seven": "\u0D6D",
-    "/mlym:sha": "\u0D36",
-    "/mlym:six": "\u0D6C",
-    "/mlym:ssa": "\u0D37",
-    "/mlym:ta": "\u0D24",
-    "/mlym:ten": "\u0D70",
-    "/mlym:tha": "\u0D25",
-    "/mlym:three": "\u0D69",
-    "/mlym:threeeightieths": "\u0D5A",
-    "/mlym:threequarters": "\u0D75",
-    "/mlym:threesixteenths": "\u0D78",
-    "/mlym:threetwentieths": "\u0D5D",
-    "/mlym:tta": "\u0D1F",
-    "/mlym:ttha": "\u0D20",
-    "/mlym:ttta": "\u0D3A",
-    "/mlym:two": "\u0D68",
-    "/mlym:u": "\u0D09",
-    "/mlym:usign": "\u0D41",
-    "/mlym:uu": "\u0D0A",
-    "/mlym:uusign": "\u0D42",
-    "/mlym:va": "\u0D35",
-    "/mlym:verticalbarviramasign": "\u0D3B",
-    "/mlym:viramasign": "\u0D4D",
-    "/mlym:visargasign": "\u0D03",
-    "/mlym:ya": "\u0D2F",
-    "/mlym:ychillu": "\u0D55",
-    "/mlym:zero": "\u0D66",
-    "/mm2fullwidth": "\u339F",
-    "/mm3fullwidth": "\u33A3",
-    "/mmcubedsquare": "\u33A3",
-    "/mmfullwidth": "\u339C",
-    "/mmonospace": "\uFF4D",
-    "/mmsquaredsquare": "\u339F",
-    "/mobilePhone": "\u1F4F1",
-    "/mobilePhoneOff": "\u1F4F4",
-    "/mobilePhoneRightwardsArrowAtLeft": "\u1F4F2",
-    "/mocirclekatakana": "\u32F2",
-    "/models": "\u22A7",
-    "/mohiragana": "\u3082",
-    "/mohmfullwidth": "\u33C1",
-    "/mohmsquare": "\u33C1",
-    "/mokatakana": "\u30E2",
-    "/mokatakanahalfwidth": "\uFF93",
-    "/molfullwidth": "\u33D6",
-    "/molsquare": "\u33D6",
-    "/momathai": "\u0E21",
-    "/moneyBag": "\u1F4B0",
-    "/moneyWings": "\u1F4B8",
-    "/mong:a": "\u1820",
-    "/mong:aaligali": "\u1887",
-    "/mong:ahaligali": "\u1897",
-    "/mong:ang": "\u1829",
-    "/mong:angsibe": "\u1862",
-    "/mong:angtodo": "\u184A",
-    "/mong:anusvaraonealigali": "\u1880",
-    "/mong:ba": "\u182A",
-    "/mong:baludaaligali": "\u1885",
-    "/mong:baludaaligalithree": "\u1886",
-    "/mong:batodo": "\u184B",
-    "/mong:bhamanchualigali": "\u18A8",
-    "/mong:birga": "\u1800",
-    "/mong:caaligali": "\u188B",
-    "/mong:camanchualigali": "\u189C",
-    "/mong:cha": "\u1834",
-    "/mong:chasibe": "\u1871",
-    "/mong:chatodo": "\u1852",
-    "/mong:chi": "\u1842",
-    "/mong:colon": "\u1804",
-    "/mong:comma": "\u1802",
-    "/mong:commamanchu": "\u1808",
-    "/mong:cyamanchualigali": "\u18A3",
-    "/mong:da": "\u1833",
-    "/mong:daaligali": "\u1891",
-    "/mong:dagalgaaligali": "\u18A9",
-    "/mong:damarualigali": "\u1882",
-    "/mong:dasibe": "\u1869",
-    "/mong:datodo": "\u1851",
-    "/mong:ddaaligali": "\u188E",
-    "/mong:ddhamanchualigali": "\u189F",
-    "/mong:dhamanchualigali": "\u18A1",
-    "/mong:dzatodo": "\u185C",
-    "/mong:e": "\u1821",
-    "/mong:ee": "\u1827",
-    "/mong:eight": "\u1818",
-    "/mong:ellipsis": "\u1801",
-    "/mong:esibe": "\u185D",
-    "/mong:etodo": "\u1844",
-    "/mong:fa": "\u1839",
-    "/mong:famanchu": "\u1876",
-    "/mong:fasibe": "\u186B",
-    "/mong:five": "\u1815",
-    "/mong:four": "\u1814",
-    "/mong:fourdots": "\u1805",
-    "/mong:freevariationselectorone": "\u180B",
-    "/mong:freevariationselectorthree": "\u180D",
-    "/mong:freevariationselectortwo": "\u180C",
-    "/mong:ga": "\u182D",
-    "/mong:gaasibe": "\u186C",
-    "/mong:gaatodo": "\u1858",
-    "/mong:gasibe": "\u1864",
-    "/mong:gatodo": "\u184E",
-    "/mong:ghamanchualigali": "\u189A",
-    "/mong:haa": "\u183E",
-    "/mong:haasibe": "\u186D",
-    "/mong:haatodo": "\u1859",
-    "/mong:hasibe": "\u1865",
-    "/mong:i": "\u1822",
-    "/mong:ialigali": "\u1888",
-    "/mong:imanchu": "\u1873",
-    "/mong:isibe": "\u185E",
-    "/mong:itodo": "\u1845",
-    "/mong:iysibe": "\u185F",
-    "/mong:ja": "\u1835",
-    "/mong:jasibe": "\u186A",
-    "/mong:jatodo": "\u1853",
-    "/mong:jhamanchualigali": "\u189D",
-    "/mong:jiatodo": "\u185A",
-    "/mong:ka": "\u183A",
-    "/mong:kaaligali": "\u1889",
-    "/mong:kamanchu": "\u1874",
-    "/mong:kasibe": "\u1863",
-    "/mong:katodo": "\u1857",
-    "/mong:kha": "\u183B",
-    "/mong:la": "\u182F",
-    "/mong:lha": "\u1840",
-    "/mong:lhamanchualigali": "\u18AA",
-    "/mong:longvowelsigntodo": "\u1843",
-    "/mong:ma": "\u182E",
-    "/mong:matodo": "\u184F",
-    "/mong:na": "\u1828",
-    "/mong:ngaaligali": "\u188A",
-    "/mong:ngamanchualigali": "\u189B",
-    "/mong:niatodo": "\u185B",
-    "/mong:nine": "\u1819",
-    "/mong:nirugu": "\u180A",
-    "/mong:nnaaligali": "\u188F",
-    "/mong:o": "\u1823",
-    "/mong:oe": "\u1825",
-    "/mong:oetodo": "\u1848",
-    "/mong:one": "\u1811",
-    "/mong:otodo": "\u1846",
-    "/mong:pa": "\u182B",
-    "/mong:paaligali": "\u1892",
-    "/mong:pasibe": "\u1866",
-    "/mong:patodo": "\u184C",
-    "/mong:period": "\u1803",
-    "/mong:periodmanchu": "\u1809",
-    "/mong:phaaligali": "\u1893",
-    "/mong:qa": "\u182C",
-    "/mong:qatodo": "\u184D",
-    "/mong:ra": "\u1837",
-    "/mong:raasibe": "\u1870",
-    "/mong:ramanchu": "\u1875",
-    "/mong:sa": "\u1830",
-    "/mong:seven": "\u1817",
-    "/mong:sha": "\u1831",
-    "/mong:shasibe": "\u1867",
-    "/mong:six": "\u1816",
-    "/mong:softhyphentodo": "\u1806",
-    "/mong:ssaaligali": "\u1894",
-    "/mong:ssamanchualigali": "\u18A2",
-    "/mong:syllableboundarymarkersibe": "\u1807",
-    "/mong:ta": "\u1832",
-    "/mong:taaligali": "\u1890",
-    "/mong:tamanchualigali": "\u18A0",
-    "/mong:tasibe": "\u1868",
-    "/mong:tatodo": "\u1850",
-    "/mong:tatodoaligali": "\u1898",
-    "/mong:three": "\u1813",
-    "/mong:tsa": "\u183C",
-    "/mong:tsasibe": "\u186E",
-    "/mong:tsatodo": "\u1854",
-    "/mong:ttaaligali": "\u188C",
-    "/mong:ttamanchualigali": "\u189E",
-    "/mong:tthaaligali": "\u188D",
-    "/mong:two": "\u1812",
-    "/mong:u": "\u1824",
-    "/mong:ualigalihalf": "\u18A6",
-    "/mong:ubadamaaligali": "\u1883",
-    "/mong:ubadamaaligaliinverted": "\u1884",
-    "/mong:ue": "\u1826",
-    "/mong:uesibe": "\u1860",
-    "/mong:uetodo": "\u1849",
-    "/mong:usibe": "\u1861",
-    "/mong:utodo": "\u1847",
-    "/mong:visargaonealigali": "\u1881",
-    "/mong:vowelseparator": "\u180E",
-    "/mong:wa": "\u1838",
-    "/mong:watodo": "\u1856",
-    "/mong:ya": "\u1836",
-    "/mong:yaaligalihalf": "\u18A7",
-    "/mong:yatodo": "\u1855",
-    "/mong:za": "\u183D",
-    "/mong:zaaligali": "\u1896",
-    "/mong:zamanchualigali": "\u18A5",
-    "/mong:zasibe": "\u186F",
-    "/mong:zero": "\u1810",
-    "/mong:zhaaligali": "\u1895",
-    "/mong:zhamanchu": "\u1877",
-    "/mong:zhamanchualigali": "\u18A4",
-    "/mong:zhasibe": "\u1872",
-    "/mong:zhatodoaligali": "\u1899",
-    "/mong:zhi": "\u1841",
-    "/mong:zra": "\u183F",
-    "/monkey": "\u1F412",
-    "/monkeyFace": "\u1F435",
-    "/monogramyang": "\u268A",
-    "/monogramyin": "\u268B",
-    "/monorail": "\u1F69D",
-    "/monostable": "\u238D",
-    "/moodBubble": "\u1F5F0",
-    "/moonViewingCeremony": "\u1F391",
-    "/moonideographiccircled": "\u328A",
-    "/moonideographicparen": "\u322A",
-    "/moonlilithblack": "\u26B8",
-    "/mosque": "\u1F54C",
-    "/motorBoat": "\u1F6E5",
-    "/motorScooter": "\u1F6F5",
-    "/motorway": "\u1F6E3",
-    "/mountFuji": "\u1F5FB",
-    "/mountain": "\u26F0",
-    "/mountainBicyclist": "\u1F6B5",
-    "/mountainCableway": "\u1F6A0",
-    "/mountainRailway": "\u1F69E",
-    "/mouse": "\u1F401",
-    "/mouseFace": "\u1F42D",
-    "/mouth": "\u1F444",
-    "/movers2fullwidth": "\u33A8",
-    "/moversfullwidth": "\u33A7",
-    "/moverssquare": "\u33A7",
-    "/moverssquaredsquare": "\u33A8",
-    "/movieCamera": "\u1F3A5",
-    "/moyai": "\u1F5FF",
-    "/mpafullwidth": "\u33AB",
-    "/mparen": "\u24A8",
-    "/mparenthesized": "\u24A8",
-    "/mpasquare": "\u33AB",
-    "/msfullwidth": "\u33B3",
-    "/mssquare": "\u33B3",
-    "/msuperior": "\uF6EF",
-    "/mturned": "\u026F",
-    "/mu": "\u00B5",
-    "/mu.math": "\u00B5",
-    "/mu1": "\u00B5",
-    "/muafullwidth": "\u3382",
-    "/muasquare": "\u3382",
-    "/muchgreater": "\u226B",
-    "/muchless": "\u226A",
-    "/mucirclekatakana": "\u32F0",
-    "/muffullwidth": "\u338C",
-    "/mufsquare": "\u338C",
-    "/mugfullwidth": "\u338D",
-    "/mugreek": "\u03BC",
-    "/mugsquare": "\u338D",
-    "/muhiragana": "\u3080",
-    "/mukatakana": "\u30E0",
-    "/mukatakanahalfwidth": "\uFF91",
-    "/mulfullwidth": "\u3395",
-    "/mulsquare": "\u3395",
-    "/multimap": "\u22B8",
-    "/multimapleft": "\u27DC",
-    "/multipleMusicalNotes": "\u1F3B6",
-    "/multiply": "\u00D7",
-    "/multiset": "\u228C",
-    "/multisetmultiplication": "\u228D",
-    "/multisetunion": "\u228E",
-    "/mum": "\uA773",
-    "/mumfullwidth": "\u339B",
-    "/mumsquare": "\u339B",
-    "/munach:hb": "\u05A3",
-    "/munahhebrew": "\u05A3",
-    "/munahlefthebrew": "\u05A3",
-    "/musfullwidth": "\u33B2",
-    "/mushroom": "\u1F344",
-    "/musicalKeyboard": "\u1F3B9",
-    "/musicalKeyboardJacks": "\u1F398",
-    "/musicalNote": "\u1F3B5",
-    "/musicalScore": "\u1F3BC",
-    "/musicalnote": "\u266A",
-    "/musicalnotedbl": "\u266B",
-    "/musicflat": "\u266D",
-    "/musicflatsign": "\u266D",
-    "/musicnatural": "\u266E",
-    "/musicsharp": "\u266F",
-    "/musicsharpsign": "\u266F",
-    "/mussquare": "\u33B2",
-    "/muvfullwidth": "\u33B6",
-    "/muvsquare": "\u33B6",
-    "/muwfullwidth": "\u33BC",
-    "/muwsquare": "\u33BC",
-    "/mvfullwidth": "\u33B7",
-    "/mvmegafullwidth": "\u33B9",
-    "/mvmegasquare": "\u33B9",
-    "/mvsquare": "\u33B7",
-    "/mwfullwidth": "\u33BD",
-    "/mwmegafullwidth": "\u33BF",
-    "/mwmegasquare": "\u33BF",
-    "/mwsquare": "\u33BD",
-    "/n": "\u006E",
-    "/n.inferior": "\u2099",
-    "/n.superior": "\u207F",
-    "/nabengali": "\u09A8",
-    "/nabla": "\u2207",
-    "/nacirclekatakana": "\u32E4",
-    "/nacute": "\u0144",
-    "/nadeva": "\u0928",
-    "/nafullwidth": "\u3381",
-    "/nagujarati": "\u0AA8",
-    "/nagurmukhi": "\u0A28",
-    "/nahiragana": "\u306A",
-    "/nailPolish": "\u1F485",
-    "/naira": "\u20A6",
-    "/nakatakana": "\u30CA",
-    "/nakatakanahalfwidth": "\uFF85",
-    "/nameBadge": "\u1F4DB",
-    "/nameideographiccircled": "\u3294",
-    "/nameideographicparen": "\u3234",
-    "/namurda": "\uA99F",
-    "/nand": "\u22BC",
-    "/nanosquare": "\u3328",
-    "/napostrophe": "\u0149",
-    "/narrownobreakspace": "\u202F",
-    "/nasquare": "\u3381",
-    "/nationalPark": "\u1F3DE",
-    "/nationaldigitshapes": "\u206E",
-    "/nbopomofo": "\u310B",
-    "/nbspace": "\u00A0",
-    "/ncaron": "\u0148",
-    "/ncedilla": "\u0146",
-    "/ncircle": "\u24DD",
-    "/ncircumflexbelow": "\u1E4B",
-    "/ncommaaccent": "\u0146",
-    "/ncurl": "\u0235",
-    "/ndescender": "\uA791",
-    "/ndot": "\u1E45",
-    "/ndotaccent": "\u1E45",
-    "/ndotbelow": "\u1E47",
-    "/necirclekatakana": "\u32E7",
-    "/necktie": "\u1F454",
-    "/negatedturnstiledblverticalbarright": "\u22AF",
-    "/nehiragana": "\u306D",
-    "/neirapproximatelynoractuallyequal": "\u2247",
-    "/neirasersetnorequalup": "\u2289",
-    "/neirasubsetnorequal": "\u2288",
-    "/neirgreaternorequal": "\u2271",
-    "/neirgreaternorequivalent": "\u2275",
-    "/neirgreaternorless": "\u2279",
-    "/neirlessnorequal": "\u2270",
-    "/neirlessnorequivalent": "\u2274",
-    "/neirlessnorgreater": "\u2278",
-    "/nekatakana": "\u30CD",
-    "/nekatakanahalfwidth": "\uFF88",
-    "/neptune": "\u2646",
-    "/neuter": "\u26B2",
-    "/neutralFace": "\u1F610",
-    "/newMoon": "\u1F311",
-    "/newMoonFace": "\u1F31A",
-    "/newsheqel": "\u20AA",
-    "/newsheqelsign": "\u20AA",
-    "/newspaper": "\u1F4F0",
-    "/newsquare": "\u1F195",
-    "/nextpage": "\u2398",
-    "/nffullwidth": "\u338B",
-    "/nfsquare": "\u338B",
-    "/ng.fina": "\uFBD4",
-    "/ng.init": "\uFBD5",
-    "/ng.isol": "\uFBD3",
-    "/ng.medi": "\uFBD6",
-    "/ngabengali": "\u0999",
-    "/ngadeva": "\u0919",
-    "/ngagujarati": "\u0A99",
-    "/ngagurmukhi": "\u0A19",
-    "/ngalelet": "\uA98A",
-    "/ngaleletraswadi": "\uA98B",
-    "/ngoeh": "\u06B1",
-    "/ngoeh.fina": "\uFB9B",
-    "/ngoeh.init": "\uFB9C",
-    "/ngoeh.isol": "\uFB9A",
-    "/ngoeh.medi": "\uFB9D",
-    "/ngonguthai": "\u0E07",
-    "/ngrave": "\u01F9",
-    "/ngsquare": "\u1F196",
-    "/nhiragana": "\u3093",
-    "/nhookleft": "\u0272",
-    "/nhookretroflex": "\u0273",
-    "/nicirclekatakana": "\u32E5",
-    "/nieunacirclekorean": "\u326F",
-    "/nieunaparenkorean": "\u320F",
-    "/nieuncieuckorean": "\u3135",
-    "/nieuncirclekorean": "\u3261",
-    "/nieunhieuhkorean": "\u3136",
-    "/nieunkorean": "\u3134",
-    "/nieunpansioskorean": "\u3168",
-    "/nieunparenkorean": "\u3201",
-    "/nieunsioskorean": "\u3167",
-    "/nieuntikeutkorean": "\u3166",
-    "/nightStars": "\u1F303",
-    "/nightideographiccircled": "\u32B0",
-    "/nihiragana": "\u306B",
-    "/nikatakana": "\u30CB",
-    "/nikatakanahalfwidth": "\uFF86",
-    "/nikhahitleftthai": "\uF899",
-    "/nikhahitthai": "\u0E4D",
-    "/nine": "\u0039",
-    "/nine.inferior": "\u2089",
-    "/nine.roman": "\u2168",
-    "/nine.romansmall": "\u2178",
-    "/nine.superior": "\u2079",
-    "/ninearabic": "\u0669",
-    "/ninebengali": "\u09EF",
-    "/ninecircle": "\u2468",
-    "/ninecircledbl": "\u24FD",
-    "/ninecircleinversesansserif": "\u2792",
-    "/ninecomma": "\u1F10A",
-    "/ninedeva": "\u096F",
-    "/ninefar": "\u06F9",
-    "/ninegujarati": "\u0AEF",
-    "/ninegurmukhi": "\u0A6F",
-    "/ninehackarabic": "\u0669",
-    "/ninehangzhou": "\u3029",
-    "/nineideographiccircled": "\u3288",
-    "/nineideographicparen": "\u3228",
-    "/nineinferior": "\u2089",
-    "/ninemonospace": "\uFF19",
-    "/nineoldstyle": "\uF739",
-    "/nineparen": "\u247C",
-    "/nineparenthesized": "\u247C",
-    "/nineperiod": "\u2490",
-    "/ninepersian": "\u06F9",
-    "/nineroman": "\u2178",
-    "/ninesuperior": "\u2079",
-    "/nineteencircle": "\u2472",
-    "/nineteencircleblack": "\u24F3",
-    "/nineteenparen": "\u2486",
-    "/nineteenparenthesized": "\u2486",
-    "/nineteenperiod": "\u249A",
-    "/ninethai": "\u0E59",
-    "/nj": "\u01CC",
-    "/njecyr": "\u045A",
-    "/njecyrillic": "\u045A",
-    "/njekomicyr": "\u050B",
-    "/nkatakana": "\u30F3",
-    "/nkatakanahalfwidth": "\uFF9D",
-    "/nlegrightlong": "\u019E",
-    "/nlinebelow": "\u1E49",
-    "/nlongrightleg": "\u019E",
-    "/nmbr:oneeighth": "\u215B",
-    "/nmbr:onefifth": "\u2155",
-    "/nmbr:onetenth": "\u2152",
-    "/nmfullwidth": "\u339A",
-    "/nmonospace": "\uFF4E",
-    "/nmsquare": "\u339A",
-    "/nnabengali": "\u09A3",
-    "/nnadeva": "\u0923",
-    "/nnagujarati": "\u0AA3",
-    "/nnagurmukhi": "\u0A23",
-    "/nnnadeva": "\u0929",
-    "/noBicycles": "\u1F6B3",
-    "/noEntrySign": "\u1F6AB",
-    "/noMobilePhones": "\u1F4F5",
-    "/noOneUnderEighteen": "\u1F51E",
-    "/noPedestrians": "\u1F6B7",
-    "/noPiracy": "\u1F572",
-    "/noSmoking": "\u1F6AD",
-    "/nobliquestroke": "\uA7A5",
-    "/nocirclekatakana": "\u32E8",
-    "/nodeascending": "\u260A",
-    "/nodedescending": "\u260B",
-    "/noentry": "\u26D4",
-    "/nohiragana": "\u306E",
-    "/nokatakana": "\u30CE",
-    "/nokatakanahalfwidth": "\uFF89",
-    "/nominaldigitshapes": "\u206F",
-    "/nonPotableWater": "\u1F6B1",
-    "/nonbreakinghyphen": "\u2011",
-    "/nonbreakingspace": "\u00A0",
-    "/nonenthai": "\u0E13",
-    "/nonuthai": "\u0E19",
-    "/noon": "\u0646",
-    "/noon.fina": "\uFEE6",
-    "/noon.init": "\uFEE7",
-    "/noon.init_alefmaksura.fina": "\uFC4F",
-    "/noon.init_hah.fina": "\uFC4C",
-    "/noon.init_hah.medi": "\uFCD3",
-    "/noon.init_hah.medi_meem.medi": "\uFD95",
-    "/noon.init_heh.medi": "\uFCD6",
-    "/noon.init_jeem.fina": "\uFC4B",
-    "/noon.init_jeem.medi": "\uFCD2",
-    "/noon.init_jeem.medi_hah.medi": "\uFDB8",
-    "/noon.init_jeem.medi_meem.medi": "\uFD98",
-    "/noon.init_khah.fina": "\uFC4D",
-    "/noon.init_khah.medi": "\uFCD4",
-    "/noon.init_meem.fina": "\uFC4E",
-    "/noon.init_meem.medi": "\uFCD5",
-    "/noon.init_yeh.fina": "\uFC50",
-    "/noon.isol": "\uFEE5",
-    "/noon.medi": "\uFEE8",
-    "/noon.medi_alefmaksura.fina": "\uFC8E",
-    "/noon.medi_hah.medi_alefmaksura.fina": "\uFD96",
-    "/noon.medi_hah.medi_yeh.fina": "\uFDB3",
-    "/noon.medi_heh.medi": "\uFCEF",
-    "/noon.medi_jeem.medi_alefmaksura.fina": "\uFD99",
-    "/noon.medi_jeem.medi_hah.fina": "\uFDBD",
-    "/noon.medi_jeem.medi_meem.fina": "\uFD97",
-    "/noon.medi_jeem.medi_yeh.fina": "\uFDC7",
-    "/noon.medi_meem.fina": "\uFC8C",
-    "/noon.medi_meem.medi": "\uFCEE",
-    "/noon.medi_meem.medi_alefmaksura.fina": "\uFD9B",
-    "/noon.medi_meem.medi_yeh.fina": "\uFD9A",
-    "/noon.medi_noon.fina": "\uFC8D",
-    "/noon.medi_reh.fina": "\uFC8A",
-    "/noon.medi_yeh.fina": "\uFC8F",
-    "/noon.medi_zain.fina": "\uFC8B",
-    "/noonSmallTah": "\u0768",
-    "/noonSmallV": "\u0769",
-    "/noonTwoDotsBelow": "\u0767",
-    "/noonabove": "\u06E8",
-    "/noonarabic": "\u0646",
-    "/noondotbelow": "\u06B9",
-    "/noonfinalarabic": "\uFEE6",
-    "/noonghunna": "\u06BA",
-    "/noonghunna.fina": "\uFB9F",
-    "/noonghunna.isol": "\uFB9E",
-    "/noonghunnaarabic": "\u06BA",
-    "/noonghunnafinalarabic": "\uFB9F",
-    "/noonhehinitialarabic": "\uFEE7",
-    "/nooninitialarabic": "\uFEE7",
-    "/noonjeeminitialarabic": "\uFCD2",
-    "/noonjeemisolatedarabic": "\uFC4B",
-    "/noonmedialarabic": "\uFEE8",
-    "/noonmeeminitialarabic": "\uFCD5",
-    "/noonmeemisolatedarabic": "\uFC4E",
-    "/noonnoonfinalarabic": "\uFC8D",
-    "/noonring": "\u06BC",
-    "/noonthreedotsabove": "\u06BD",
-    "/nor": "\u22BD",
-    "/nordicmark": "\u20BB",
-    "/normalfacrsemidirectproductleft": "\u22C9",
-    "/normalfacrsemidirectproductright": "\u22CA",
-    "/normalsubgroorequalup": "\u22B4",
-    "/normalsubgroup": "\u22B2",
-    "/northeastPointingAirplane": "\u1F6EA",
-    "/nose": "\u1F443",
-    "/notalmostequal": "\u2249",
-    "/notasersetup": "\u2285",
-    "/notasympticallyequal": "\u2244",
-    "/notcheckmark": "\u237B",
-    "/notchedLeftSemicircleThreeDots": "\u1F543",
-    "/notchedRightSemicircleThreeDots": "\u1F544",
-    "/notcontains": "\u220C",
-    "/note": "\u1F5C8",
-    "/notePad": "\u1F5CA",
-    "/notePage": "\u1F5C9",
-    "/notebook": "\u1F4D3",
-    "/notebookDecorativeCover": "\u1F4D4",
-    "/notelement": "\u2209",
-    "/notelementof": "\u2209",
-    "/notequal": "\u2260",
-    "/notequivalent": "\u226D",
-    "/notexistential": "\u2204",
-    "/notgreater": "\u226F",
-    "/notgreaternorequal": "\u2271",
-    "/notgreaternorless": "\u2279",
-    "/notidentical": "\u2262",
-    "/notless": "\u226E",
-    "/notlessnorequal": "\u2270",
-    "/notnormalsubgroorequalup": "\u22EC",
-    "/notnormalsubgroup": "\u22EA",
-    "/notparallel": "\u2226",
-    "/notprecedes": "\u2280",
-    "/notsignturned": "\u2319",
-    "/notsquareimageorequal": "\u22E2",
-    "/notsquareoriginalorequal": "\u22E3",
-    "/notsubset": "\u2284",
-    "/notsucceeds": "\u2281",
-    "/notsuperset": "\u2285",
-    "/nottilde": "\u2241",
-    "/nottosquare": "\u3329",
-    "/nottrue": "\u22AD",
-    "/novembertelegraph": "\u32CA",
-    "/nowarmenian": "\u0576",
-    "/nparen": "\u24A9",
-    "/nparenthesized": "\u24A9",
-    "/nretroflex": "\u0273",
-    "/nsfullwidth": "\u33B1",
-    "/nssquare": "\u33B1",
-    "/nsuperior": "\u207F",
-    "/ntilde": "\u00F1",
-    "/nu": "\u03BD",
-    "/nucirclekatakana": "\u32E6",
-    "/nuhiragana": "\u306C",
-    "/nukatakana": "\u30CC",
-    "/nukatakanahalfwidth": "\uFF87",
-    "/nuktabengali": "\u09BC",
-    "/nuktadeva": "\u093C",
-    "/nuktagujarati": "\u0ABC",
-    "/nuktagurmukhi": "\u0A3C",
-    "/num": "\uA774",
-    "/numbermarkabove": "\u0605",
-    "/numbersign": "\u0023",
-    "/numbersignmonospace": "\uFF03",
-    "/numbersignsmall": "\uFE5F",
-    "/numeralsign": "\u0374",
-    "/numeralsigngreek": "\u0374",
-    "/numeralsignlowergreek": "\u0375",
-    "/numero": "\u2116",
-    "/nun": "\u05E0",
-    "/nun:hb": "\u05E0",
-    "/nunHafukha:hb": "\u05C6",
-    "/nundagesh": "\uFB40",
-    "/nundageshhebrew": "\uFB40",
-    "/nunhebrew": "\u05E0",
-    "/nunwithdagesh:hb": "\uFB40",
-    "/nutAndBolt": "\u1F529",
-    "/nvfullwidth": "\u33B5",
-    "/nvsquare": "\u33B5",
-    "/nwfullwidth": "\u33BB",
-    "/nwsquare": "\u33BB",
-    "/nyabengali": "\u099E",
-    "/nyadeva": "\u091E",
-    "/nyagujarati": "\u0A9E",
-    "/nyagurmukhi": "\u0A1E",
-    "/nyamurda": "\uA998",
-    "/nyeh": "\u0683",
-    "/nyeh.fina": "\uFB77",
-    "/nyeh.init": "\uFB78",
-    "/nyeh.isol": "\uFB76",
-    "/nyeh.medi": "\uFB79",
-    "/o": "\u006F",
-    "/o.inferior": "\u2092",
-    "/oacute": "\u00F3",
-    "/oangthai": "\u0E2D",
-    "/obarcyr": "\u04E9",
-    "/obardieresiscyr": "\u04EB",
-    "/obarred": "\u0275",
-    "/obarredcyrillic": "\u04E9",
-    "/obarreddieresiscyrillic": "\u04EB",
-    "/obelosdotted": "\u2E13",
-    "/obengali": "\u0993",
-    "/obopomofo": "\u311B",
-    "/obreve": "\u014F",
-    "/observereye": "\u23FF",
-    "/ocandradeva": "\u0911",
-    "/ocandragujarati": "\u0A91",
-    "/ocandravowelsigndeva": "\u0949",
-    "/ocandravowelsigngujarati": "\u0AC9",
-    "/ocaron": "\u01D2",
-    "/ocircle": "\u24DE",
-    "/ocirclekatakana": "\u32D4",
-    "/ocircumflex": "\u00F4",
-    "/ocircumflexacute": "\u1ED1",
-    "/ocircumflexdotbelow": "\u1ED9",
-    "/ocircumflexgrave": "\u1ED3",
-    "/ocircumflexhoi": "\u1ED5",
-    "/ocircumflexhookabove": "\u1ED5",
-    "/ocircumflextilde": "\u1ED7",
-    "/ocr:bowtie": "\u2445",
-    "/ocr:dash": "\u2448",
-    "/octagonalSign": "\u1F6D1",
-    "/octobertelegraph": "\u32C9",
-    "/octopus": "\u1F419",
-    "/ocyr": "\u043E",
-    "/ocyrillic": "\u043E",
-    "/odblacute": "\u0151",
-    "/odblgrave": "\u020D",
-    "/oden": "\u1F362",
-    "/odeva": "\u0913",
-    "/odieresis": "\u00F6",
-    "/odieresiscyr": "\u04E7",
-    "/odieresiscyrillic": "\u04E7",
-    "/odieresismacron": "\u022B",
-    "/odot": "\u022F",
-    "/odotbelow": "\u1ECD",
-    "/odotmacron": "\u0231",
-    "/oe": "\u0153",
-    "/oe.fina": "\uFBDA",
-    "/oe.isol": "\uFBD9",
-    "/oekirghiz": "\u06C5",
-    "/oekirghiz.fina": "\uFBE1",
-    "/oekirghiz.isol": "\uFBE0",
-    "/oekorean": "\u315A",
-    "/officeBuilding": "\u1F3E2",
-    "/ogonek": "\u02DB",
-    "/ogonekcmb": "\u0328",
-    "/ograve": "\u00F2",
-    "/ogravedbl": "\u020D",
-    "/ogujarati": "\u0A93",
-    "/oharmenian": "\u0585",
-    "/ohiragana": "\u304A",
-    "/ohm": "\u2126",
-    "/ohminverted": "\u2127",
-    "/ohoi": "\u1ECF",
-    "/ohookabove": "\u1ECF",
-    "/ohorn": "\u01A1",
-    "/ohornacute": "\u1EDB",
-    "/ohorndotbelow": "\u1EE3",
-    "/ohorngrave": "\u1EDD",
-    "/ohornhoi": "\u1EDF",
-    "/ohornhookabove": "\u1EDF",
-    "/ohorntilde": "\u1EE1",
-    "/ohungarumlaut": "\u0151",
-    "/ohuparen": "\u321E",
-    "/oi": "\u01A3",
-    "/oilDrum": "\u1F6E2",
-    "/oinvertedbreve": "\u020F",
-    "/ojeonparen": "\u321D",
-    "/okHandSign": "\u1F44C",
-    "/okatakana": "\u30AA",
-    "/okatakanahalfwidth": "\uFF75",
-    "/okorean": "\u3157",
-    "/oksquare": "\u1F197",
-    "/oldKey": "\u1F5DD",
-    "/oldPersonalComputer": "\u1F5B3",
-    "/olderMan": "\u1F474",
-    "/olderWoman": "\u1F475",
-    "/ole:hb": "\u05AB",
-    "/olehebrew": "\u05AB",
-    "/oloop": "\uA74D",
-    "/olowringinside": "\u2C7A",
-    "/omacron": "\u014D",
-    "/omacronacute": "\u1E53",
-    "/omacrongrave": "\u1E51",
-    "/omdeva": "\u0950",
-    "/omega": "\u03C9",
-    "/omega1": "\u03D6",
-    "/omegaacute": "\u1F7D",
-    "/omegaacuteiotasub": "\u1FF4",
-    "/omegaasper": "\u1F61",
-    "/omegaasperacute": "\u1F65",
-    "/omegaasperacuteiotasub": "\u1FA5",
-    "/omegaaspergrave": "\u1F63",
-    "/omegaaspergraveiotasub": "\u1FA3",
-    "/omegaasperiotasub": "\u1FA1",
-    "/omegaaspertilde": "\u1F67",
-    "/omegaaspertildeiotasub": "\u1FA7",
-    "/omegaclosed": "\u0277",
-    "/omegacyr": "\u0461",
-    "/omegacyrillic": "\u0461",
-    "/omegafunc": "\u2375",
-    "/omegagrave": "\u1F7C",
-    "/omegagraveiotasub": "\u1FF2",
-    "/omegaiotasub": "\u1FF3",
-    "/omegalatinclosed": "\u0277",
-    "/omegalenis": "\u1F60",
-    "/omegalenisacute": "\u1F64",
-    "/omegalenisacuteiotasub": "\u1FA4",
-    "/omegalenisgrave": "\u1F62",
-    "/omegalenisgraveiotasub": "\u1FA2",
-    "/omegalenisiotasub": "\u1FA0",
-    "/omegalenistilde": "\u1F66",
-    "/omegalenistildeiotasub": "\u1FA6",
-    "/omegaroundcyr": "\u047B",
-    "/omegaroundcyrillic": "\u047B",
-    "/omegatilde": "\u1FF6",
-    "/omegatildeiotasub": "\u1FF7",
-    "/omegatitlocyr": "\u047D",
-    "/omegatitlocyrillic": "\u047D",
-    "/omegatonos": "\u03CE",
-    "/omegaunderlinefunc": "\u2379",
-    "/omgujarati": "\u0AD0",
-    "/omicron": "\u03BF",
-    "/omicronacute": "\u1F79",
-    "/omicronasper": "\u1F41",
-    "/omicronasperacute": "\u1F45",
-    "/omicronaspergrave": "\u1F43",
-    "/omicrongrave": "\u1F78",
-    "/omicronlenis": "\u1F40",
-    "/omicronlenisacute": "\u1F44",
-    "/omicronlenisgrave": "\u1F42",
-    "/omicrontonos": "\u03CC",
-    "/omonospace": "\uFF4F",
-    "/onExclamationMarkLeftRightArrowAbove": "\u1F51B",
-    "/oncomingAutomobile": "\u1F698",
-    "/oncomingBus": "\u1F68D",
-    "/oncomingFireEngine": "\u1F6F1",
-    "/oncomingPoliceCar": "\u1F694",
-    "/oncomingTaxi": "\u1F696",
-    "/one": "\u0031",
-    "/one.inferior": "\u2081",
-    "/one.roman": "\u2160",
-    "/one.romansmall": "\u2170",
-    "/oneButtonMouse": "\u1F5AF",
-    "/onearabic": "\u0661",
-    "/onebengali": "\u09E7",
-    "/onecircle": "\u2460",
-    "/onecircledbl": "\u24F5",
-    "/onecircleinversesansserif": "\u278A",
-    "/onecomma": "\u1F102",
-    "/onedeva": "\u0967",
-    "/onedotenleader": "\u2024",
-    "/onedotovertwodots": "\u2E2B",
-    "/oneeighth": "\u215B",
-    "/onefar": "\u06F1",
-    "/onefitted": "\uF6DC",
-    "/onefraction": "\u215F",
-    "/onegujarati": "\u0AE7",
-    "/onegurmukhi": "\u0A67",
-    "/onehackarabic": "\u0661",
-    "/onehalf": "\u00BD",
-    "/onehangzhou": "\u3021",
-    "/onehundred.roman": "\u216D",
-    "/onehundred.romansmall": "\u217D",
-    "/onehundredthousand.roman": "\u2188",
-    "/onehundredtwentypsquare": "\u1F1A4",
-    "/oneideographiccircled": "\u3280",
-    "/oneideographicparen": "\u3220",
-    "/oneinferior": "\u2081",
-    "/onemonospace": "\uFF11",
-    "/oneninth": "\u2151",
-    "/onenumeratorbengali": "\u09F4",
-    "/oneoldstyle": "\uF731",
-    "/oneparen": "\u2474",
-    "/oneparenthesized": "\u2474",
-    "/oneperiod": "\u2488",
-    "/onepersian": "\u06F1",
-    "/onequarter": "\u00BC",
-    "/oneroman": "\u2170",
-    "/oneseventh": "\u2150",
-    "/onesixth": "\u2159",
-    "/onesuperior": "\u00B9",
-    "/onethai": "\u0E51",
-    "/onethird": "\u2153",
-    "/onethousand.roman": "\u216F",
-    "/onethousand.romansmall": "\u217F",
-    "/onethousandcd.roman": "\u2180",
-    "/onsusquare": "\u3309",
-    "/oo": "\uA74F",
-    "/oogonek": "\u01EB",
-    "/oogonekmacron": "\u01ED",
-    "/oogurmukhi": "\u0A13",
-    "/oomatragurmukhi": "\u0A4B",
-    "/oomusquare": "\u330A",
-    "/oopen": "\u0254",
-    "/oparen": "\u24AA",
-    "/oparenthesized": "\u24AA",
-    "/openBook": "\u1F4D6",
-    "/openFileFolder": "\u1F4C2",
-    "/openFolder": "\u1F5C1",
-    "/openHandsSign": "\u1F450",
-    "/openLock": "\u1F513",
-    "/openMailboxLoweredFlag": "\u1F4ED",
-    "/openMailboxRaisedFlag": "\u1F4EC",
-    "/openbullet": "\u25E6",
-    "/openheadarrowleft": "\u21FD",
-    "/openheadarrowleftright": "\u21FF",
-    "/openheadarrowright": "\u21FE",
-    "/opensubset": "\u27C3",
-    "/opensuperset": "\u27C4",
-    "/ophiuchus": "\u26CE",
-    "/opposition": "\u260D",
-    "/opticalDisc": "\u1F4BF",
-    "/opticalDiscIcon": "\u1F5B8",
-    "/option": "\u2325",
-    "/orangeBook": "\u1F4D9",
-    "/ordfeminine": "\u00AA",
-    "/ordmasculine": "\u00BA",
-    "/ordotinside": "\u27C7",
-    "/original": "\u22B6",
-    "/ornateleftparenthesis": "\uFD3E",
-    "/ornaterightparenthesis": "\uFD3F",
-    "/orthodoxcross": "\u2626",
-    "/orthogonal": "\u221F",
-    "/orya:a": "\u0B05",
-    "/orya:aa": "\u0B06",
-    "/orya:aasign": "\u0B3E",
-    "/orya:ai": "\u0B10",
-    "/orya:ailengthmark": "\u0B56",
-    "/orya:aisign": "\u0B48",
-    "/orya:anusvara": "\u0B02",
-    "/orya:au": "\u0B14",
-    "/orya:aulengthmark": "\u0B57",
-    "/orya:ausign": "\u0B4C",
-    "/orya:avagraha": "\u0B3D",
-    "/orya:ba": "\u0B2C",
-    "/orya:bha": "\u0B2D",
-    "/orya:ca": "\u0B1A",
-    "/orya:candrabindu": "\u0B01",
-    "/orya:cha": "\u0B1B",
-    "/orya:da": "\u0B26",
-    "/orya:dda": "\u0B21",
-    "/orya:ddha": "\u0B22",
-    "/orya:dha": "\u0B27",
-    "/orya:e": "\u0B0F",
-    "/orya:eight": "\u0B6E",
-    "/orya:esign": "\u0B47",
-    "/orya:five": "\u0B6B",
-    "/orya:four": "\u0B6A",
-    "/orya:fractiononeeighth": "\u0B76",
-    "/orya:fractiononehalf": "\u0B73",
-    "/orya:fractiononequarter": "\u0B72",
-    "/orya:fractiononesixteenth": "\u0B75",
-    "/orya:fractionthreequarters": "\u0B74",
-    "/orya:fractionthreesixteenths": "\u0B77",
-    "/orya:ga": "\u0B17",
-    "/orya:gha": "\u0B18",
-    "/orya:ha": "\u0B39",
-    "/orya:i": "\u0B07",
-    "/orya:ii": "\u0B08",
-    "/orya:iisign": "\u0B40",
-    "/orya:isign": "\u0B3F",
-    "/orya:isshar": "\u0B70",
-    "/orya:ja": "\u0B1C",
-    "/orya:jha": "\u0B1D",
-    "/orya:ka": "\u0B15",
-    "/orya:kha": "\u0B16",
-    "/orya:la": "\u0B32",
-    "/orya:lla": "\u0B33",
-    "/orya:llvocal": "\u0B61",
-    "/orya:llvocalsign": "\u0B63",
-    "/orya:lvocal": "\u0B0C",
-    "/orya:lvocalsign": "\u0B62",
-    "/orya:ma": "\u0B2E",
-    "/orya:na": "\u0B28",
-    "/orya:nga": "\u0B19",
-    "/orya:nine": "\u0B6F",
-    "/orya:nna": "\u0B23",
-    "/orya:nukta": "\u0B3C",
-    "/orya:nya": "\u0B1E",
-    "/orya:o": "\u0B13",
-    "/orya:one": "\u0B67",
-    "/orya:osign": "\u0B4B",
-    "/orya:pa": "\u0B2A",
-    "/orya:pha": "\u0B2B",
-    "/orya:ra": "\u0B30",
-    "/orya:rha": "\u0B5D",
-    "/orya:rra": "\u0B5C",
-    "/orya:rrvocal": "\u0B60",
-    "/orya:rrvocalsign": "\u0B44",
-    "/orya:rvocal": "\u0B0B",
-    "/orya:rvocalsign": "\u0B43",
-    "/orya:sa": "\u0B38",
-    "/orya:seven": "\u0B6D",
-    "/orya:sha": "\u0B36",
-    "/orya:six": "\u0B6C",
-    "/orya:ssa": "\u0B37",
-    "/orya:ta": "\u0B24",
-    "/orya:tha": "\u0B25",
-    "/orya:three": "\u0B69",
-    "/orya:tta": "\u0B1F",
-    "/orya:ttha": "\u0B20",
-    "/orya:two": "\u0B68",
-    "/orya:u": "\u0B09",
-    "/orya:usign": "\u0B41",
-    "/orya:uu": "\u0B0A",
-    "/orya:uusign": "\u0B42",
-    "/orya:va": "\u0B35",
-    "/orya:virama": "\u0B4D",
-    "/orya:visarga": "\u0B03",
-    "/orya:wa": "\u0B71",
-    "/orya:ya": "\u0B2F",
-    "/orya:yya": "\u0B5F",
-    "/orya:zero": "\u0B66",
-    "/oscript": "\u2134",
-    "/oshortdeva": "\u0912",
-    "/oshortvowelsigndeva": "\u094A",
-    "/oslash": "\u00F8",
-    "/oslashacute": "\u01FF",
-    "/osmallhiragana": "\u3049",
-    "/osmallkatakana": "\u30A9",
-    "/osmallkatakanahalfwidth": "\uFF6B",
-    "/ostroke": "\uA74B",
-    "/ostrokeacute": "\u01FF",
-    "/osuperior": "\uF6F0",
-    "/otcyr": "\u047F",
-    "/otcyrillic": "\u047F",
-    "/otilde": "\u00F5",
-    "/otildeacute": "\u1E4D",
-    "/otildedieresis": "\u1E4F",
-    "/otildemacron": "\u022D",
-    "/ou": "\u0223",
-    "/oubopomofo": "\u3121",
-    "/ounce": "\u2125",
-    "/outboxTray": "\u1F4E4",
-    "/outerjoinfull": "\u27D7",
-    "/outerjoinleft": "\u27D5",
-    "/outerjoinright": "\u27D6",
-    "/outputpassiveup": "\u2392",
-    "/overlap": "\u1F5D7",
-    "/overline": "\u203E",
-    "/overlinecenterline": "\uFE4A",
-    "/overlinecmb": "\u0305",
-    "/overlinedashed": "\uFE49",
-    "/overlinedblwavy": "\uFE4C",
-    "/overlinewavy": "\uFE4B",
-    "/overscore": "\u00AF",
-    "/ovfullwidth": "\u3375",
-    "/ovowelsignbengali": "\u09CB",
-    "/ovowelsigndeva": "\u094B",
-    "/ovowelsigngujarati": "\u0ACB",
-    "/ox": "\u1F402",
-    "/p": "\u0070",
-    "/p.inferior": "\u209A",
-    "/paampsfullwidth": "\u3380",
-    "/paampssquare": "\u3380",
-    "/paasentosquare": "\u332B",
-    "/paatusquare": "\u332C",
-    "/pabengali": "\u09AA",
-    "/pacerek": "\uA989",
-    "/package": "\u1F4E6",
-    "/pacute": "\u1E55",
-    "/padeva": "\u092A",
-    "/pafullwidth": "\u33A9",
-    "/page": "\u1F5CF",
-    "/pageCircledText": "\u1F5DF",
-    "/pageCurl": "\u1F4C3",
-    "/pageFacingUp": "\u1F4C4",
-    "/pagedown": "\u21DF",
-    "/pager": "\u1F4DF",
-    "/pages": "\u1F5D0",
-    "/pageup": "\u21DE",
-    "/pagoda": "\u1F6D4",
-    "/pagujarati": "\u0AAA",
-    "/pagurmukhi": "\u0A2A",
-    "/pahiragana": "\u3071",
-    "/paiyannoithai": "\u0E2F",
-    "/pakatakana": "\u30D1",
-    "/palatalizationcyrilliccmb": "\u0484",
-    "/palatcmbcyr": "\u0484",
-    "/pallas": "\u26B4",
-    "/palmTree": "\u1F334",
-    "/palmbranch": "\u2E19",
-    "/palochkacyr": "\u04CF",
-    "/palochkacyrillic": "\u04C0",
-    "/pamurda": "\uA9A6",
-    "/pandaFace": "\u1F43C",
-    "/pangkatpada": "\uA9C7",
-    "/pangkon": "\uA9C0",
-    "/pangrangkep": "\uA9CF",
-    "/pansioskorean": "\u317F",
-    "/panyangga": "\uA980",
-    "/paperclip": "\u1F4CE",
-    "/paragraph": "\u00B6",
-    "/paragraphos": "\u2E0F",
-    "/paragraphosforked": "\u2E10",
-    "/paragraphosforkedreversed": "\u2E11",
-    "/paragraphseparator": "\u2029",
-    "/parallel": "\u2225",
-    "/parallelogramblack": "\u25B0",
-    "/parallelogramwhite": "\u25B1",
-    "/parenbottom": "\u23DD",
-    "/parendblleft": "\u2E28",
-    "/parendblright": "\u2E29",
-    "/parenextensionleft": "\u239C",
-    "/parenextensionright": "\u239F",
-    "/parenflatleft": "\u27EE",
-    "/parenflatright": "\u27EF",
-    "/parenhookupleft": "\u239B",
-    "/parenhookupright": "\u239E",
-    "/parenleft": "\u0028",
-    "/parenleft.inferior": "\u208D",
-    "/parenleft.superior": "\u207D",
-    "/parenleftaltonearabic": "\uFD3E",
-    "/parenleftbt": "\uF8ED",
-    "/parenleftex": "\uF8EC",
-    "/parenleftinferior": "\u208D",
-    "/parenleftmonospace": "\uFF08",
-    "/parenleftsmall": "\uFE59",
-    "/parenleftsuperior": "\u207D",
-    "/parenlefttp": "\uF8EB",
-    "/parenleftvertical": "\uFE35",
-    "/parenlowerhookleft": "\u239D",
-    "/parenlowerhookright": "\u23A0",
-    "/parenright": "\u0029",
-    "/parenright.inferior": "\u208E",
-    "/parenright.superior": "\u207E",
-    "/parenrightaltonearabic": "\uFD3F",
-    "/parenrightbt": "\uF8F8",
-    "/parenrightex": "\uF8F7",
-    "/parenrightinferior": "\u208E",
-    "/parenrightmonospace": "\uFF09",
-    "/parenrightsmall": "\uFE5A",
-    "/parenrightsuperior": "\u207E",
-    "/parenrighttp": "\uF8F6",
-    "/parenrightvertical": "\uFE36",
-    "/parentop": "\u23DC",
-    "/partalternationmark": "\u303D",
-    "/partialdiff": "\u2202",
-    "/partnership": "\u3250",
-    "/partyPopper": "\u1F389",
-    "/paseq:hb": "\u05C0",
-    "/paseqhebrew": "\u05C0",
-    "/pashta:hb": "\u0599",
-    "/pashtahebrew": "\u0599",
-    "/pasquare": "\u33A9",
-    "/passengerShip": "\u1F6F3",
-    "/passivedown": "\u2391",
-    "/passportControl": "\u1F6C2",
-    "/patah": "\u05B7",
-    "/patah11": "\u05B7",
-    "/patah1d": "\u05B7",
-    "/patah2a": "\u05B7",
-    "/patah:hb": "\u05B7",
-    "/patahhebrew": "\u05B7",
-    "/patahnarrowhebrew": "\u05B7",
-    "/patahquarterhebrew": "\u05B7",
-    "/patahwidehebrew": "\u05B7",
-    "/pawPrints": "\u1F43E",
-    "/pawnblack": "\u265F",
-    "/pawnwhite": "\u2659",
-    "/pazer:hb": "\u05A1",
-    "/pazerhebrew": "\u05A1",
-    "/pbopomofo": "\u3106",
-    "/pcfullwidth": "\u3376",
-    "/pcircle": "\u24DF",
-    "/pdot": "\u1E57",
-    "/pdotaccent": "\u1E57",
-    "/pe": "\u05E4",
-    "/pe:hb": "\u05E4",
-    "/peace": "\u262E",
-    "/peach": "\u1F351",
-    "/pear": "\u1F350",
-    "/pecyr": "\u043F",
-    "/pecyrillic": "\u043F",
-    "/pedagesh": "\uFB44",
-    "/pedageshhebrew": "\uFB44",
-    "/pedestrian": "\u1F6B6",
-    "/peezisquare": "\u333B",
-    "/pefinaldageshhebrew": "\uFB43",
-    "/peh.fina": "\uFB57",
-    "/peh.init": "\uFB58",
-    "/peh.isol": "\uFB56",
-    "/peh.medi": "\uFB59",
-    "/peharabic": "\u067E",
-    "/peharmenian": "\u057A",
-    "/pehebrew": "\u05E4",
-    "/peheh": "\u06A6",
-    "/peheh.fina": "\uFB6F",
-    "/peheh.init": "\uFB70",
-    "/peheh.isol": "\uFB6E",
-    "/peheh.medi": "\uFB71",
-    "/pehfinalarabic": "\uFB57",
-    "/pehinitialarabic": "\uFB58",
-    "/pehiragana": "\u307A",
-    "/pehmedialarabic": "\uFB59",
-    "/pehookcyr": "\u04A7",
-    "/pekatakana": "\u30DA",
-    "/pemiddlehookcyrillic": "\u04A7",
-    "/penOverStampedEnvelope": "\u1F586",
-    "/pengkalconsonant": "\uA9BE",
-    "/penguin": "\u1F427",
-    "/penihisquare": "\u3338",
-    "/pensiveFace": "\u1F614",
-    "/pensusquare": "\u333A",
-    "/pentagram": "\u26E4",
-    "/pentasememetrical": "\u23D9",
-    "/pepetvowel": "\uA9BC",
-    "/per": "\u214C",
-    "/perafehebrew": "\uFB4E",
-    "/percent": "\u0025",
-    "/percentarabic": "\u066A",
-    "/percentmonospace": "\uFF05",
-    "/percentsmall": "\uFE6A",
-    "/percussivebidental": "\u02AD",
-    "/percussivebilabial": "\u02AC",
-    "/performingArts": "\u1F3AD",
-    "/period": "\u002E",
-    "/periodarmenian": "\u0589",
-    "/periodcentered": "\u00B7",
-    "/periodhalfwidth": "\uFF61",
-    "/periodinferior": "\uF6E7",
-    "/periodmonospace": "\uFF0E",
-    "/periodsmall": "\uFE52",
-    "/periodsuperior": "\uF6E8",
-    "/periodurdu": "\u06D4",
-    "/perispomenigreekcmb": "\u0342",
-    "/permanentpaper": "\u267E",
-    "/permille": "\u0609",
-    "/perpendicular": "\u22A5",
-    "/perseveringFace": "\u1F623",
-    "/personBlondHair": "\u1F471",
-    "/personBowingDeeply": "\u1F647",
-    "/personFrowning": "\u1F64D",
-    "/personRaisingBothHandsInCelebration": "\u1F64C",
-    "/personWithFoldedHands": "\u1F64F",
-    "/personWithPoutingFace": "\u1F64E",
-    "/personalComputer": "\u1F4BB",
-    "/personball": "\u26F9",
-    "/perspective": "\u2306",
-    "/pertenthousandsign": "\u2031",
-    "/perthousand": "\u2030",
-    "/peseta": "\u20A7",
-    "/peso": "\u20B1",
-    "/pesosquare": "\u3337",
-    "/petailcyr": "\u0525",
-    "/pewithdagesh:hb": "\uFB44",
-    "/pewithrafe:hb": "\uFB4E",
-    "/pffullwidth": "\u338A",
-    "/pflourish": "\uA753",
-    "/pfsquare": "\u338A",
-    "/phabengali": "\u09AB",
-    "/phadeva": "\u092B",
-    "/phagujarati": "\u0AAB",
-    "/phagurmukhi": "\u0A2B",
-    "/pharyngealvoicedfricative": "\u0295",
-    "/phfullwidth": "\u33D7",
-    "/phi": "\u03C6",
-    "/phi.math": "\u03D5",
-    "/phi1": "\u03D5",
-    "/phieuphacirclekorean": "\u327A",
-    "/phieuphaparenkorean": "\u321A",
-    "/phieuphcirclekorean": "\u326C",
-    "/phieuphkorean": "\u314D",
-    "/phieuphparenkorean": "\u320C",
-    "/philatin": "\u0278",
-    "/phinthuthai": "\u0E3A",
-    "/phisymbolgreek": "\u03D5",
-    "/phitailless": "\u2C77",
-    "/phon:AEsmall": "\u1D01",
-    "/phon:Aemod": "\u1D2D",
-    "/phon:Amod": "\u1D2C",
-    "/phon:Asmall": "\u1D00",
-    "/phon:Bbarmod": "\u1D2F",
-    "/phon:Bbarsmall": "\u1D03",
-    "/phon:Bmod": "\u1D2E",
-    "/phon:Csmall": "\u1D04",
-    "/phon:Dmod": "\u1D30",
-    "/phon:Dsmall": "\u1D05",
-    "/phon:ENcyrmod": "\u1D78",
-    "/phon:Elsmallcyr": "\u1D2B",
-    "/phon:Emod": "\u1D31",
-    "/phon:Ereversedmod": "\u1D32",
-    "/phon:Esmall": "\u1D07",
-    "/phon:Ethsmall": "\u1D06",
-    "/phon:Ezhsmall": "\u1D23",
-    "/phon:Gmod": "\u1D33",
-    "/phon:Hmod": "\u1D34",
-    "/phon:Imod": "\u1D35",
-    "/phon:Ismallmod": "\u1DA6",
-    "/phon:Ismallstroke": "\u1D7B",
-    "/phon:Istrokesmallmod": "\u1DA7",
-    "/phon:Jmod": "\u1D36",
-    "/phon:Jsmall": "\u1D0A",
-    "/phon:Kmod": "\u1D37",
-    "/phon:Ksmall": "\u1D0B",
-    "/phon:Lmod": "\u1D38",
-    "/phon:Lsmallmod": "\u1DAB",
-    "/phon:Lsmallstroke": "\u1D0C",
-    "/phon:Mmod": "\u1D39",
-    "/phon:Msmall": "\u1D0D",
-    "/phon:Nmod": "\u1D3A",
-    "/phon:Nreversedmod": "\u1D3B",
-    "/phon:Nsmallmod": "\u1DB0",
-    "/phon:Nsmallreversed": "\u1D0E",
-    "/phon:OUsmall": "\u1D15",
-    "/phon:Omod": "\u1D3C",
-    "/phon:Oopensmall": "\u1D10",
-    "/phon:Osmall": "\u1D0F",
-    "/phon:Oumod": "\u1D3D",
-    "/phon:Pmod": "\u1D3E",
-    "/phon:Psmall": "\u1D18",
-    "/phon:Rmod": "\u1D3F",
-    "/phon:Rsmallreversed": "\u1D19",
-    "/phon:Rsmallturned": "\u1D1A",
-    "/phon:Tmod": "\u1D40",
-    "/phon:Tsmall": "\u1D1B",
-    "/phon:Umod": "\u1D41",
-    "/phon:Usmall": "\u1D1C",
-    "/phon:Usmallmod": "\u1DB8",
-    "/phon:Usmallstroke": "\u1D7E",
-    "/phon:Vsmall": "\u1D20",
-    "/phon:Wmod": "\u1D42",
-    "/phon:Wsmall": "\u1D21",
-    "/phon:Zsmall": "\u1D22",
-    "/phon:aeturned": "\u1D02",
-    "/phon:aeturnedmod": "\u1D46",
-    "/phon:ain": "\u1D25",
-    "/phon:ainmod": "\u1D5C",
-    "/phon:alphamod": "\u1D45",
-    "/phon:alpharetroflexhook": "\u1D90",
-    "/phon:alphaturnedmod": "\u1D9B",
-    "/phon:amod": "\u1D43",
-    "/phon:aretroflexhook": "\u1D8F",
-    "/phon:aturnedmod": "\u1D44",
-    "/phon:betamod": "\u1D5D",
-    "/phon:bmiddletilde": "\u1D6C",
-    "/phon:bmod": "\u1D47",
-    "/phon:bpalatalhook": "\u1D80",
-    "/phon:ccurlmod": "\u1D9D",
-    "/phon:chimod": "\u1D61",
-    "/phon:cmod": "\u1D9C",
-    "/phon:deltamod": "\u1D5F",
-    "/phon:dhooktail": "\u1D91",
-    "/phon:dmiddletilde": "\u1D6D",
-    "/phon:dmod": "\u1D48",
-    "/phon:dotlessjstrokemod": "\u1DA1",
-    "/phon:dpalatalhook": "\u1D81",
-    "/phon:emod": "\u1D49",
-    "/phon:engmod": "\u1D51",
-    "/phon:eopenmod": "\u1D4B",
-    "/phon:eopenretroflexhook": "\u1D93",
-    "/phon:eopenreversedmod": "\u1D9F",
-    "/phon:eopenreversedretroflexhook": "\u1D94",
-    "/phon:eopenturned": "\u1D08",
-    "/phon:eopenturnedmod": "\u1D4C",
-    "/phon:eretroflexhook": "\u1D92",
-    "/phon:eshmod": "\u1DB4",
-    "/phon:eshpalatalhook": "\u1D8B",
-    "/phon:eshretroflexhook": "\u1D98",
-    "/phon:ethmod": "\u1D9E",
-    "/phon:ezhmod": "\u1DBE",
-    "/phon:ezhretroflexhook": "\u1D9A",
-    "/phon:fmiddletilde": "\u1D6E",
-    "/phon:fmod": "\u1DA0",
-    "/phon:fpalatalhook": "\u1D82",
-    "/phon:ginsular": "\u1D79",
-    "/phon:gmod": "\u1D4D",
-    "/phon:gpalatalhook": "\u1D83",
-    "/phon:gr:Gammasmall": "\u1D26",
-    "/phon:gr:Lambdasmall": "\u1D27",
-    "/phon:gr:Pismall": "\u1D28",
-    "/phon:gr:Psismall": "\u1D2A",
-    "/phon:gr:RsmallHO": "\u1D29",
-    "/phon:gr:betasubscript": "\u1D66",
-    "/phon:gr:chisubscript": "\u1D6A",
-    "/phon:gr:gammamod": "\u1D5E",
-    "/phon:gr:gammasubscript": "\u1D67",
-    "/phon:gr:phimod": "\u1D60",
-    "/phon:gr:phisubscript": "\u1D69",
-    "/phon:gr:rhosubscript": "\u1D68",
-    "/phon:gscriptmod": "\u1DA2",
-    "/phon:gturned": "\u1D77",
-    "/phon:hturnedmod": "\u1DA3",
-    "/phon:iotamod": "\u1DA5",
-    "/phon:iotastroke": "\u1D7C",
-    "/phon:iretroflexhook": "\u1D96",
-    "/phon:istrokemod": "\u1DA4",
-    "/phon:isubscript": "\u1D62",
-    "/phon:iturned": "\u1D09",
-    "/phon:iturnedmod": "\u1D4E",
-    "/phon:jcrossedtailmod": "\u1DA8",
-    "/phon:kmod": "\u1D4F",
-    "/phon:kpalatalhook": "\u1D84",
-    "/phon:lpalatalhook": "\u1D85",
-    "/phon:lpalatalhookmod": "\u1DAA",
-    "/phon:lretroflexhookmod": "\u1DA9",
-    "/phon:mhookmod": "\u1DAC",
-    "/phon:mlonglegturnedmod": "\u1DAD",
-    "/phon:mmiddletilde": "\u1D6F",
-    "/phon:mmod": "\u1D50",
-    "/phon:mpalatalhook": "\u1D86",
-    "/phon:mturnedmod": "\u1D5A",
-    "/phon:mturnedsideways": "\u1D1F",
-    "/phon:nlefthookmod": "\u1DAE",
-    "/phon:nmiddletilde": "\u1D70",
-    "/phon:npalatalhook": "\u1D87",
-    "/phon:nretroflexhookmod": "\u1DAF",
-    "/phon:obarmod": "\u1DB1",
-    "/phon:obottomhalf": "\u1D17",
-    "/phon:obottomhalfmod": "\u1D55",
-    "/phon:oeturned": "\u1D14",
-    "/phon:omod": "\u1D52",
-    "/phon:oopenmod": "\u1D53",
-    "/phon:oopenretroflexhook": "\u1D97",
-    "/phon:oopensideways": "\u1D12",
-    "/phon:osideways": "\u1D11",
-    "/phon:ostrokesideways": "\u1D13",
-    "/phon:otophalf": "\u1D16",
-    "/phon:otophalfmod": "\u1D54",
-    "/phon:phimod": "\u1DB2",
-    "/phon:pmiddletilde": "\u1D71",
-    "/phon:pmod": "\u1D56",
-    "/phon:ppalatalhook": "\u1D88",
-    "/phon:pstroke": "\u1D7D",
-    "/phon:rfishmiddletilde": "\u1D73",
-    "/phon:rmiddletilde": "\u1D72",
-    "/phon:rpalatalhook": "\u1D89",
-    "/phon:rsubscript": "\u1D63",
-    "/phon:schwamod": "\u1D4A",
-    "/phon:schwaretroflexhook": "\u1D95",
-    "/phon:shookmod": "\u1DB3",
-    "/phon:smiddletilde": "\u1D74",
-    "/phon:spalatalhook": "\u1D8A",
-    "/phon:spirantvoicedlaryngeal": "\u1D24",
-    "/phon:thetamod": "\u1DBF",
-    "/phon:thstrike": "\u1D7A",
-    "/phon:tmiddletilde": "\u1D75",
-    "/phon:tmod": "\u1D57",
-    "/phon:tpalatalhookmod": "\u1DB5",
-    "/phon:ubarmod": "\u1DB6",
-    "/phon:ue": "\u1D6B",
-    "/phon:umod": "\u1D58",
-    "/phon:upsilonmod": "\u1DB7",
-    "/phon:upsilonstroke": "\u1D7F",
-    "/phon:uretroflexhook": "\u1D99",
-    "/phon:usideways": "\u1D1D",
-    "/phon:usidewaysdieresised": "\u1D1E",
-    "/phon:usidewaysmod": "\u1D59",
-    "/phon:usubscript": "\u1D64",
-    "/phon:vhookmod": "\u1DB9",
-    "/phon:vmod": "\u1D5B",
-    "/phon:vpalatalhook": "\u1D8C",
-    "/phon:vsubscript": "\u1D65",
-    "/phon:vturnedmod": "\u1DBA",
-    "/phon:xpalatalhook": "\u1D8D",
-    "/phon:zcurlmod": "\u1DBD",
-    "/phon:zmiddletilde": "\u1D76",
-    "/phon:zmod": "\u1DBB",
-    "/phon:zpalatalhook": "\u1D8E",
-    "/phon:zretroflexhookmod": "\u1DBC",
-    "/phook": "\u01A5",
-    "/phophanthai": "\u0E1E",
-    "/phophungthai": "\u0E1C",
-    "/phosamphaothai": "\u0E20",
-    "/pi": "\u03C0",
-    "/pi.math": "\u03D6",
-    "/piasutorusquare": "\u332E",
-    "/pick": "\u26CF",
-    "/pidblstruck": "\u213C",
-    "/pieupacirclekorean": "\u3273",
-    "/pieupaparenkorean": "\u3213",
-    "/pieupcieuckorean": "\u3176",
-    "/pieupcirclekorean": "\u3265",
-    "/pieupkiyeokkorean": "\u3172",
-    "/pieupkorean": "\u3142",
-    "/pieupparenkorean": "\u3205",
-    "/pieupsioskiyeokkorean": "\u3174",
-    "/pieupsioskorean": "\u3144",
-    "/pieupsiostikeutkorean": "\u3175",
-    "/pieupthieuthkorean": "\u3177",
-    "/pieuptikeutkorean": "\u3173",
-    "/pig": "\u1F416",
-    "/pigFace": "\u1F437",
-    "/pigNose": "\u1F43D",
-    "/pihiragana": "\u3074",
-    "/pikatakana": "\u30D4",
-    "/pikosquare": "\u3330",
-    "/pikurusquare": "\u332F",
-    "/pilcrowsignreversed": "\u204B",
-    "/pileOfPoo": "\u1F4A9",
-    "/pill": "\u1F48A",
-    "/pineDecoration": "\u1F38D",
-    "/pineapple": "\u1F34D",
-    "/pisces": "\u2653",
-    "/piselehpada": "\uA9CC",
-    "/pistol": "\u1F52B",
-    "/pisymbolgreek": "\u03D6",
-    "/pitchfork": "\u22D4",
-    "/piwrarmenian": "\u0583",
-    "/placeOfWorship": "\u1F6D0",
-    "/placeofinterestsign": "\u2318",
-    "/planck": "\u210E",
-    "/plancktwopi": "\u210F",
-    "/plus": "\u002B",
-    "/plus.inferior": "\u208A",
-    "/plus.superior": "\u207A",
-    "/plusbelowcmb": "\u031F",
-    "/pluscircle": "\u2295",
-    "/plusminus": "\u00B1",
-    "/plusmod": "\u02D6",
-    "/plusmonospace": "\uFF0B",
-    "/plussignalt:hb": "\uFB29",
-    "/plussignmod": "\u02D6",
-    "/plussmall": "\uFE62",
-    "/plussuperior": "\u207A",
-    "/pluto": "\u2647",
-    "/pmfullwidth": "\u33D8",
-    "/pmonospace": "\uFF50",
-    "/pmsquare": "\u33D8",
-    "/pocketCalculator": "\u1F5A9",
-    "/poeticverse": "\u060E",
-    "/pohiragana": "\u307D",
-    "/pointerleftblack": "\u25C4",
-    "/pointerleftwhite": "\u25C5",
-    "/pointerrightblack": "\u25BA",
-    "/pointerrightwhite": "\u25BB",
-    "/pointingindexdownwhite": "\u261F",
-    "/pointingindexleftblack": "\u261A",
-    "/pointingindexleftwhite": "\u261C",
-    "/pointingindexrightblack": "\u261B",
-    "/pointingindexrightwhite": "\u261E",
-    "/pointingindexupwhite": "\u261D",
-    "/pointingtriangledownheavywhite": "\u26DB",
-    "/pointosquare": "\u333D",
-    "/pointring": "\u2E30",
-    "/pokatakana": "\u30DD",
-    "/pokrytiecmbcyr": "\u0487",
-    "/policeCar": "\u1F693",
-    "/policeCarsRevolvingLight": "\u1F6A8",
-    "/policeOfficer": "\u1F46E",
-    "/pondosquare": "\u3340",
-    "/poodle": "\u1F429",
-    "/popcorn": "\u1F37F",
-    "/popdirectionalformatting": "\u202C",
-    "/popdirectionalisolate": "\u2069",
-    "/poplathai": "\u0E1B",
-    "/portableStereo": "\u1F4FE",
-    "/positionindicator": "\u2316",
-    "/postalHorn": "\u1F4EF",
-    "/postalmark": "\u3012",
-    "/postalmarkface": "\u3020",
-    "/postbox": "\u1F4EE",
-    "/potOfFood": "\u1F372",
-    "/potableWater": "\u1F6B0",
-    "/pouch": "\u1F45D",
-    "/poultryLeg": "\u1F357",
-    "/poutingCatFace": "\u1F63E",
-    "/poutingFace": "\u1F621",
-    "/power": "\u23FB",
-    "/poweron": "\u23FD",
-    "/poweronoff": "\u23FC",
-    "/powersleep": "\u23FE",
-    "/pparen": "\u24AB",
-    "/pparenthesized": "\u24AB",
-    "/ppmfullwidth": "\u33D9",
-    "/prayerBeads": "\u1F4FF",
-    "/precedes": "\u227A",
-    "/precedesbutnotequivalent": "\u22E8",
-    "/precedesorequal": "\u227C",
-    "/precedesorequivalent": "\u227E",
-    "/precedesunderrelation": "\u22B0",
-    "/prescription": "\u211E",
-    "/preversedepigraphic": "\uA7FC",
-    "/previouspage": "\u2397",
-    "/prfullwidth": "\u33DA",
-    "/primedblmod": "\u02BA",
-    "/primemod": "\u02B9",
-    "/primereversed": "\u2035",
-    "/princess": "\u1F478",
-    "/printer": "\u1F5A8",
-    "/printerIcon": "\u1F5B6",
-    "/printideographiccircled": "\u329E",
-    "/printscreen": "\u2399",
-    "/product": "\u220F",
-    "/prohibitedSign": "\u1F6C7",
-    "/projective": "\u2305",
-    "/prolongedkana": "\u30FC",
-    "/propellor": "\u2318",
-    "/propersubset": "\u2282",
-    "/propersuperset": "\u2283",
-    "/propertyline": "\u214A",
-    "/proportion": "\u2237",
-    "/proportional": "\u221D",
-    "/psfullwidth": "\u33B0",
-    "/psi": "\u03C8",
-    "/psicyr": "\u0471",
-    "/psicyrillic": "\u0471",
-    "/psilicmbcyr": "\u0486",
-    "/psilipneumatacyrilliccmb": "\u0486",
-    "/pssquare": "\u33B0",
-    "/pstrokedescender": "\uA751",
-    "/ptail": "\uA755",
-    "/publicAddressLoudspeaker": "\u1F4E2",
-    "/puhiragana": "\u3077",
-    "/pukatakana": "\u30D7",
-    "/punctuationspace": "\u2008",
-    "/purpleHeart": "\u1F49C",
-    "/purse": "\u1F45B",
-    "/pushpin": "\u1F4CC",
-    "/putLitterInItsPlace": "\u1F6AE",
-    "/pvfullwidth": "\u33B4",
-    "/pvsquare": "\u33B4",
-    "/pwfullwidth": "\u33BA",
-    "/pwsquare": "\u33BA",
-    "/q": "\u0071",
-    "/qacyr": "\u051B",
-    "/qadeva": "\u0958",
-    "/qadma:hb": "\u05A8",
-    "/qadmahebrew": "\u05A8",
-    "/qaf": "\u0642",
-    "/qaf.fina": "\uFED6",
-    "/qaf.init": "\uFED7",
-    "/qaf.init_alefmaksura.fina": "\uFC35",
-    "/qaf.init_hah.fina": "\uFC33",
-    "/qaf.init_hah.medi": "\uFCC2",
-    "/qaf.init_meem.fina": "\uFC34",
-    "/qaf.init_meem.medi": "\uFCC3",
-    "/qaf.init_meem.medi_hah.medi": "\uFDB4",
-    "/qaf.init_yeh.fina": "\uFC36",
-    "/qaf.isol": "\uFED5",
-    "/qaf.medi": "\uFED8",
-    "/qaf.medi_alefmaksura.fina": "\uFC7E",
-    "/qaf.medi_meem.medi_hah.fina": "\uFD7E",
-    "/qaf.medi_meem.medi_meem.fina": "\uFD7F",
-    "/qaf.medi_meem.medi_yeh.fina": "\uFDB2",
-    "/qaf.medi_yeh.fina": "\uFC7F",
-    "/qaf_lam_alefmaksuraabove": "\u06D7",
-    "/qafarabic": "\u0642",
-    "/qafdotabove": "\u06A7",
-    "/qaffinalarabic": "\uFED6",
-    "/qafinitialarabic": "\uFED7",
-    "/qafmedialarabic": "\uFED8",
-    "/qafthreedotsabove": "\u06A8",
-    "/qamats": "\u05B8",
-    "/qamats10": "\u05B8",
-    "/qamats1a": "\u05B8",
-    "/qamats1c": "\u05B8",
-    "/qamats27": "\u05B8",
-    "/qamats29": "\u05B8",
-    "/qamats33": "\u05B8",
-    "/qamats:hb": "\u05B8",
-    "/qamatsQatan:hb": "\u05C7",
-    "/qamatsde": "\u05B8",
-    "/qamatshebrew": "\u05B8",
-    "/qamatsnarrowhebrew": "\u05B8",
-    "/qamatsqatanhebrew": "\u05B8",
-    "/qamatsqatannarrowhebrew": "\u05B8",
-    "/qamatsqatanquarterhebrew": "\u05B8",
-    "/qamatsqatanwidehebrew": "\u05B8",
-    "/qamatsquarterhebrew": "\u05B8",
-    "/qamatswidehebrew": "\u05B8",
-    "/qarneFarah:hb": "\u059F",
-    "/qarneyparahebrew": "\u059F",
-    "/qbopomofo": "\u3111",
-    "/qcircle": "\u24E0",
-    "/qdiagonalstroke": "\uA759",
-    "/qhook": "\u02A0",
-    "/qhooktail": "\u024B",
-    "/qmonospace": "\uFF51",
-    "/qof": "\u05E7",
-    "/qof:hb": "\u05E7",
-    "/qofdagesh": "\uFB47",
-    "/qofdageshhebrew": "\uFB47",
-    "/qofhatafpatah": "\u05E7",
-    "/qofhatafpatahhebrew": "\u05E7",
-    "/qofhatafsegol": "\u05E7",
-    "/qofhatafsegolhebrew": "\u05E7",
-    "/qofhebrew": "\u05E7",
-    "/qofhiriq": "\u05E7",
-    "/qofhiriqhebrew": "\u05E7",
-    "/qofholam": "\u05E7",
-    "/qofholamhebrew": "\u05E7",
-    "/qofpatah": "\u05E7",
-    "/qofpatahhebrew": "\u05E7",
-    "/qofqamats": "\u05E7",
-    "/qofqamatshebrew": "\u05E7",
-    "/qofqubuts": "\u05E7",
-    "/qofqubutshebrew": "\u05E7",
-    "/qofsegol": "\u05E7",
-    "/qofsegolhebrew": "\u05E7",
-    "/qofsheva": "\u05E7",
-    "/qofshevahebrew": "\u05E7",
-    "/qoftsere": "\u05E7",
-    "/qoftserehebrew": "\u05E7",
-    "/qofwithdagesh:hb": "\uFB47",
-    "/qparen": "\u24AC",
-    "/qparenthesized": "\u24AC",
-    "/qpdigraph": "\u0239",
-    "/qstrokedescender": "\uA757",
-    "/quadarrowdownfunc": "\u2357",
-    "/quadarrowleftfunc": "\u2347",
-    "/quadarrowrightfunc": "\u2348",
-    "/quadarrowupfunc": "\u2350",
-    "/quadbackslashfunc": "\u2342",
-    "/quadcaretdownfunc": "\u234C",
-    "/quadcaretupfunc": "\u2353",
-    "/quadcirclefunc": "\u233C",
-    "/quadcolonfunc": "\u2360",
-    "/quaddelfunc": "\u2354",
-    "/quaddeltafunc": "\u234D",
-    "/quaddiamondfunc": "\u233A",
-    "/quaddividefunc": "\u2339",
-    "/quadequalfunc": "\u2338",
-    "/quadfunc": "\u2395",
-    "/quadgreaterfunc": "\u2344",
-    "/quadjotfunc": "\u233B",
-    "/quadlessfunc": "\u2343",
-    "/quadnotequalfunc": "\u236F",
-    "/quadquestionfunc": "\u2370",
-    "/quadrantLowerLeft": "\u2596",
-    "/quadrantLowerRight": "\u2597",
-    "/quadrantUpperLeft": "\u2598",
-    "/quadrantUpperLeftAndLowerLeftAndLowerRight": "\u2599",
-    "/quadrantUpperLeftAndLowerRight": "\u259A",
-    "/quadrantUpperLeftAndUpperRightAndLowerLeft": "\u259B",
-    "/quadrantUpperLeftAndUpperRightAndLowerRight": "\u259C",
-    "/quadrantUpperRight": "\u259D",
-    "/quadrantUpperRightAndLowerLeft": "\u259E",
-    "/quadrantUpperRightAndLowerLeftAndLowerRight": "\u259F",
-    "/quadrupleminute": "\u2057",
-    "/quadslashfunc": "\u2341",
-    "/quarternote": "\u2669",
-    "/qubuts": "\u05BB",
-    "/qubuts18": "\u05BB",
-    "/qubuts25": "\u05BB",
-    "/qubuts31": "\u05BB",
-    "/qubuts:hb": "\u05BB",
-    "/qubutshebrew": "\u05BB",
-    "/qubutsnarrowhebrew": "\u05BB",
-    "/qubutsquarterhebrew": "\u05BB",
-    "/qubutswidehebrew": "\u05BB",
-    "/queenblack": "\u265B",
-    "/queenwhite": "\u2655",
-    "/question": "\u003F",
-    "/questionarabic": "\u061F",
-    "/questionarmenian": "\u055E",
-    "/questiondbl": "\u2047",
-    "/questiondown": "\u00BF",
-    "/questiondownsmall": "\uF7BF",
-    "/questionedequal": "\u225F",
-    "/questionexclamationmark": "\u2048",
-    "/questiongreek": "\u037E",
-    "/questionideographiccircled": "\u3244",
-    "/questionmonospace": "\uFF1F",
-    "/questionreversed": "\u2E2E",
-    "/questionsmall": "\uF73F",
-    "/quincunx": "\u26BB",
-    "/quotedbl": "\u0022",
-    "/quotedblbase": "\u201E",
-    "/quotedblleft": "\u201C",
-    "/quotedbllowreversed": "\u2E42",
-    "/quotedblmonospace": "\uFF02",
-    "/quotedblprime": "\u301E",
-    "/quotedblprimereversed": "\u301D",
-    "/quotedblreversed": "\u201F",
-    "/quotedblright": "\u201D",
-    "/quoteleft": "\u2018",
-    "/quoteleftreversed": "\u201B",
-    "/quotequadfunc": "\u235E",
-    "/quotereversed": "\u201B",
-    "/quoteright": "\u2019",
-    "/quoterightn": "\u0149",
-    "/quotesinglbase": "\u201A",
-    "/quotesingle": "\u0027",
-    "/quotesinglemonospace": "\uFF07",
-    "/quoteunderlinefunc": "\u2358",
-    "/r": "\u0072",
-    "/raagung": "\uA9AC",
-    "/raarmenian": "\u057C",
-    "/rabbit": "\u1F407",
-    "/rabbitFace": "\u1F430",
-    "/rabengali": "\u09B0",
-    "/racingCar": "\u1F3CE",
-    "/racingMotorcycle": "\u1F3CD",
-    "/racirclekatakana": "\u32F6",
-    "/racute": "\u0155",
-    "/radeva": "\u0930",
-    "/radfullwidth": "\u33AD",
-    "/radical": "\u221A",
-    "/radicalbottom": "\u23B7",
-    "/radicalex": "\uF8E5",
-    "/radio": "\u1F4FB",
-    "/radioButton": "\u1F518",
-    "/radioactive": "\u2622",
-    "/radovers2fullwidth": "\u33AF",
-    "/radoversfullwidth": "\u33AE",
-    "/radoverssquare": "\u33AE",
-    "/radoverssquaredsquare": "\u33AF",
-    "/radsquare": "\u33AD",
-    "/rafe": "\u05BF",
-    "/rafe:hb": "\u05BF",
-    "/rafehebrew": "\u05BF",
-    "/ragujarati": "\u0AB0",
-    "/ragurmukhi": "\u0A30",
-    "/rahiragana": "\u3089",
-    "/railwayCar": "\u1F683",
-    "/railwayTrack": "\u1F6E4",
-    "/rain": "\u26C6",
-    "/rainbow": "\u1F308",
-    "/raisedHandFingersSplayed": "\u1F590",
-    "/raisedHandPartBetweenMiddleAndRingFingers": "\u1F596",
-    "/raisedmcsign": "\u1F16A",
-    "/raisedmdsign": "\u1F16B",
-    "/rakatakana": "\u30E9",
-    "/rakatakanahalfwidth": "\uFF97",
-    "/ralowerdiagonalbengali": "\u09F1",
-    "/ram": "\u1F40F",
-    "/ramiddlediagonalbengali": "\u09F0",
-    "/ramshorn": "\u0264",
-    "/rat": "\u1F400",
-    "/ratio": "\u2236",
-    "/ray": "\u0608",
-    "/rbopomofo": "\u3116",
-    "/rcaron": "\u0159",
-    "/rcedilla": "\u0157",
-    "/rcircle": "\u24E1",
-    "/rcommaaccent": "\u0157",
-    "/rdblgrave": "\u0211",
-    "/rdot": "\u1E59",
-    "/rdotaccent": "\u1E59",
-    "/rdotbelow": "\u1E5B",
-    "/rdotbelowmacron": "\u1E5D",
-    "/reachideographicparen": "\u3243",
-    "/recirclekatakana": "\u32F9",
-    "/recreationalVehicle": "\u1F699",
-    "/rectangleblack": "\u25AC",
-    "/rectangleverticalblack": "\u25AE",
-    "/rectangleverticalwhite": "\u25AF",
-    "/rectanglewhite": "\u25AD",
-    "/recycledpaper": "\u267C",
-    "/recyclefiveplastics": "\u2677",
-    "/recyclefourplastics": "\u2676",
-    "/recyclegeneric": "\u267A",
-    "/recycleoneplastics": "\u2673",
-    "/recyclepartiallypaper": "\u267D",
-    "/recyclesevenplastics": "\u2679",
-    "/recyclesixplastics": "\u2678",
-    "/recyclethreeplastics": "\u2675",
-    "/recycletwoplastics": "\u2674",
-    "/recycleuniversal": "\u2672",
-    "/recycleuniversalblack": "\u267B",
-    "/redApple": "\u1F34E",
-    "/redTriangleDOwn": "\u1F53B",
-    "/redTriangleUp": "\u1F53A",
-    "/referencemark": "\u203B",
-    "/reflexsubset": "\u2286",
-    "/reflexsuperset": "\u2287",
-    "/regionalindicatorsymbollettera": "\u1F1E6",
-    "/regionalindicatorsymbolletterb": "\u1F1E7",
-    "/regionalindicatorsymbolletterc": "\u1F1E8",
-    "/regionalindicatorsymbolletterd": "\u1F1E9",
-    "/regionalindicatorsymbollettere": "\u1F1EA",
-    "/regionalindicatorsymbolletterf": "\u1F1EB",
-    "/regionalindicatorsymbolletterg": "\u1F1EC",
-    "/regionalindicatorsymbolletterh": "\u1F1ED",
-    "/regionalindicatorsymbolletteri": "\u1F1EE",
-    "/regionalindicatorsymbolletterj": "\u1F1EF",
-    "/regionalindicatorsymbolletterk": "\u1F1F0",
-    "/regionalindicatorsymbolletterl": "\u1F1F1",
-    "/regionalindicatorsymbolletterm": "\u1F1F2",
-    "/regionalindicatorsymbollettern": "\u1F1F3",
-    "/regionalindicatorsymbollettero": "\u1F1F4",
-    "/regionalindicatorsymbolletterp": "\u1F1F5",
-    "/regionalindicatorsymbolletterq": "\u1F1F6",
-    "/regionalindicatorsymbolletterr": "\u1F1F7",
-    "/regionalindicatorsymbolletters": "\u1F1F8",
-    "/regionalindicatorsymbollettert": "\u1F1F9",
-    "/regionalindicatorsymbolletteru": "\u1F1FA",
-    "/regionalindicatorsymbolletterv": "\u1F1FB",
-    "/regionalindicatorsymbolletterw": "\u1F1FC",
-    "/regionalindicatorsymbolletterx": "\u1F1FD",
-    "/regionalindicatorsymbollettery": "\u1F1FE",
-    "/regionalindicatorsymbolletterz": "\u1F1FF",
-    "/registered": "\u00AE",
-    "/registersans": "\uF8E8",
-    "/registerserif": "\uF6DA",
-    "/reh.fina": "\uFEAE",
-    "/reh.init_superscriptalef.fina": "\uFC5C",
-    "/reh.isol": "\uFEAD",
-    "/rehHamzaAbove": "\u076C",
-    "/rehSmallTahTwoDots": "\u0771",
-    "/rehStroke": "\u075B",
-    "/rehTwoDotsVerticallyAbove": "\u076B",
-    "/rehVabove": "\u0692",
-    "/rehVbelow": "\u0695",
-    "/reharabic": "\u0631",
-    "/reharmenian": "\u0580",
-    "/rehdotbelow": "\u0694",
-    "/rehdotbelowdotabove": "\u0696",
-    "/rehfinalarabic": "\uFEAE",
-    "/rehfourdotsabove": "\u0699",
-    "/rehinvertedV": "\u06EF",
-    "/rehiragana": "\u308C",
-    "/rehring": "\u0693",
-    "/rehtwodotsabove": "\u0697",
-    "/rehyehaleflamarabic": "\u0631",
-    "/rekatakana": "\u30EC",
-    "/rekatakanahalfwidth": "\uFF9A",
-    "/relievedFace": "\u1F60C",
-    "/religionideographiccircled": "\u32AA",
-    "/reminderRibbon": "\u1F397",
-    "/remusquare": "\u3355",
-    "/rentogensquare": "\u3356",
-    "/replacementchar": "\uFFFD",
-    "/replacementcharobj": "\uFFFC",
-    "/representideographicparen": "\u3239",
-    "/rerengganleft": "\uA9C1",
-    "/rerengganright": "\uA9C2",
-    "/resh": "\u05E8",
-    "/resh:hb": "\u05E8",
-    "/reshdageshhebrew": "\uFB48",
-    "/reshhatafpatah": "\u05E8",
-    "/reshhatafpatahhebrew": "\u05E8",
-    "/reshhatafsegol": "\u05E8",
-    "/reshhatafsegolhebrew": "\u05E8",
-    "/reshhebrew": "\u05E8",
-    "/reshhiriq": "\u05E8",
-    "/reshhiriqhebrew": "\u05E8",
-    "/reshholam": "\u05E8",
-    "/reshholamhebrew": "\u05E8",
-    "/reshpatah": "\u05E8",
-    "/reshpatahhebrew": "\u05E8",
-    "/reshqamats": "\u05E8",
-    "/reshqamatshebrew": "\u05E8",
-    "/reshqubuts": "\u05E8",
-    "/reshqubutshebrew": "\u05E8",
-    "/reshsegol": "\u05E8",
-    "/reshsegolhebrew": "\u05E8",
-    "/reshsheva": "\u05E8",
-    "/reshshevahebrew": "\u05E8",
-    "/reshtsere": "\u05E8",
-    "/reshtserehebrew": "\u05E8",
-    "/reshwide:hb": "\uFB27",
-    "/reshwithdagesh:hb": "\uFB48",
-    "/resourceideographiccircled": "\u32AE",
-    "/resourceideographicparen": "\u323E",
-    "/response": "\u211F",
-    "/restideographiccircled": "\u32A1",
-    "/restideographicparen": "\u3241",
-    "/restrictedentryoneleft": "\u26E0",
-    "/restrictedentrytwoleft": "\u26E1",
-    "/restroom": "\u1F6BB",
-    "/return": "\u23CE",
-    "/reversedHandMiddleFingerExtended": "\u1F595",
-    "/reversedRaisedHandFingersSplayed": "\u1F591",
-    "/reversedThumbsDownSign": "\u1F593",
-    "/reversedThumbsUpSign": "\u1F592",
-    "/reversedVictoryHand": "\u1F594",
-    "/reversedonehundred.roman": "\u2183",
-    "/reversedtilde": "\u223D",
-    "/reversedzecyr": "\u0511",
-    "/revia:hb": "\u0597",
-    "/reviahebrew": "\u0597",
-    "/reviamugrashhebrew": "\u0597",
-    "/revlogicalnot": "\u2310",
-    "/revolvingHearts": "\u1F49E",
-    "/rfishhook": "\u027E",
-    "/rfishhookreversed": "\u027F",
-    "/rgravedbl": "\u0211",
-    "/rhabengali": "\u09DD",
-    "/rhacyr": "\u0517",
-    "/rhadeva": "\u095D",
-    "/rho": "\u03C1",
-    "/rhoasper": "\u1FE5",
-    "/rhofunc": "\u2374",
-    "/rholenis": "\u1FE4",
-    "/rhook": "\u027D",
-    "/rhookturned": "\u027B",
-    "/rhookturnedsuperior": "\u02B5",
-    "/rhookturnedsupmod": "\u02B5",
-    "/rhostrokesymbol": "\u03FC",
-    "/rhosymbol": "\u03F1",
-    "/rhosymbolgreek": "\u03F1",
-    "/rhotichookmod": "\u02DE",
-    "/rial": "\uFDFC",
-    "/ribbon": "\u1F380",
-    "/riceBall": "\u1F359",
-    "/riceCracker": "\u1F358",
-    "/ricirclekatakana": "\u32F7",
-    "/rieulacirclekorean": "\u3271",
-    "/rieulaparenkorean": "\u3211",
-    "/rieulcirclekorean": "\u3263",
-    "/rieulhieuhkorean": "\u3140",
-    "/rieulkiyeokkorean": "\u313A",
-    "/rieulkiyeoksioskorean": "\u3169",
-    "/rieulkorean": "\u3139",
-    "/rieulmieumkorean": "\u313B",
-    "/rieulpansioskorean": "\u316C",
-    "/rieulparenkorean": "\u3203",
-    "/rieulphieuphkorean": "\u313F",
-    "/rieulpieupkorean": "\u313C",
-    "/rieulpieupsioskorean": "\u316B",
-    "/rieulsioskorean": "\u313D",
-    "/rieulthieuthkorean": "\u313E",
-    "/rieultikeutkorean": "\u316A",
-    "/rieulyeorinhieuhkorean": "\u316D",
-    "/right-pointingMagnifyingGlass": "\u1F50E",
-    "/rightAngerBubble": "\u1F5EF",
-    "/rightHalfBlock": "\u2590",
-    "/rightHandTelephoneReceiver": "\u1F57D",
-    "/rightOneEighthBlock": "\u2595",
-    "/rightSpeaker": "\u1F568",
-    "/rightSpeakerOneSoundWave": "\u1F569",
-    "/rightSpeakerThreeSoundWaves": "\u1F56A",
-    "/rightSpeechBubble": "\u1F5E9",
-    "/rightThoughtBubble": "\u1F5ED",
-    "/rightangle": "\u221F",
-    "/rightarrowoverleftarrow": "\u21C4",
-    "/rightdnheavyleftuplight": "\u2546",
-    "/rightharpoonoverleftharpoon": "\u21CC",
-    "/rightheavyleftdnlight": "\u252E",
-    "/rightheavyleftuplight": "\u2536",
-    "/rightheavyleftvertlight": "\u253E",
-    "/rightideographiccircled": "\u32A8",
-    "/rightlightleftdnheavy": "\u2531",
-    "/rightlightleftupheavy": "\u2539",
-    "/rightlightleftvertheavy": "\u2549",
-    "/righttackbelowcmb": "\u0319",
-    "/righttoleftembed": "\u202B",
-    "/righttoleftisolate": "\u2067",
-    "/righttoleftmark": "\u200F",
-    "/righttoleftoverride": "\u202E",
-    "/righttriangle": "\u22BF",
-    "/rightupheavyleftdnlight": "\u2544",
-    "/rihiragana": "\u308A",
-    "/rikatakana": "\u30EA",
-    "/rikatakanahalfwidth": "\uFF98",
-    "/ring": "\u02DA",
-    "/ringbelowcmb": "\u0325",
-    "/ringcmb": "\u030A",
-    "/ringequal": "\u2257",
-    "/ringhalfleft": "\u02BF",
-    "/ringhalfleftarmenian": "\u0559",
-    "/ringhalfleftbelowcmb": "\u031C",
-    "/ringhalfleftcentered": "\u02D3",
-    "/ringhalfleftcentredmod": "\u02D3",
-    "/ringhalfleftmod": "\u02BF",
-    "/ringhalfright": "\u02BE",
-    "/ringhalfrightbelowcmb": "\u0339",
-    "/ringhalfrightcentered": "\u02D2",
-    "/ringhalfrightcentredmod": "\u02D2",
-    "/ringhalfrightmod": "\u02BE",
-    "/ringinequal": "\u2256",
-    "/ringingBell": "\u1F56D",
-    "/ringlowmod": "\u02F3",
-    "/ringoperator": "\u2218",
-    "/rinsular": "\uA783",
-    "/rinvertedbreve": "\u0213",
-    "/rirasquare": "\u3352",
-    "/risingdiagonal": "\u27CB",
-    "/rittorusquare": "\u3351",
-    "/rlinebelow": "\u1E5F",
-    "/rlongleg": "\u027C",
-    "/rlonglegturned": "\u027A",
-    "/rmacrondot": "\u1E5D",
-    "/rmonospace": "\uFF52",
-    "/rnoon": "\u06BB",
-    "/rnoon.fina": "\uFBA1",
-    "/rnoon.init": "\uFBA2",
-    "/rnoon.isol": "\uFBA0",
-    "/rnoon.medi": "\uFBA3",
-    "/roastedSweetPotato": "\u1F360",
-    "/robliquestroke": "\uA7A7",
-    "/rocirclekatakana": "\u32FA",
-    "/rocket": "\u1F680",
-    "/rohiragana": "\u308D",
-    "/rokatakana": "\u30ED",
-    "/rokatakanahalfwidth": "\uFF9B",
-    "/rolled-upNewspaper": "\u1F5DE",
-    "/rollerCoaster": "\u1F3A2",
-    "/rookblack": "\u265C",
-    "/rookwhite": "\u2656",
-    "/rooster": "\u1F413",
-    "/roruathai": "\u0E23",
-    "/rose": "\u1F339",
-    "/rosette": "\u1F3F5",
-    "/roundPushpin": "\u1F4CD",
-    "/roundedzeroabove": "\u06DF",
-    "/rowboat": "\u1F6A3",
-    "/rparen": "\u24AD",
-    "/rparenthesized": "\u24AD",
-    "/rrabengali": "\u09DC",
-    "/rradeva": "\u0931",
-    "/rragurmukhi": "\u0A5C",
-    "/rreh": "\u0691",
-    "/rreh.fina": "\uFB8D",
-    "/rreh.isol": "\uFB8C",
-    "/rreharabic": "\u0691",
-    "/rrehfinalarabic": "\uFB8D",
-    "/rrotunda": "\uA75B",
-    "/rrvocalicbengali": "\u09E0",
-    "/rrvocalicdeva": "\u0960",
-    "/rrvocalicgujarati": "\u0AE0",
-    "/rrvocalicvowelsignbengali": "\u09C4",
-    "/rrvocalicvowelsigndeva": "\u0944",
-    "/rrvocalicvowelsigngujarati": "\u0AC4",
-    "/rstroke": "\u024D",
-    "/rsuperior": "\uF6F1",
-    "/rsupmod": "\u02B3",
-    "/rtailturned": "\u2C79",
-    "/rtblock": "\u2590",
-    "/rturned": "\u0279",
-    "/rturnedsuperior": "\u02B4",
-    "/rturnedsupmod": "\u02B4",
-    "/ruble": "\u20BD",
-    "/rucirclekatakana": "\u32F8",
-    "/rugbyFootball": "\u1F3C9",
-    "/ruhiragana": "\u308B",
-    "/rukatakana": "\u30EB",
-    "/rukatakanahalfwidth": "\uFF99",
-    "/rum": "\uA775",
-    "/rumrotunda": "\uA75D",
-    "/runner": "\u1F3C3",
-    "/runningShirtSash": "\u1F3BD",
-    "/rupeemarkbengali": "\u09F2",
-    "/rupeesignbengali": "\u09F3",
-    "/rupiah": "\uF6DD",
-    "/rupiisquare": "\u3353",
-    "/ruthai": "\u0E24",
-    "/ruuburusquare": "\u3354",
-    "/rvocalicbengali": "\u098B",
-    "/rvocalicdeva": "\u090B",
-    "/rvocalicgujarati": "\u0A8B",
-    "/rvocalicvowelsignbengali": "\u09C3",
-    "/rvocalicvowelsigndeva": "\u0943",
-    "/rvocalicvowelsigngujarati": "\u0AC3",
-    "/s": "\u0073",
-    "/s.inferior": "\u209B",
-    "/s_t": "\uFB06",
-    "/sabengali": "\u09B8",
-    "/sacirclekatakana": "\u32DA",
-    "/sacute": "\u015B",
-    "/sacutedotaccent": "\u1E65",
-    "/sad": "\u0635",
-    "/sad.fina": "\uFEBA",
-    "/sad.init": "\uFEBB",
-    "/sad.init_alefmaksura.fina": "\uFD05",
-    "/sad.init_hah.fina": "\uFC20",
-    "/sad.init_hah.medi": "\uFCB1",
-    "/sad.init_hah.medi_hah.medi": "\uFD65",
-    "/sad.init_khah.medi": "\uFCB2",
-    "/sad.init_meem.fina": "\uFC21",
-    "/sad.init_meem.medi": "\uFCB3",
-    "/sad.init_meem.medi_meem.medi": "\uFDC5",
-    "/sad.init_reh.fina": "\uFD0F",
-    "/sad.init_yeh.fina": "\uFD06",
-    "/sad.isol": "\uFEB9",
-    "/sad.medi": "\uFEBC",
-    "/sad.medi_alefmaksura.fina": "\uFD21",
-    "/sad.medi_hah.medi_hah.fina": "\uFD64",
-    "/sad.medi_hah.medi_yeh.fina": "\uFDA9",
-    "/sad.medi_meem.medi_meem.fina": "\uFD66",
-    "/sad.medi_reh.fina": "\uFD2B",
-    "/sad.medi_yeh.fina": "\uFD22",
-    "/sad_lam_alefmaksuraabove": "\u06D6",
-    "/sadarabic": "\u0635",
-    "/sadeva": "\u0938",
-    "/sadfinalarabic": "\uFEBA",
-    "/sadinitialarabic": "\uFEBB",
-    "/sadmedialarabic": "\uFEBC",
-    "/sadthreedotsabove": "\u069E",
-    "/sadtwodotsbelow": "\u069D",
-    "/sagittarius": "\u2650",
-    "/sagujarati": "\u0AB8",
-    "/sagurmukhi": "\u0A38",
-    "/sahiragana": "\u3055",
-    "/saikurusquare": "\u331F",
-    "/sailboat": "\u26F5",
-    "/sakatakana": "\u30B5",
-    "/sakatakanahalfwidth": "\uFF7B",
-    "/sakeBottleAndCup": "\u1F376",
-    "/sallallahoualayhewasallamarabic": "\uFDFA",
-    "/saltillo": "\uA78C",
-    "/saltire": "\u2613",
-    "/samahaprana": "\uA9B0",
-    "/samekh": "\u05E1",
-    "/samekh:hb": "\u05E1",
-    "/samekhdagesh": "\uFB41",
-    "/samekhdageshhebrew": "\uFB41",
-    "/samekhhebrew": "\u05E1",
-    "/samekhwithdagesh:hb": "\uFB41",
-    "/sampi": "\u03E1",
-    "/sampiarchaic": "\u0373",
-    "/samurda": "\uA9AF",
-    "/samvat": "\u0604",
-    "/san": "\u03FB",
-    "/santiimusquare": "\u3320",
-    "/saraaathai": "\u0E32",
-    "/saraaethai": "\u0E41",
-    "/saraaimaimalaithai": "\u0E44",
-    "/saraaimaimuanthai": "\u0E43",
-    "/saraamthai": "\u0E33",
-    "/saraathai": "\u0E30",
-    "/saraethai": "\u0E40",
-    "/saraiileftthai": "\uF886",
-    "/saraiithai": "\u0E35",
-    "/saraileftthai": "\uF885",
-    "/saraithai": "\u0E34",
-    "/saraothai": "\u0E42",
-    "/saraueeleftthai": "\uF888",
-    "/saraueethai": "\u0E37",
-    "/saraueleftthai": "\uF887",
-    "/sarauethai": "\u0E36",
-    "/sarauthai": "\u0E38",
-    "/sarauuthai": "\u0E39",
-    "/satellite": "\u1F6F0",
-    "/satelliteAntenna": "\u1F4E1",
-    "/saturn": "\u2644",
-    "/saxophone": "\u1F3B7",
-    "/sbopomofo": "\u3119",
-    "/scales": "\u2696",
-    "/scanninehorizontal": "\u23BD",
-    "/scanonehorizontal": "\u23BA",
-    "/scansevenhorizontal": "\u23BC",
-    "/scanthreehorizontal": "\u23BB",
-    "/scaron": "\u0161",
-    "/scarondot": "\u1E67",
-    "/scarondotaccent": "\u1E67",
-    "/scedilla": "\u015F",
-    "/school": "\u1F3EB",
-    "/schoolSatchel": "\u1F392",
-    "/schoolideographiccircled": "\u3246",
-    "/schwa": "\u0259",
-    "/schwa.inferior": "\u2094",
-    "/schwacyr": "\u04D9",
-    "/schwacyrillic": "\u04D9",
-    "/schwadieresiscyr": "\u04DB",
-    "/schwadieresiscyrillic": "\u04DB",
-    "/schwahook": "\u025A",
-    "/scircle": "\u24E2",
-    "/scircumflex": "\u015D",
-    "/scommaaccent": "\u0219",
-    "/scooter": "\u1F6F4",
-    "/scorpius": "\u264F",
-    "/screen": "\u1F5B5",
-    "/scroll": "\u1F4DC",
-    "/scruple": "\u2108",
-    "/sdot": "\u1E61",
-    "/sdotaccent": "\u1E61",
-    "/sdotbelow": "\u1E63",
-    "/sdotbelowdotabove": "\u1E69",
-    "/sdotbelowdotaccent": "\u1E69",
-    "/seagullbelowcmb": "\u033C",
-    "/seat": "\u1F4BA",
-    "/secirclekatakana": "\u32DD",
-    "/second": "\u2033",
-    "/secondreversed": "\u2036",
-    "/secondscreensquare": "\u1F19C",
-    "/secondtonechinese": "\u02CA",
-    "/secretideographiccircled": "\u3299",
-    "/section": "\u00A7",
-    "/sectionsignhalftop": "\u2E39",
-    "/sector": "\u2314",
-    "/seeNoEvilMonkey": "\u1F648",
-    "/seedling": "\u1F331",
-    "/seen": "\u0633",
-    "/seen.fina": "\uFEB2",
-    "/seen.init": "\uFEB3",
-    "/seen.init_alefmaksura.fina": "\uFCFB",
-    "/seen.init_hah.fina": "\uFC1D",
-    "/seen.init_hah.medi": "\uFCAE",
-    "/seen.init_hah.medi_jeem.medi": "\uFD5C",
-    "/seen.init_heh.medi": "\uFD31",
-    "/seen.init_jeem.fina": "\uFC1C",
-    "/seen.init_jeem.medi": "\uFCAD",
-    "/seen.init_jeem.medi_hah.medi": "\uFD5D",
-    "/seen.init_khah.fina": "\uFC1E",
-    "/seen.init_khah.medi": "\uFCAF",
-    "/seen.init_meem.fina": "\uFC1F",
-    "/seen.init_meem.medi": "\uFCB0",
-    "/seen.init_meem.medi_hah.medi": "\uFD60",
-    "/seen.init_meem.medi_jeem.medi": "\uFD61",
-    "/seen.init_meem.medi_meem.medi": "\uFD63",
-    "/seen.init_reh.fina": "\uFD0E",
-    "/seen.init_yeh.fina": "\uFCFC",
-    "/seen.isol": "\uFEB1",
-    "/seen.medi": "\uFEB4",
-    "/seen.medi_alefmaksura.fina": "\uFD17",
-    "/seen.medi_hah.medi": "\uFD35",
-    "/seen.medi_heh.medi": "\uFCE8",
-    "/seen.medi_jeem.medi": "\uFD34",
-    "/seen.medi_jeem.medi_alefmaksura.fina": "\uFD5E",
-    "/seen.medi_khah.medi": "\uFD36",
-    "/seen.medi_khah.medi_alefmaksura.fina": "\uFDA8",
-    "/seen.medi_khah.medi_yeh.fina": "\uFDC6",
-    "/seen.medi_meem.medi": "\uFCE7",
-    "/seen.medi_meem.medi_hah.fina": "\uFD5F",
-    "/seen.medi_meem.medi_meem.fina": "\uFD62",
-    "/seen.medi_reh.fina": "\uFD2A",
-    "/seen.medi_yeh.fina": "\uFD18",
-    "/seenDigitFourAbove": "\u077D",
-    "/seenFourDotsAbove": "\u075C",
-    "/seenInvertedV": "\u077E",
-    "/seenSmallTahTwoDots": "\u0770",
-    "/seenTwoDotsVerticallyAbove": "\u076D",
-    "/seenabove": "\u06DC",
-    "/seenarabic": "\u0633",
-    "/seendotbelowdotabove": "\u069A",
-    "/seenfinalarabic": "\uFEB2",
-    "/seeninitialarabic": "\uFEB3",
-    "/seenlow": "\u06E3",
-    "/seenmedialarabic": "\uFEB4",
-    "/seenthreedotsbelow": "\u069B",
-    "/seenthreedotsbelowthreedotsabove": "\u069C",
-    "/segment": "\u2313",
-    "/segol": "\u05B6",
-    "/segol13": "\u05B6",
-    "/segol1f": "\u05B6",
-    "/segol2c": "\u05B6",
-    "/segol:hb": "\u05B6",
-    "/segolhebrew": "\u05B6",
-    "/segolnarrowhebrew": "\u05B6",
-    "/segolquarterhebrew": "\u05B6",
-    "/segolta:hb": "\u0592",
-    "/segoltahebrew": "\u0592",
-    "/segolwidehebrew": "\u05B6",
-    "/seharmenian": "\u057D",
-    "/sehiragana": "\u305B",
-    "/sekatakana": "\u30BB",
-    "/sekatakanahalfwidth": "\uFF7E",
-    "/selfideographicparen": "\u3242",
-    "/semicolon": "\u003B",
-    "/semicolonarabic": "\u061B",
-    "/semicolonmonospace": "\uFF1B",
-    "/semicolonreversed": "\u204F",
-    "/semicolonsmall": "\uFE54",
-    "/semicolonunderlinefunc": "\u236E",
-    "/semidirectproductleft": "\u22CB",
-    "/semidirectproductright": "\u22CC",
-    "/semisextile": "\u26BA",
-    "/semisoftcyr": "\u048D",
-    "/semivoicedmarkkana": "\u309C",
-    "/semivoicedmarkkanahalfwidth": "\uFF9F",
-    "/sentisquare": "\u3322",
-    "/sentosquare": "\u3323",
-    "/septembertelegraph": "\u32C8",
-    "/sersetdblup": "\u22D1",
-    "/sersetnotequalup": "\u228B",
-    "/servicemark": "\u2120",
-    "/sesamedot": "\uFE45",
-    "/sesquiquadrate": "\u26BC",
-    "/setminus": "\u2216",
-    "/seven": "\u0037",
-    "/seven.inferior": "\u2087",
-    "/seven.roman": "\u2166",
-    "/seven.romansmall": "\u2176",
-    "/seven.superior": "\u2077",
-    "/sevenarabic": "\u0667",
-    "/sevenbengali": "\u09ED",
-    "/sevencircle": "\u2466",
-    "/sevencircledbl": "\u24FB",
-    "/sevencircleinversesansserif": "\u2790",
-    "/sevencomma": "\u1F108",
-    "/sevendeva": "\u096D",
-    "/seveneighths": "\u215E",
-    "/sevenfar": "\u06F7",
-    "/sevengujarati": "\u0AED",
-    "/sevengurmukhi": "\u0A6D",
-    "/sevenhackarabic": "\u0667",
-    "/sevenhangzhou": "\u3027",
-    "/sevenideographiccircled": "\u3286",
-    "/sevenideographicparen": "\u3226",
-    "/seveninferior": "\u2087",
-    "/sevenmonospace": "\uFF17",
-    "/sevenoldstyle": "\uF737",
-    "/sevenparen": "\u247A",
-    "/sevenparenthesized": "\u247A",
-    "/sevenperiod": "\u248E",
-    "/sevenpersian": "\u06F7",
-    "/sevenpointonesquare": "\u1F1A1",
-    "/sevenroman": "\u2176",
-    "/sevensuperior": "\u2077",
-    "/seventeencircle": "\u2470",
-    "/seventeencircleblack": "\u24F1",
-    "/seventeenparen": "\u2484",
-    "/seventeenparenthesized": "\u2484",
-    "/seventeenperiod": "\u2498",
-    "/seventhai": "\u0E57",
-    "/seventycirclesquare": "\u324E",
-    "/sextile": "\u26B9",
-    "/sfthyphen": "\u00AD",
-    "/shaarmenian": "\u0577",
-    "/shabengali": "\u09B6",
-    "/shacyr": "\u0448",
-    "/shacyrillic": "\u0448",
-    "/shaddaAlefIsol": "\uFC63",
-    "/shaddaDammaIsol": "\uFC61",
-    "/shaddaDammaMedi": "\uFCF3",
-    "/shaddaDammatanIsol": "\uFC5E",
-    "/shaddaFathaIsol": "\uFC60",
-    "/shaddaFathaMedi": "\uFCF2",
-    "/shaddaIsol": "\uFE7C",
-    "/shaddaKasraIsol": "\uFC62",
-    "/shaddaKasraMedi": "\uFCF4",
-    "/shaddaKasratanIsol": "\uFC5F",
-    "/shaddaMedi": "\uFE7D",
-    "/shaddaarabic": "\u0651",
-    "/shaddadammaarabic": "\uFC61",
-    "/shaddadammatanarabic": "\uFC5E",
-    "/shaddafathaarabic": "\uFC60",
-    "/shaddafathatanarabic": "\u0651",
-    "/shaddakasraarabic": "\uFC62",
-    "/shaddakasratanarabic": "\uFC5F",
-    "/shade": "\u2592",
-    "/shadedark": "\u2593",
-    "/shadelight": "\u2591",
-    "/shademedium": "\u2592",
-    "/shadeva": "\u0936",
-    "/shagujarati": "\u0AB6",
-    "/shagurmukhi": "\u0A36",
-    "/shalshelet:hb": "\u0593",
-    "/shalshelethebrew": "\u0593",
-    "/shamrock": "\u2618",
-    "/shavedIce": "\u1F367",
-    "/shbopomofo": "\u3115",
-    "/shchacyr": "\u0449",
-    "/shchacyrillic": "\u0449",
-    "/sheen": "\u0634",
-    "/sheen.fina": "\uFEB6",
-    "/sheen.init": "\uFEB7",
-    "/sheen.init_alefmaksura.fina": "\uFCFD",
-    "/sheen.init_hah.fina": "\uFD0A",
-    "/sheen.init_hah.medi": "\uFD2E",
-    "/sheen.init_hah.medi_meem.medi": "\uFD68",
-    "/sheen.init_heh.medi": "\uFD32",
-    "/sheen.init_jeem.fina": "\uFD09",
-    "/sheen.init_jeem.medi": "\uFD2D",
-    "/sheen.init_khah.fina": "\uFD0B",
-    "/sheen.init_khah.medi": "\uFD2F",
-    "/sheen.init_meem.fina": "\uFD0C",
-    "/sheen.init_meem.medi": "\uFD30",
-    "/sheen.init_meem.medi_khah.medi": "\uFD6B",
-    "/sheen.init_meem.medi_meem.medi": "\uFD6D",
-    "/sheen.init_reh.fina": "\uFD0D",
-    "/sheen.init_yeh.fina": "\uFCFE",
-    "/sheen.isol": "\uFEB5",
-    "/sheen.medi": "\uFEB8",
-    "/sheen.medi_alefmaksura.fina": "\uFD19",
-    "/sheen.medi_hah.fina": "\uFD26",
-    "/sheen.medi_hah.medi": "\uFD38",
-    "/sheen.medi_hah.medi_meem.fina": "\uFD67",
-    "/sheen.medi_hah.medi_yeh.fina": "\uFDAA",
-    "/sheen.medi_heh.medi": "\uFCEA",
-    "/sheen.medi_jeem.fina": "\uFD25",
-    "/sheen.medi_jeem.medi": "\uFD37",
-    "/sheen.medi_jeem.medi_yeh.fina": "\uFD69",
-    "/sheen.medi_khah.fina": "\uFD27",
-    "/sheen.medi_khah.medi": "\uFD39",
-    "/sheen.medi_meem.fina": "\uFD28",
-    "/sheen.medi_meem.medi": "\uFCE9",
-    "/sheen.medi_meem.medi_khah.fina": "\uFD6A",
-    "/sheen.medi_meem.medi_meem.fina": "\uFD6C",
-    "/sheen.medi_reh.fina": "\uFD29",
-    "/sheen.medi_yeh.fina": "\uFD1A",
-    "/sheenarabic": "\u0634",
-    "/sheendotbelow": "\u06FA",
-    "/sheenfinalarabic": "\uFEB6",
-    "/sheeninitialarabic": "\uFEB7",
-    "/sheenmedialarabic": "\uFEB8",
-    "/sheep": "\u1F411",
-    "/sheicoptic": "\u03E3",
-    "/shelfmod": "\u02FD",
-    "/shelfopenmod": "\u02FE",
-    "/sheqel": "\u20AA",
-    "/sheqelhebrew": "\u20AA",
-    "/sheva": "\u05B0",
-    "/sheva115": "\u05B0",
-    "/sheva15": "\u05B0",
-    "/sheva22": "\u05B0",
-    "/sheva2e": "\u05B0",
-    "/sheva:hb": "\u05B0",
-    "/shevahebrew": "\u05B0",
-    "/shevanarrowhebrew": "\u05B0",
-    "/shevaquarterhebrew": "\u05B0",
-    "/shevawidehebrew": "\u05B0",
-    "/shhacyr": "\u04BB",
-    "/shhacyrillic": "\u04BB",
-    "/shhatailcyr": "\u0527",
-    "/shield": "\u1F6E1",
-    "/shimacoptic": "\u03ED",
-    "/shin": "\u05E9",
-    "/shin:hb": "\u05E9",
-    "/shinDot:hb": "\u05C1",
-    "/shindagesh": "\uFB49",
-    "/shindageshhebrew": "\uFB49",
-    "/shindageshshindot": "\uFB2C",
-    "/shindageshshindothebrew": "\uFB2C",
-    "/shindageshsindot": "\uFB2D",
-    "/shindageshsindothebrew": "\uFB2D",
-    "/shindothebrew": "\u05C1",
-    "/shinhebrew": "\u05E9",
-    "/shinshindot": "\uFB2A",
-    "/shinshindothebrew": "\uFB2A",
-    "/shinsindot": "\uFB2B",
-    "/shinsindothebrew": "\uFB2B",
-    "/shintoshrine": "\u26E9",
-    "/shinwithdagesh:hb": "\uFB49",
-    "/shinwithdageshandshinDot:hb": "\uFB2C",
-    "/shinwithdageshandsinDot:hb": "\uFB2D",
-    "/shinwithshinDot:hb": "\uFB2A",
-    "/shinwithsinDot:hb": "\uFB2B",
-    "/ship": "\u1F6A2",
-    "/sho": "\u03F8",
-    "/shoejotupfunc": "\u235D",
-    "/shoestiledownfunc": "\u2366",
-    "/shoestileleftfunc": "\u2367",
-    "/shogipieceblack": "\u2617",
-    "/shogipiecewhite": "\u2616",
-    "/shook": "\u0282",
-    "/shootingStar": "\u1F320",
-    "/shoppingBags": "\u1F6CD",
-    "/shoppingTrolley": "\u1F6D2",
-    "/shortcake": "\u1F370",
-    "/shortequalsmod": "\uA78A",
-    "/shortoverlongmetrical": "\u23D3",
-    "/shoulderedopenbox": "\u237D",
-    "/shower": "\u1F6BF",
-    "/shvsquare": "\u1F1AA",
-    "/sicirclekatakana": "\u32DB",
-    "/sidewaysBlackDownPointingIndex": "\u1F5A1",
-    "/sidewaysBlackLeftPointingIndex": "\u1F59A",
-    "/sidewaysBlackRightPointingIndex": "\u1F59B",
-    "/sidewaysBlackUpPointingIndex": "\u1F5A0",
-    "/sidewaysWhiteDownPointingIndex": "\u1F59F",
-    "/sidewaysWhiteLeftPointingIndex": "\u1F598",
-    "/sidewaysWhiteRightPointingIndex": "\u1F599",
-    "/sidewaysWhiteUpPointingIndex": "\u1F59E",
-    "/sigma": "\u03C3",
-    "/sigma1": "\u03C2",
-    "/sigmafinal": "\u03C2",
-    "/sigmalunatedottedreversedsymbol": "\u037D",
-    "/sigmalunatedottedsymbol": "\u037C",
-    "/sigmalunatereversedsymbol": "\u037B",
-    "/sigmalunatesymbol": "\u03F2",
-    "/sigmalunatesymbolgreek": "\u03F2",
-    "/sihiragana": "\u3057",
-    "/sikatakana": "\u30B7",
-    "/sikatakanahalfwidth": "\uFF7C",
-    "/silhouetteOfJapan": "\u1F5FE",
-    "/siluqhebrew": "\u05BD",
-    "/siluqlefthebrew": "\u05BD",
-    "/similar": "\u223C",
-    "/sinDot:hb": "\u05C2",
-    "/sindothebrew": "\u05C2",
-    "/sinewave": "\u223F",
-    "/sinh:a": "\u0D85",
-    "/sinh:aa": "\u0D86",
-    "/sinh:aae": "\u0D88",
-    "/sinh:aaesign": "\u0DD1",
-    "/sinh:aasign": "\u0DCF",
-    "/sinh:ae": "\u0D87",
-    "/sinh:aesign": "\u0DD0",
-    "/sinh:ai": "\u0D93",
-    "/sinh:aisign": "\u0DDB",
-    "/sinh:anusvara": "\u0D82",
-    "/sinh:au": "\u0D96",
-    "/sinh:ausign": "\u0DDE",
-    "/sinh:ba": "\u0DB6",
-    "/sinh:bha": "\u0DB7",
-    "/sinh:ca": "\u0DA0",
-    "/sinh:cha": "\u0DA1",
-    "/sinh:da": "\u0DAF",
-    "/sinh:dda": "\u0DA9",
-    "/sinh:ddha": "\u0DAA",
-    "/sinh:dha": "\u0DB0",
-    "/sinh:e": "\u0D91",
-    "/sinh:ee": "\u0D92",
-    "/sinh:eesign": "\u0DDA",
-    "/sinh:esign": "\u0DD9",
-    "/sinh:fa": "\u0DC6",
-    "/sinh:ga": "\u0D9C",
-    "/sinh:gha": "\u0D9D",
-    "/sinh:ha": "\u0DC4",
-    "/sinh:i": "\u0D89",
-    "/sinh:ii": "\u0D8A",
-    "/sinh:iisign": "\u0DD3",
-    "/sinh:isign": "\u0DD2",
-    "/sinh:ja": "\u0DA2",
-    "/sinh:jha": "\u0DA3",
-    "/sinh:jnya": "\u0DA5",
-    "/sinh:ka": "\u0D9A",
-    "/sinh:kha": "\u0D9B",
-    "/sinh:kunddaliya": "\u0DF4",
-    "/sinh:la": "\u0DBD",
-    "/sinh:litheight": "\u0DEE",
-    "/sinh:lithfive": "\u0DEB",
-    "/sinh:lithfour": "\u0DEA",
-    "/sinh:lithnine": "\u0DEF",
-    "/sinh:lithone": "\u0DE7",
-    "/sinh:lithseven": "\u0DED",
-    "/sinh:lithsix": "\u0DEC",
-    "/sinh:liththree": "\u0DE9",
-    "/sinh:lithtwo": "\u0DE8",
-    "/sinh:lithzero": "\u0DE6",
-    "/sinh:lla": "\u0DC5",
-    "/sinh:llvocal": "\u0D90",
-    "/sinh:llvocalsign": "\u0DF3",
-    "/sinh:lvocal": "\u0D8F",
-    "/sinh:lvocalsign": "\u0DDF",
-    "/sinh:ma": "\u0DB8",
-    "/sinh:mba": "\u0DB9",
-    "/sinh:na": "\u0DB1",
-    "/sinh:nda": "\u0DB3",
-    "/sinh:nga": "\u0D9E",
-    "/sinh:nna": "\u0DAB",
-    "/sinh:nndda": "\u0DAC",
-    "/sinh:nnga": "\u0D9F",
-    "/sinh:nya": "\u0DA4",
-    "/sinh:nyja": "\u0DA6",
-    "/sinh:o": "\u0D94",
-    "/sinh:oo": "\u0D95",
-    "/sinh:oosign": "\u0DDD",
-    "/sinh:osign": "\u0DDC",
-    "/sinh:pa": "\u0DB4",
-    "/sinh:pha": "\u0DB5",
-    "/sinh:ra": "\u0DBB",
-    "/sinh:rrvocal": "\u0D8E",
-    "/sinh:rrvocalsign": "\u0DF2",
-    "/sinh:rvocal": "\u0D8D",
-    "/sinh:rvocalsign": "\u0DD8",
-    "/sinh:sa": "\u0DC3",
-    "/sinh:sha": "\u0DC1",
-    "/sinh:ssa": "\u0DC2",
-    "/sinh:ta": "\u0DAD",
-    "/sinh:tha": "\u0DAE",
-    "/sinh:tta": "\u0DA7",
-    "/sinh:ttha": "\u0DA8",
-    "/sinh:u": "\u0D8B",
-    "/sinh:usign": "\u0DD4",
-    "/sinh:uu": "\u0D8C",
-    "/sinh:uusign": "\u0DD6",
-    "/sinh:va": "\u0DC0",
-    "/sinh:virama": "\u0DCA",
-    "/sinh:visarga": "\u0D83",
-    "/sinh:ya": "\u0DBA",
-    "/sinologicaldot": "\uA78F",
-    "/sinsular": "\uA785",
-    "/siosacirclekorean": "\u3274",
-    "/siosaparenkorean": "\u3214",
-    "/sioscieuckorean": "\u317E",
-    "/sioscirclekorean": "\u3266",
-    "/sioskiyeokkorean": "\u317A",
-    "/sioskorean": "\u3145",
-    "/siosnieunkorean": "\u317B",
-    "/siosparenkorean": "\u3206",
-    "/siospieupkorean": "\u317D",
-    "/siostikeutkorean": "\u317C",
-    "/siringusquare": "\u3321",
-    "/six": "\u0036",
-    "/six.inferior": "\u2086",
-    "/six.roman": "\u2165",
-    "/six.romansmall": "\u2175",
-    "/six.superior": "\u2076",
-    "/sixPointedStarMiddleDot": "\u1F52F",
-    "/sixarabic": "\u0666",
-    "/sixbengali": "\u09EC",
-    "/sixcircle": "\u2465",
-    "/sixcircledbl": "\u24FA",
-    "/sixcircleinversesansserif": "\u278F",
-    "/sixcomma": "\u1F107",
-    "/sixdeva": "\u096C",
-    "/sixdotsvertical": "\u2E3D",
-    "/sixfar": "\u06F6",
-    "/sixgujarati": "\u0AEC",
-    "/sixgurmukhi": "\u0A6C",
-    "/sixhackarabic": "\u0666",
-    "/sixhangzhou": "\u3026",
-    "/sixideographiccircled": "\u3285",
-    "/sixideographicparen": "\u3225",
-    "/sixinferior": "\u2086",
-    "/sixlateform.roman": "\u2185",
-    "/sixmonospace": "\uFF16",
-    "/sixoldstyle": "\uF736",
-    "/sixparen": "\u2479",
-    "/sixparenthesized": "\u2479",
-    "/sixperemspace": "\u2006",
-    "/sixperiod": "\u248D",
-    "/sixpersian": "\u06F6",
-    "/sixroman": "\u2175",
-    "/sixsuperior": "\u2076",
-    "/sixteencircle": "\u246F",
-    "/sixteencircleblack": "\u24F0",
-    "/sixteencurrencydenominatorbengali": "\u09F9",
-    "/sixteenparen": "\u2483",
-    "/sixteenparenthesized": "\u2483",
-    "/sixteenperiod": "\u2497",
-    "/sixthai": "\u0E56",
-    "/sixtycirclesquare": "\u324D",
-    "/sixtypsquare": "\u1F1A3",
-    "/sjekomicyr": "\u050D",
-    "/skiAndSkiBoot": "\u1F3BF",
-    "/skier": "\u26F7",
-    "/skull": "\u1F480",
-    "/skullcrossbones": "\u2620",
-    "/slash": "\u002F",
-    "/slashbarfunc": "\u233F",
-    "/slashmonospace": "\uFF0F",
-    "/sled": "\u1F6F7",
-    "/sleeping": "\u1F4A4",
-    "/sleepingAccommodation": "\u1F6CC",
-    "/sleepingFace": "\u1F634",
-    "/sleepyFace": "\u1F62A",
-    "/sleuthOrSpy": "\u1F575",
-    "/sliceOfPizza": "\u1F355",
-    "/slightlyFrowningFace": "\u1F641",
-    "/slightlySmilingFace": "\u1F642",
-    "/slong": "\u017F",
-    "/slongdotaccent": "\u1E9B",
-    "/slope": "\u2333",
-    "/slotMachine": "\u1F3B0",
-    "/smallAirplane": "\u1F6E9",
-    "/smallBlueDiamond": "\u1F539",
-    "/smallOrangeDiamond": "\u1F538",
-    "/smallRedTriangleDOwn": "\u1F53D",
-    "/smallRedTriangleUp": "\u1F53C",
-    "/smile": "\u2323",
-    "/smileface": "\u263A",
-    "/smilingCatFaceWithHeartShapedEyes": "\u1F63B",
-    "/smilingCatFaceWithOpenMouth": "\u1F63A",
-    "/smilingFaceWithHalo": "\u1F607",
-    "/smilingFaceWithHeartShapedEyes": "\u1F60D",
-    "/smilingFaceWithHorns": "\u1F608",
-    "/smilingFaceWithOpenMouth": "\u1F603",
-    "/smilingFaceWithOpenMouthAndColdSweat": "\u1F605",
-    "/smilingFaceWithOpenMouthAndSmilingEyes": "\u1F604",
-    "/smilingFaceWithOpenMouthAndTightlyClosedEyes": "\u1F606",
-    "/smilingFaceWithSmilingEyes": "\u1F60A",
-    "/smilingFaceWithSunglasses": "\u1F60E",
-    "/smilingfaceblack": "\u263B",
-    "/smilingfacewhite": "\u263A",
-    "/smirkingFace": "\u1F60F",
-    "/smll:ampersand": "\uFE60",
-    "/smll:asterisk": "\uFE61",
-    "/smll:backslash": "\uFE68",
-    "/smll:braceleft": "\uFE5B",
-    "/smll:braceright": "\uFE5C",
-    "/smll:colon": "\uFE55",
-    "/smll:comma": "\uFE50",
-    "/smll:dollar": "\uFE69",
-    "/smll:emdash": "\uFE58",
-    "/smll:equal": "\uFE66",
-    "/smll:exclam": "\uFE57",
-    "/smll:greater": "\uFE65",
-    "/smll:hyphen": "\uFE63",
-    "/smll:ideographiccomma": "\uFE51",
-    "/smll:less": "\uFE64",
-    "/smll:numbersign": "\uFE5F",
-    "/smll:parenthesisleft": "\uFE59",
-    "/smll:parenthesisright": "\uFE5A",
-    "/smll:percent": "\uFE6A",
-    "/smll:period": "\uFE52",
-    "/smll:plus": "\uFE62",
-    "/smll:question": "\uFE56",
-    "/smll:semicolon": "\uFE54",
-    "/smll:tortoiseshellbracketleft": "\uFE5D",
-    "/smll:tortoiseshellbracketright": "\uFE5E",
-    "/smoking": "\u1F6AC",
-    "/smonospace": "\uFF53",
-    "/snail": "\u1F40C",
-    "/snake": "\u1F40D",
-    "/snowboarder": "\u1F3C2",
-    "/snowcappedMountain": "\u1F3D4",
-    "/snowman": "\u2603",
-    "/snowmanblack": "\u26C7",
-    "/snowmanoutsnow": "\u26C4",
-    "/sobliquestroke": "\uA7A9",
-    "/soccerball": "\u26BD",
-    "/societyideographiccircled": "\u3293",
-    "/societyideographicparen": "\u3233",
-    "/socirclekatakana": "\u32DE",
-    "/sofPasuq:hb": "\u05C3",
-    "/sofpasuqhebrew": "\u05C3",
-    "/softIceCream": "\u1F366",
-    "/softShellFloppyDisk": "\u1F5AC",
-    "/softcyr": "\u044C",
-    "/softhyphen": "\u00AD",
-    "/softsigncyrillic": "\u044C",
-    "/softwarefunction": "\u2394",
-    "/sohiragana": "\u305D",
-    "/sokatakana": "\u30BD",
-    "/sokatakanahalfwidth": "\uFF7F",
-    "/soliduslongoverlaycmb": "\u0338",
-    "/solidusshortoverlaycmb": "\u0337",
-    "/solidussubsetreversepreceding": "\u27C8",
-    "/solidussupersetpreceding": "\u27C9",
-    "/soonRightwardsArrowAbove": "\u1F51C",
-    "/sorusithai": "\u0E29",
-    "/sosalathai": "\u0E28",
-    "/sosothai": "\u0E0B",
-    "/sossquare": "\u1F198",
-    "/sosuathai": "\u0E2A",
-    "/soundcopyright": "\u2117",
-    "/space": "\u0020",
-    "/spacehackarabic": "\u0020",
-    "/spade": "\u2660",
-    "/spadeblack": "\u2660",
-    "/spadesuitblack": "\u2660",
-    "/spadesuitwhite": "\u2664",
-    "/spadewhite": "\u2664",
-    "/spaghetti": "\u1F35D",
-    "/sparen": "\u24AE",
-    "/sparenthesized": "\u24AE",
-    "/sparklingHeart": "\u1F496",
-    "/speakNoEvilMonkey": "\u1F64A",
-    "/speaker": "\u1F508",
-    "/speakerCancellationStroke": "\u1F507",
-    "/speakerOneSoundWave": "\u1F509",
-    "/speakerThreeSoundWaves": "\u1F50A",
-    "/speakingHeadInSilhouette": "\u1F5E3",
-    "/specialideographiccircled": "\u3295",
-    "/specialideographicparen": "\u3235",
-    "/speechBalloon": "\u1F4AC",
-    "/speedboat": "\u1F6A4",
-    "/spesmilo": "\u20B7",
-    "/sphericalangle": "\u2222",
-    "/spider": "\u1F577",
-    "/spiderWeb": "\u1F578",
-    "/spiralCalendarPad": "\u1F5D3",
-    "/spiralNotePad": "\u1F5D2",
-    "/spiralShell": "\u1F41A",
-    "/splashingSweat": "\u1F4A6",
-    "/sportsMedal": "\u1F3C5",
-    "/spoutingWhale": "\u1F433",
-    "/sppl:tildevertical": "\u2E2F",
-    "/squarebelowcmb": "\u033B",
-    "/squareblack": "\u25A0",
-    "/squarebracketleftvertical": "\uFE47",
-    "/squarebracketrightvertical": "\uFE48",
-    "/squarecap": "\u2293",
-    "/squarecc": "\u33C4",
-    "/squarecm": "\u339D",
-    "/squarecup": "\u2294",
-    "/squareddotoperator": "\u22A1",
-    "/squarediagonalcrosshatchfill": "\u25A9",
-    "/squaredj": "\u1F190",
-    "/squaredkey": "\u26BF",
-    "/squaredminus": "\u229F",
-    "/squaredplus": "\u229E",
-    "/squaredsaltire": "\u26DD",
-    "/squaredtimes": "\u22A0",
-    "/squarefourcorners": "\u26F6",
-    "/squarehalfleftblack": "\u25E7",
-    "/squarehalfrightblack": "\u25E8",
-    "/squarehorizontalfill": "\u25A4",
-    "/squareimage": "\u228F",
-    "/squareimageorequal": "\u2291",
-    "/squareimageornotequal": "\u22E4",
-    "/squarekg": "\u338F",
-    "/squarekm": "\u339E",
-    "/squarekmcapital": "\u33CE",
-    "/squareln": "\u33D1",
-    "/squarelog": "\u33D2",
-    "/squarelowerdiagonalhalfrightblack": "\u25EA",
-    "/squaremediumblack": "\u25FC",
-    "/squaremediumwhite": "\u25FB",
-    "/squaremg": "\u338E",
-    "/squaremil": "\u33D5",
-    "/squaremm": "\u339C",
-    "/squaremsquared": "\u33A1",
-    "/squareoriginal": "\u2290",
-    "/squareoriginalorequal": "\u2292",
-    "/squareoriginalornotequal": "\u22E5",
-    "/squareorthogonalcrosshatchfill": "\u25A6",
-    "/squareraised": "\u2E0B",
-    "/squaresmallblack": "\u25AA",
-    "/squaresmallmediumblack": "\u25FE",
-    "/squaresmallmediumwhite": "\u25FD",
-    "/squaresmallwhite": "\u25AB",
-    "/squareupperdiagonalhalfleftblack": "\u25E9",
-    "/squareupperlefttolowerrightfill": "\u25A7",
-    "/squareupperrighttolowerleftfill": "\u25A8",
-    "/squareverticalfill": "\u25A5",
-    "/squarewhite": "\u25A1",
-    "/squarewhitebisectinglinevertical": "\u25EB",
-    "/squarewhitelowerquadrantleft": "\u25F1",
-    "/squarewhitelowerquadrantright": "\u25F2",
-    "/squarewhiteround": "\u25A2",
-    "/squarewhiteupperquadrantleft": "\u25F0",
-    "/squarewhiteupperquadrantright": "\u25F3",
-    "/squarewhitewithsmallblack": "\u25A3",
-    "/squarewhitewithsquaresmallblack": "\u25A3",
-    "/squishquadfunc": "\u2337",
-    "/srfullwidth": "\u33DB",
-    "/srsquare": "\u33DB",
-    "/ssabengali": "\u09B7",
-    "/ssadeva": "\u0937",
-    "/ssagujarati": "\u0AB7",
-    "/ssangcieuckorean": "\u3149",
-    "/ssanghieuhkorean": "\u3185",
-    "/ssangieungkorean": "\u3180",
-    "/ssangkiyeokkorean": "\u3132",
-    "/ssangnieunkorean": "\u3165",
-    "/ssangpieupkorean": "\u3143",
-    "/ssangsioskorean": "\u3146",
-    "/ssangtikeutkorean": "\u3138",
-    "/ssuperior": "\uF6F2",
-    "/ssupmod": "\u02E2",
-    "/sswashtail": "\u023F",
-    "/stackedcommadbl": "\u2E49",
-    "/stadium": "\u1F3DF",
-    "/staffofaesculapius": "\u2695",
-    "/staffofhermes": "\u269A",
-    "/stampedEnvelope": "\u1F583",
-    "/star": "\u22C6",
-    "/starblack": "\u2605",
-    "/starcrescent": "\u262A",
-    "/stardiaeresisfunc": "\u2363",
-    "/starequals": "\u225B",
-    "/staroperator": "\u22C6",
-    "/staroutlinedwhite": "\u269D",
-    "/starwhite": "\u2606",
-    "/station": "\u1F689",
-    "/statueOfLiberty": "\u1F5FD",
-    "/steamLocomotive": "\u1F682",
-    "/steamingBowl": "\u1F35C",
-    "/stenographicfullstop": "\u2E3C",
-    "/sterling": "\u00A3",
-    "/sterlingmonospace": "\uFFE1",
-    "/stigma": "\u03DB",
-    "/stiletildefunc": "\u236D",
-    "/stockChart": "\u1F5E0",
-    "/stockideographiccircled": "\u3291",
-    "/stockideographicparen": "\u3231",
-    "/stopabove": "\u06EB",
-    "/stopbelow": "\u06EA",
-    "/straightRuler": "\u1F4CF",
-    "/straightness": "\u23E4",
-    "/strawberry": "\u1F353",
-    "/stresslowtonemod": "\uA721",
-    "/stresstonemod": "\uA720",
-    "/strictlyequivalent": "\u2263",
-    "/strokelongoverlaycmb": "\u0336",
-    "/strokeshortoverlaycmb": "\u0335",
-    "/studioMicrophone": "\u1F399",
-    "/studyideographiccircled": "\u32AB",
-    "/studyideographicparen": "\u323B",
-    "/stupa": "\u1F6D3",
-    "/subscriptalef": "\u0656",
-    "/subset": "\u2282",
-    "/subsetdbl": "\u22D0",
-    "/subsetnotequal": "\u228A",
-    "/subsetorequal": "\u2286",
-    "/succeeds": "\u227B",
-    "/succeedsbutnotequivalent": "\u22E9",
-    "/succeedsorequal": "\u227D",
-    "/succeedsorequivalent": "\u227F",
-    "/succeedsunderrelation": "\u22B1",
-    "/suchthat": "\u220B",
-    "/sucirclekatakana": "\u32DC",
-    "/suhiragana": "\u3059",
-    "/suitableideographiccircled": "\u329C",
-    "/sukatakana": "\u30B9",
-    "/sukatakanahalfwidth": "\uFF7D",
-    "/sukumendutvowel": "\uA9B9",
-    "/sukunIsol": "\uFE7E",
-    "/sukunMedi": "\uFE7F",
-    "/sukunarabic": "\u0652",
-    "/sukuvowel": "\uA9B8",
-    "/summation": "\u2211",
-    "/summationbottom": "\u23B3",
-    "/summationdblstruck": "\u2140",
-    "/summationtop": "\u23B2",
-    "/sun": "\u263C",
-    "/sunFace": "\u1F31E",
-    "/sunbehindcloud": "\u26C5",
-    "/sunflower": "\u1F33B",
-    "/sunideographiccircled": "\u3290",
-    "/sunideographicparen": "\u3230",
-    "/sunraysblack": "\u2600",
-    "/sunrayswhite": "\u263C",
-    "/sunrise": "\u1F305",
-    "/sunriseOverMountains": "\u1F304",
-    "/sunsetOverBuildings": "\u1F307",
-    "/superset": "\u2283",
-    "/supersetnotequal": "\u228B",
-    "/supersetorequal": "\u2287",
-    "/superviseideographiccircled": "\u32AC",
-    "/superviseideographicparen": "\u323C",
-    "/surfer": "\u1F3C4",
-    "/sushi": "\u1F363",
-    "/suspensionRailway": "\u1F69F",
-    "/suspensiondbl": "\u2E44",
-    "/svfullwidth": "\u33DC",
-    "/svsquare": "\u33DC",
-    "/swatchtop": "\u23F1",
-    "/swimmer": "\u1F3CA",
-    "/swungdash": "\u2053",
-    "/symbolabovethreedotsabove": "\uFBB6",
-    "/symbolbelowthreedotsabove": "\uFBB7",
-    "/symboldotabove": "\uFBB2",
-    "/symboldotbelow": "\uFBB3",
-    "/symboldoubleverticalbarbelow": "\uFBBC",
-    "/symbolfourdotsabove": "\uFBBA",
-    "/symbolfourdotsbelow": "\uFBBB",
-    "/symbolpointingabovedownthreedotsabove": "\uFBB8",
-    "/symbolpointingbelowdownthreedotsabove": "\uFBB9",
-    "/symbolring": "\uFBBF",
-    "/symboltahabovesmall": "\uFBC0",
-    "/symboltahbelowsmall": "\uFBC1",
-    "/symboltwodotsabove": "\uFBB4",
-    "/symboltwodotsbelow": "\uFBB5",
-    "/symboltwodotsverticallyabove": "\uFBBD",
-    "/symboltwodotsverticallybelow": "\uFBBE",
-    "/symmetry": "\u232F",
-    "/synagogue": "\u1F54D",
-    "/syouwaerasquare": "\u337C",
-    "/syringe": "\u1F489",
-    "/t": "\u0074",
-    "/t-shirt": "\u1F455",
-    "/t.inferior": "\u209C",
-    "/tabengali": "\u09A4",
-    "/tableTennisPaddleAndBall": "\u1F3D3",
-    "/tacirclekatakana": "\u32DF",
-    "/tackcircleaboveup": "\u27DF",
-    "/tackdiaeresisupfunc": "\u2361",
-    "/tackdown": "\u22A4",
-    "/tackdownmod": "\u02D5",
-    "/tackjotdownfunc": "\u234E",
-    "/tackjotupfunc": "\u2355",
-    "/tackleft": "\u22A3",
-    "/tackleftright": "\u27DB",
-    "/tackoverbarupfunc": "\u2351",
-    "/tackright": "\u22A2",
-    "/tackunderlinedownfunc": "\u234A",
-    "/tackup": "\u22A5",
-    "/tackupmod": "\u02D4",
-    "/taco": "\u1F32E",
-    "/tadeva": "\u0924",
-    "/tagujarati": "\u0AA4",
-    "/tagurmukhi": "\u0A24",
-    "/tah": "\u0637",
-    "/tah.fina": "\uFEC2",
-    "/tah.init": "\uFEC3",
-    "/tah.init_alefmaksura.fina": "\uFCF5",
-    "/tah.init_hah.fina": "\uFC26",
-    "/tah.init_hah.medi": "\uFCB8",
-    "/tah.init_meem.fina": "\uFC27",
-    "/tah.init_meem.medi": "\uFD33",
-    "/tah.init_meem.medi_hah.medi": "\uFD72",
-    "/tah.init_meem.medi_meem.medi": "\uFD73",
-    "/tah.init_yeh.fina": "\uFCF6",
-    "/tah.isol": "\uFEC1",
-    "/tah.medi": "\uFEC4",
-    "/tah.medi_alefmaksura.fina": "\uFD11",
-    "/tah.medi_meem.medi": "\uFD3A",
-    "/tah.medi_meem.medi_hah.fina": "\uFD71",
-    "/tah.medi_meem.medi_yeh.fina": "\uFD74",
-    "/tah.medi_yeh.fina": "\uFD12",
-    "/tahabove": "\u0615",
-    "/taharabic": "\u0637",
-    "/tahfinalarabic": "\uFEC2",
-    "/tahinitialarabic": "\uFEC3",
-    "/tahiragana": "\u305F",
-    "/tahmedialarabic": "\uFEC4",
-    "/tahthreedotsabove": "\u069F",
-    "/taisyouerasquare": "\u337D",
-    "/takatakana": "\u30BF",
-    "/takatakanahalfwidth": "\uFF80",
-    "/takhallus": "\u0614",
-    "/talingvowel": "\uA9BA",
-    "/taml:a": "\u0B85",
-    "/taml:aa": "\u0B86",
-    "/taml:aasign": "\u0BBE",
-    "/taml:ai": "\u0B90",
-    "/taml:aisign": "\u0BC8",
-    "/taml:anusvarasign": "\u0B82",
-    "/taml:asabovesign": "\u0BF8",
-    "/taml:au": "\u0B94",
-    "/taml:aulengthmark": "\u0BD7",
-    "/taml:ausign": "\u0BCC",
-    "/taml:ca": "\u0B9A",
-    "/taml:creditsign": "\u0BF7",
-    "/taml:daysign": "\u0BF3",
-    "/taml:debitsign": "\u0BF6",
-    "/taml:e": "\u0B8E",
-    "/taml:ee": "\u0B8F",
-    "/taml:eesign": "\u0BC7",
-    "/taml:eight": "\u0BEE",
-    "/taml:esign": "\u0BC6",
-    "/taml:five": "\u0BEB",
-    "/taml:four": "\u0BEA",
-    "/taml:ha": "\u0BB9",
-    "/taml:i": "\u0B87",
-    "/taml:ii": "\u0B88",
-    "/taml:iisign": "\u0BC0",
-    "/taml:isign": "\u0BBF",
-    "/taml:ja": "\u0B9C",
-    "/taml:ka": "\u0B95",
-    "/taml:la": "\u0BB2",
-    "/taml:lla": "\u0BB3",
-    "/taml:llla": "\u0BB4",
-    "/taml:ma": "\u0BAE",
-    "/taml:monthsign": "\u0BF4",
-    "/taml:na": "\u0BA8",
-    "/taml:nga": "\u0B99",
-    "/taml:nine": "\u0BEF",
-    "/taml:nna": "\u0BA3",
-    "/taml:nnna": "\u0BA9",
-    "/taml:nya": "\u0B9E",
-    "/taml:o": "\u0B92",
-    "/taml:om": "\u0BD0",
-    "/taml:one": "\u0BE7",
-    "/taml:onehundred": "\u0BF1",
-    "/taml:onethousand": "\u0BF2",
-    "/taml:oo": "\u0B93",
-    "/taml:oosign": "\u0BCB",
-    "/taml:osign": "\u0BCA",
-    "/taml:pa": "\u0BAA",
-    "/taml:ra": "\u0BB0",
-    "/taml:rra": "\u0BB1",
-    "/taml:rupeesign": "\u0BF9",
-    "/taml:sa": "\u0BB8",
-    "/taml:seven": "\u0BED",
-    "/taml:sha": "\u0BB6",
-    "/taml:sign": "\u0BFA",
-    "/taml:six": "\u0BEC",
-    "/taml:ssa": "\u0BB7",
-    "/taml:ta": "\u0BA4",
-    "/taml:ten": "\u0BF0",
-    "/taml:three": "\u0BE9",
-    "/taml:tta": "\u0B9F",
-    "/taml:two": "\u0BE8",
-    "/taml:u": "\u0B89",
-    "/taml:usign": "\u0BC1",
-    "/taml:uu": "\u0B8A",
-    "/taml:uusign": "\u0BC2",
-    "/taml:va": "\u0BB5",
-    "/taml:viramasign": "\u0BCD",
-    "/taml:visargasign": "\u0B83",
-    "/taml:ya": "\u0BAF",
-    "/taml:yearsign": "\u0BF5",
-    "/taml:zero": "\u0BE6",
-    "/tamurda": "\uA9A1",
-    "/tanabataTree": "\u1F38B",
-    "/tangerine": "\u1F34A",
-    "/tapeCartridge": "\u1F5AD",
-    "/tarungvowel": "\uA9B4",
-    "/tatweelFathatanAbove": "\uFE71",
-    "/tatweelarabic": "\u0640",
-    "/tau": "\u03C4",
-    "/taurus": "\u2649",
-    "/tav": "\u05EA",
-    "/tav:hb": "\u05EA",
-    "/tavdages": "\uFB4A",
-    "/tavdagesh": "\uFB4A",
-    "/tavdageshhebrew": "\uFB4A",
-    "/tavhebrew": "\u05EA",
-    "/tavwide:hb": "\uFB28",
-    "/tavwithdagesh:hb": "\uFB4A",
-    "/taxi": "\u1F695",
-    "/tbar": "\u0167",
-    "/tbopomofo": "\u310A",
-    "/tcaron": "\u0165",
-    "/tccurl": "\u02A8",
-    "/tcedilla": "\u0163",
-    "/tcheh": "\u0686",
-    "/tcheh.fina": "\uFB7B",
-    "/tcheh.init": "\uFB7C",
-    "/tcheh.isol": "\uFB7A",
-    "/tcheh.medi": "\uFB7D",
-    "/tcheharabic": "\u0686",
-    "/tchehdotabove": "\u06BF",
-    "/tcheheh": "\u0687",
-    "/tcheheh.fina": "\uFB7F",
-    "/tcheheh.init": "\uFB80",
-    "/tcheheh.isol": "\uFB7E",
-    "/tcheheh.medi": "\uFB81",
-    "/tchehfinalarabic": "\uFB7B",
-    "/tchehinitialarabic": "\uFB7C",
-    "/tchehmedialarabic": "\uFB7D",
-    "/tchehmeeminitialarabic": "\uFB7C",
-    "/tcircle": "\u24E3",
-    "/tcircumflexbelow": "\u1E71",
-    "/tcommaaccent": "\u0163",
-    "/tcurl": "\u0236",
-    "/tdieresis": "\u1E97",
-    "/tdot": "\u1E6B",
-    "/tdotaccent": "\u1E6B",
-    "/tdotbelow": "\u1E6D",
-    "/teacupOutHandle": "\u1F375",
-    "/tear-offCalendar": "\u1F4C6",
-    "/tecirclekatakana": "\u32E2",
-    "/tecyr": "\u0442",
-    "/tecyrillic": "\u0442",
-    "/tedescendercyrillic": "\u04AD",
-    "/teh": "\u062A",
-    "/teh.fina": "\uFE96",
-    "/teh.init": "\uFE97",
-    "/teh.init_alefmaksura.fina": "\uFC0F",
-    "/teh.init_hah.fina": "\uFC0C",
-    "/teh.init_hah.medi": "\uFCA2",
-    "/teh.init_hah.medi_jeem.medi": "\uFD52",
-    "/teh.init_hah.medi_meem.medi": "\uFD53",
-    "/teh.init_heh.medi": "\uFCA5",
-    "/teh.init_jeem.fina": "\uFC0B",
-    "/teh.init_jeem.medi": "\uFCA1",
-    "/teh.init_jeem.medi_meem.medi": "\uFD50",
-    "/teh.init_khah.fina": "\uFC0D",
-    "/teh.init_khah.medi": "\uFCA3",
-    "/teh.init_khah.medi_meem.medi": "\uFD54",
-    "/teh.init_meem.fina": "\uFC0E",
-    "/teh.init_meem.medi": "\uFCA4",
-    "/teh.init_meem.medi_hah.medi": "\uFD56",
-    "/teh.init_meem.medi_jeem.medi": "\uFD55",
-    "/teh.init_meem.medi_khah.medi": "\uFD57",
-    "/teh.init_yeh.fina": "\uFC10",
-    "/teh.isol": "\uFE95",
-    "/teh.medi": "\uFE98",
-    "/teh.medi_alefmaksura.fina": "\uFC74",
-    "/teh.medi_hah.medi_jeem.fina": "\uFD51",
-    "/teh.medi_heh.medi": "\uFCE4",
-    "/teh.medi_jeem.medi_alefmaksura.fina": "\uFDA0",
-    "/teh.medi_jeem.medi_yeh.fina": "\uFD9F",
-    "/teh.medi_khah.medi_alefmaksura.fina": "\uFDA2",
-    "/teh.medi_khah.medi_yeh.fina": "\uFDA1",
-    "/teh.medi_meem.fina": "\uFC72",
-    "/teh.medi_meem.medi": "\uFCE3",
-    "/teh.medi_meem.medi_alefmaksura.fina": "\uFDA4",
-    "/teh.medi_meem.medi_yeh.fina": "\uFDA3",
-    "/teh.medi_noon.fina": "\uFC73",
-    "/teh.medi_reh.fina": "\uFC70",
-    "/teh.medi_yeh.fina": "\uFC75",
-    "/teh.medi_zain.fina": "\uFC71",
-    "/teharabic": "\u062A",
-    "/tehdownthreedotsabove": "\u067D",
-    "/teheh": "\u067F",
-    "/teheh.fina": "\uFB63",
-    "/teheh.init": "\uFB64",
-    "/teheh.isol": "\uFB62",
-    "/teheh.medi": "\uFB65",
-    "/tehfinalarabic": "\uFE96",
-    "/tehhahinitialarabic": "\uFCA2",
-    "/tehhahisolatedarabic": "\uFC0C",
-    "/tehinitialarabic": "\uFE97",
-    "/tehiragana": "\u3066",
-    "/tehjeeminitialarabic": "\uFCA1",
-    "/tehjeemisolatedarabic": "\uFC0B",
-    "/tehmarbuta": "\u0629",
-    "/tehmarbuta.fina": "\uFE94",
-    "/tehmarbuta.isol": "\uFE93",
-    "/tehmarbutaarabic": "\u0629",
-    "/tehmarbutafinalarabic": "\uFE94",
-    "/tehmarbutagoal": "\u06C3",
-    "/tehmedialarabic": "\uFE98",
-    "/tehmeeminitialarabic": "\uFCA4",
-    "/tehmeemisolatedarabic": "\uFC0E",
-    "/tehnoonfinalarabic": "\uFC73",
-    "/tehring": "\u067C",
-    "/tekatakana": "\u30C6",
-    "/tekatakanahalfwidth": "\uFF83",
-    "/telephone": "\u2121",
-    "/telephoneOnTopOfModem": "\u1F580",
-    "/telephoneReceiver": "\u1F4DE",
-    "/telephoneReceiverPage": "\u1F57C",
-    "/telephoneblack": "\u260E",
-    "/telephonerecorder": "\u2315",
-    "/telephonewhite": "\u260F",
-    "/telescope": "\u1F52D",
-    "/television": "\u1F4FA",
-    "/telishaGedolah:hb": "\u05A0",
-    "/telishaQetannah:hb": "\u05A9",
-    "/telishagedolahebrew": "\u05A0",
-    "/telishaqetanahebrew": "\u05A9",
-    "/telu:a": "\u0C05",
-    "/telu:aa": "\u0C06",
-    "/telu:aasign": "\u0C3E",
-    "/telu:ai": "\u0C10",
-    "/telu:ailengthmark": "\u0C56",
-    "/telu:aisign": "\u0C48",
-    "/telu:anusvarasign": "\u0C02",
-    "/telu:au": "\u0C14",
-    "/telu:ausign": "\u0C4C",
-    "/telu:avagrahasign": "\u0C3D",
-    "/telu:ba": "\u0C2C",
-    "/telu:bha": "\u0C2D",
-    "/telu:bindusigncandra": "\u0C01",
-    "/telu:ca": "\u0C1A",
-    "/telu:cha": "\u0C1B",
-    "/telu:combiningbinduabovesigncandra": "\u0C00",
-    "/telu:da": "\u0C26",
-    "/telu:dda": "\u0C21",
-    "/telu:ddha": "\u0C22",
-    "/telu:dha": "\u0C27",
-    "/telu:dza": "\u0C59",
-    "/telu:e": "\u0C0E",
-    "/telu:ee": "\u0C0F",
-    "/telu:eesign": "\u0C47",
-    "/telu:eight": "\u0C6E",
-    "/telu:esign": "\u0C46",
-    "/telu:five": "\u0C6B",
-    "/telu:four": "\u0C6A",
-    "/telu:fractiononeforevenpowersoffour": "\u0C7C",
-    "/telu:fractiononeforoddpowersoffour": "\u0C79",
-    "/telu:fractionthreeforevenpowersoffour": "\u0C7E",
-    "/telu:fractionthreeforoddpowersoffour": "\u0C7B",
-    "/telu:fractiontwoforevenpowersoffour": "\u0C7D",
-    "/telu:fractiontwoforoddpowersoffour": "\u0C7A",
-    "/telu:fractionzeroforoddpowersoffour": "\u0C78",
-    "/telu:ga": "\u0C17",
-    "/telu:gha": "\u0C18",
-    "/telu:ha": "\u0C39",
-    "/telu:i": "\u0C07",
-    "/telu:ii": "\u0C08",
-    "/telu:iisign": "\u0C40",
-    "/telu:isign": "\u0C3F",
-    "/telu:ja": "\u0C1C",
-    "/telu:jha": "\u0C1D",
-    "/telu:ka": "\u0C15",
-    "/telu:kha": "\u0C16",
-    "/telu:la": "\u0C32",
-    "/telu:lengthmark": "\u0C55",
-    "/telu:lla": "\u0C33",
-    "/telu:llla": "\u0C34",
-    "/telu:llsignvocal": "\u0C63",
-    "/telu:llvocal": "\u0C61",
-    "/telu:lsignvocal": "\u0C62",
-    "/telu:lvocal": "\u0C0C",
-    "/telu:ma": "\u0C2E",
-    "/telu:na": "\u0C28",
-    "/telu:nga": "\u0C19",
-    "/telu:nine": "\u0C6F",
-    "/telu:nna": "\u0C23",
-    "/telu:nya": "\u0C1E",
-    "/telu:o": "\u0C12",
-    "/telu:one": "\u0C67",
-    "/telu:oo": "\u0C13",
-    "/telu:oosign": "\u0C4B",
-    "/telu:osign": "\u0C4A",
-    "/telu:pa": "\u0C2A",
-    "/telu:pha": "\u0C2B",
-    "/telu:ra": "\u0C30",
-    "/telu:rra": "\u0C31",
-    "/telu:rrra": "\u0C5A",
-    "/telu:rrsignvocal": "\u0C44",
-    "/telu:rrvocal": "\u0C60",
-    "/telu:rsignvocal": "\u0C43",
-    "/telu:rvocal": "\u0C0B",
-    "/telu:sa": "\u0C38",
-    "/telu:seven": "\u0C6D",
-    "/telu:sha": "\u0C36",
-    "/telu:six": "\u0C6C",
-    "/telu:ssa": "\u0C37",
-    "/telu:ta": "\u0C24",
-    "/telu:tha": "\u0C25",
-    "/telu:three": "\u0C69",
-    "/telu:tsa": "\u0C58",
-    "/telu:tta": "\u0C1F",
-    "/telu:ttha": "\u0C20",
-    "/telu:tuumusign": "\u0C7F",
-    "/telu:two": "\u0C68",
-    "/telu:u": "\u0C09",
-    "/telu:usign": "\u0C41",
-    "/telu:uu": "\u0C0A",
-    "/telu:uusign": "\u0C42",
-    "/telu:va": "\u0C35",
-    "/telu:viramasign": "\u0C4D",
-    "/telu:visargasign": "\u0C03",
-    "/telu:ya": "\u0C2F",
-    "/telu:zero": "\u0C66",
-    "/ten.roman": "\u2169",
-    "/ten.romansmall": "\u2179",
-    "/tencircle": "\u2469",
-    "/tencircledbl": "\u24FE",
-    "/tencirclesquare": "\u3248",
-    "/tenge": "\u20B8",
-    "/tenhangzhou": "\u3038",
-    "/tenideographiccircled": "\u3289",
-    "/tenideographicparen": "\u3229",
-    "/tennisRacquetAndBall": "\u1F3BE",
-    "/tenparen": "\u247D",
-    "/tenparenthesized": "\u247D",
-    "/tenperiod": "\u2491",
-    "/tenroman": "\u2179",
-    "/tent": "\u26FA",
-    "/tenthousand.roman": "\u2182",
-    "/tesh": "\u02A7",
-    "/tet": "\u05D8",
-    "/tet:hb": "\u05D8",
-    "/tetailcyr": "\u04AD",
-    "/tetdagesh": "\uFB38",
-    "/tetdageshhebrew": "\uFB38",
-    "/tethebrew": "\u05D8",
-    "/tetrasememetrical": "\u23D8",
-    "/tetsecyr": "\u04B5",
-    "/tetsecyrillic": "\u04B5",
-    "/tetwithdagesh:hb": "\uFB38",
-    "/tevir:hb": "\u059B",
-    "/tevirhebrew": "\u059B",
-    "/tevirlefthebrew": "\u059B",
-    "/thabengali": "\u09A5",
-    "/thadeva": "\u0925",
-    "/thagujarati": "\u0AA5",
-    "/thagurmukhi": "\u0A25",
-    "/thai:angkhankhu": "\u0E5A",
-    "/thai:baht": "\u0E3F",
-    "/thai:bobaimai": "\u0E1A",
-    "/thai:chochan": "\u0E08",
-    "/thai:chochang": "\u0E0A",
-    "/thai:choching": "\u0E09",
-    "/thai:chochoe": "\u0E0C",
-    "/thai:dochada": "\u0E0E",
-    "/thai:dodek": "\u0E14",
-    "/thai:eight": "\u0E58",
-    "/thai:five": "\u0E55",
-    "/thai:fofa": "\u0E1D",
-    "/thai:fofan": "\u0E1F",
-    "/thai:fongman": "\u0E4F",
-    "/thai:four": "\u0E54",
-    "/thai:hohip": "\u0E2B",
-    "/thai:honokhuk": "\u0E2E",
-    "/thai:khokhai": "\u0E02",
-    "/thai:khokhon": "\u0E05",
-    "/thai:khokhuat": "\u0E03",
-    "/thai:khokhwai": "\u0E04",
-    "/thai:khomut": "\u0E5B",
-    "/thai:khorakhang": "\u0E06",
-    "/thai:kokai": "\u0E01",
-    "/thai:lakkhangyao": "\u0E45",
-    "/thai:lochula": "\u0E2C",
-    "/thai:loling": "\u0E25",
-    "/thai:lu": "\u0E26",
-    "/thai:maichattawa": "\u0E4B",
-    "/thai:maiek": "\u0E48",
-    "/thai:maihan-akat": "\u0E31",
-    "/thai:maitaikhu": "\u0E47",
-    "/thai:maitho": "\u0E49",
-    "/thai:maitri": "\u0E4A",
-    "/thai:maiyamok": "\u0E46",
-    "/thai:moma": "\u0E21",
-    "/thai:ngongu": "\u0E07",
-    "/thai:nikhahit": "\u0E4D",
-    "/thai:nine": "\u0E59",
-    "/thai:nonen": "\u0E13",
-    "/thai:nonu": "\u0E19",
-    "/thai:oang": "\u0E2D",
-    "/thai:one": "\u0E51",
-    "/thai:paiyannoi": "\u0E2F",
-    "/thai:phinthu": "\u0E3A",
-    "/thai:phophan": "\u0E1E",
-    "/thai:phophung": "\u0E1C",
-    "/thai:phosamphao": "\u0E20",
-    "/thai:popla": "\u0E1B",
-    "/thai:rorua": "\u0E23",
-    "/thai:ru": "\u0E24",
-    "/thai:saraa": "\u0E30",
-    "/thai:saraaa": "\u0E32",
-    "/thai:saraae": "\u0E41",
-    "/thai:saraaimaimalai": "\u0E44",
-    "/thai:saraaimaimuan": "\u0E43",
-    "/thai:saraam": "\u0E33",
-    "/thai:sarae": "\u0E40",
-    "/thai:sarai": "\u0E34",
-    "/thai:saraii": "\u0E35",
-    "/thai:sarao": "\u0E42",
-    "/thai:sarau": "\u0E38",
-    "/thai:saraue": "\u0E36",
-    "/thai:sarauee": "\u0E37",
-    "/thai:sarauu": "\u0E39",
-    "/thai:seven": "\u0E57",
-    "/thai:six": "\u0E56",
-    "/thai:sorusi": "\u0E29",
-    "/thai:sosala": "\u0E28",
-    "/thai:soso": "\u0E0B",
-    "/thai:sosua": "\u0E2A",
-    "/thai:thanthakhat": "\u0E4C",
-    "/thai:thonangmontho": "\u0E11",
-    "/thai:thophuthao": "\u0E12",
-    "/thai:thothahan": "\u0E17",
-    "/thai:thothan": "\u0E10",
-    "/thai:thothong": "\u0E18",
-    "/thai:thothung": "\u0E16",
-    "/thai:three": "\u0E53",
-    "/thai:topatak": "\u0E0F",
-    "/thai:totao": "\u0E15",
-    "/thai:two": "\u0E52",
-    "/thai:wowaen": "\u0E27",
-    "/thai:yamakkan": "\u0E4E",
-    "/thai:yoyak": "\u0E22",
-    "/thai:yoying": "\u0E0D",
-    "/thai:zero": "\u0E50",
-    "/thal": "\u0630",
-    "/thal.fina": "\uFEAC",
-    "/thal.init_superscriptalef.fina": "\uFC5B",
-    "/thal.isol": "\uFEAB",
-    "/thalarabic": "\u0630",
-    "/thalfinalarabic": "\uFEAC",
-    "/thanthakhatlowleftthai": "\uF898",
-    "/thanthakhatlowrightthai": "\uF897",
-    "/thanthakhatthai": "\u0E4C",
-    "/thanthakhatupperleftthai": "\uF896",
-    "/theh": "\u062B",
-    "/theh.fina": "\uFE9A",
-    "/theh.init": "\uFE9B",
-    "/theh.init_alefmaksura.fina": "\uFC13",
-    "/theh.init_jeem.fina": "\uFC11",
-    "/theh.init_meem.fina": "\uFC12",
-    "/theh.init_meem.medi": "\uFCA6",
-    "/theh.init_yeh.fina": "\uFC14",
-    "/theh.isol": "\uFE99",
-    "/theh.medi": "\uFE9C",
-    "/theh.medi_alefmaksura.fina": "\uFC7A",
-    "/theh.medi_heh.medi": "\uFCE6",
-    "/theh.medi_meem.fina": "\uFC78",
-    "/theh.medi_meem.medi": "\uFCE5",
-    "/theh.medi_noon.fina": "\uFC79",
-    "/theh.medi_reh.fina": "\uFC76",
-    "/theh.medi_yeh.fina": "\uFC7B",
-    "/theh.medi_zain.fina": "\uFC77",
-    "/theharabic": "\u062B",
-    "/thehfinalarabic": "\uFE9A",
-    "/thehinitialarabic": "\uFE9B",
-    "/thehmedialarabic": "\uFE9C",
-    "/thereexists": "\u2203",
-    "/therefore": "\u2234",
-    "/thermometer": "\u1F321",
-    "/theta": "\u03B8",
-    "/theta.math": "\u03D1",
-    "/theta1": "\u03D1",
-    "/thetasymbolgreek": "\u03D1",
-    "/thieuthacirclekorean": "\u3279",
-    "/thieuthaparenkorean": "\u3219",
-    "/thieuthcirclekorean": "\u326B",
-    "/thieuthkorean": "\u314C",
-    "/thieuthparenkorean": "\u320B",
-    "/thinspace": "\u2009",
-    "/thirteencircle": "\u246C",
-    "/thirteencircleblack": "\u24ED",
-    "/thirteenparen": "\u2480",
-    "/thirteenparenthesized": "\u2480",
-    "/thirteenperiod": "\u2494",
-    "/thirtycircle": "\u325A",
-    "/thirtycirclesquare": "\u324A",
-    "/thirtyeightcircle": "\u32B3",
-    "/thirtyfivecircle": "\u325F",
-    "/thirtyfourcircle": "\u325E",
-    "/thirtyhangzhou": "\u303A",
-    "/thirtyninecircle": "\u32B4",
-    "/thirtyonecircle": "\u325B",
-    "/thirtysevencircle": "\u32B2",
-    "/thirtysixcircle": "\u32B1",
-    "/thirtythreecircle": "\u325D",
-    "/thirtytwocircle": "\u325C",
-    "/thonangmonthothai": "\u0E11",
-    "/thook": "\u01AD",
-    "/thophuthaothai": "\u0E12",
-    "/thorn": "\u00FE",
-    "/thornstroke": "\uA765",
-    "/thornstrokedescender": "\uA767",
-    "/thothahanthai": "\u0E17",
-    "/thothanthai": "\u0E10",
-    "/thothongthai": "\u0E18",
-    "/thothungthai": "\u0E16",
-    "/thoughtBalloon": "\u1F4AD",
-    "/thousandcyrillic": "\u0482",
-    "/thousandscyr": "\u0482",
-    "/thousandsseparator": "\u066C",
-    "/thousandsseparatorarabic": "\u066C",
-    "/thousandsseparatorpersian": "\u066C",
-    "/three": "\u0033",
-    "/three.inferior": "\u2083",
-    "/three.roman": "\u2162",
-    "/three.romansmall": "\u2172",
-    "/threeButtonMouse": "\u1F5B1",
-    "/threeNetworkedComputers": "\u1F5A7",
-    "/threeRaysAbove": "\u1F5E4",
-    "/threeRaysBelow": "\u1F5E5",
-    "/threeRaysLeft": "\u1F5E6",
-    "/threeRaysRight": "\u1F5E7",
-    "/threeSpeechBubbles": "\u1F5EB",
-    "/threearabic": "\u0663",
-    "/threebengali": "\u09E9",
-    "/threecircle": "\u2462",
-    "/threecircledbl": "\u24F7",
-    "/threecircleinversesansserif": "\u278C",
-    "/threecomma": "\u1F104",
-    "/threedeva": "\u0969",
-    "/threedimensionalangle": "\u27C0",
-    "/threedotpunctuation": "\u2056",
-    "/threedotsaboveabove": "\u06DB",
-    "/threedsquare": "\u1F19B",
-    "/threeeighths": "\u215C",
-    "/threefar": "\u06F3",
-    "/threefifths": "\u2157",
-    "/threegujarati": "\u0AE9",
-    "/threegurmukhi": "\u0A69",
-    "/threehackarabic": "\u0663",
-    "/threehangzhou": "\u3023",
-    "/threeideographiccircled": "\u3282",
-    "/threeideographicparen": "\u3222",
-    "/threeinferior": "\u2083",
-    "/threelinesconvergingleft": "\u269F",
-    "/threelinesconvergingright": "\u269E",
-    "/threemonospace": "\uFF13",
-    "/threenumeratorbengali": "\u09F6",
-    "/threeoldstyle": "\uF733",
-    "/threeparen": "\u2476",
-    "/threeparenthesized": "\u2476",
-    "/threeperemspace": "\u2004",
-    "/threeperiod": "\u248A",
-    "/threepersian": "\u06F3",
-    "/threequarters": "\u00BE",
-    "/threequartersemdash": "\uF6DE",
-    "/threerightarrows": "\u21F6",
-    "/threeroman": "\u2172",
-    "/threesuperior": "\u00B3",
-    "/threethai": "\u0E53",
-    "/thumbsDownSign": "\u1F44E",
-    "/thumbsUpSign": "\u1F44D",
-    "/thundercloudrain": "\u26C8",
-    "/thunderstorm": "\u2608",
-    "/thzfullwidth": "\u3394",
-    "/thzsquare": "\u3394",
-    "/tibt:AA": "\u0F60",
-    "/tibt:a": "\u0F68",
-    "/tibt:aavowelsign": "\u0F71",
-    "/tibt:angkhanggyasmark": "\u0F3D",
-    "/tibt:angkhanggyonmark": "\u0F3C",
-    "/tibt:astrologicalkhyudpasign": "\u0F18",
-    "/tibt:astrologicalsdongtshugssign": "\u0F19",
-    "/tibt:astrologicalsgragcancharrtagssign": "\u0F17",
-    "/tibt:asubjoined": "\u0FB8",
-    "/tibt:ba": "\u0F56",
-    "/tibt:basubjoined": "\u0FA6",
-    "/tibt:bha": "\u0F57",
-    "/tibt:bhasubjoined": "\u0FA7",
-    "/tibt:bkashogyigmgomark": "\u0F0A",
-    "/tibt:brdarnyingyigmgomdunmainitialmark": "\u0FD3",
-    "/tibt:brdarnyingyigmgosgabmaclosingmark": "\u0FD4",
-    "/tibt:bsdusrtagsmark": "\u0F34",
-    "/tibt:bskashoggimgorgyanmark": "\u0FD0",
-    "/tibt:bskuryigmgomark": "\u0F09",
-    "/tibt:ca": "\u0F45",
-    "/tibt:cangteucantillationsign": "\u0FC2",
-    "/tibt:caretdzudrtagsbzhimigcanmark": "\u0F36",
-    "/tibt:caretdzudrtagsmelongcanmark": "\u0F13",
-    "/tibt:caretyigmgophurshadmamark": "\u0F06",
-    "/tibt:casubjoined": "\u0F95",
-    "/tibt:cha": "\u0F46",
-    "/tibt:chadrtagslogotypesign": "\u0F15",
-    "/tibt:chasubjoined": "\u0F96",
-    "/tibt:chemgomark": "\u0F38",
-    "/tibt:da": "\u0F51",
-    "/tibt:dasubjoined": "\u0FA1",
-    "/tibt:dda": "\u0F4C",
-    "/tibt:ddasubjoined": "\u0F9C",
-    "/tibt:ddha": "\u0F4D",
-    "/tibt:ddhasubjoined": "\u0F9D",
-    "/tibt:delimitertshegbstarmark": "\u0F0C",
-    "/tibt:dha": "\u0F52",
-    "/tibt:dhasubjoined": "\u0FA2",
-    "/tibt:drilbusymbol": "\u0FC4",
-    "/tibt:dza": "\u0F5B",
-    "/tibt:dzasubjoined": "\u0FAB",
-    "/tibt:dzha": "\u0F5C",
-    "/tibt:dzhasubjoined": "\u0FAC",
-    "/tibt:eevowelsign": "\u0F7B",
-    "/tibt:eight": "\u0F28",
-    "/tibt:evowelsign": "\u0F7A",
-    "/tibt:five": "\u0F25",
-    "/tibt:four": "\u0F24",
-    "/tibt:ga": "\u0F42",
-    "/tibt:gasubjoined": "\u0F92",
-    "/tibt:gha": "\u0F43",
-    "/tibt:ghasubjoined": "\u0F93",
-    "/tibt:grucanrgyingssign": "\u0F8A",
-    "/tibt:grumedrgyingssign": "\u0F8B",
-    "/tibt:gtertshegmark": "\u0F14",
-    "/tibt:gteryigmgotruncatedamark": "\u0F01",
-    "/tibt:gteryigmgoumgtertshegmamark": "\u0F03",
-    "/tibt:gteryigmgoumrnambcadmamark": "\u0F02",
-    "/tibt:gugrtagsgyasmark": "\u0F3B",
-    "/tibt:gugrtagsgyonmark": "\u0F3A",
-    "/tibt:ha": "\u0F67",
-    "/tibt:halantamark": "\u0F84",
-    "/tibt:halfeight": "\u0F31",
-    "/tibt:halffive": "\u0F2E",
-    "/tibt:halffour": "\u0F2D",
-    "/tibt:halfnine": "\u0F32",
-    "/tibt:halfone": "\u0F2A",
-    "/tibt:halfseven": "\u0F30",
-    "/tibt:halfsix": "\u0F2F",
-    "/tibt:halfthree": "\u0F2C",
-    "/tibt:halftwo": "\u0F2B",
-    "/tibt:halfzero": "\u0F33",
-    "/tibt:hasubjoined": "\u0FB7",
-    "/tibt:heavybeatcantillationsign": "\u0FC0",
-    "/tibt:iivowelsign": "\u0F73",
-    "/tibt:intersyllabictshegmark": "\u0F0B",
-    "/tibt:invertedmchucansign": "\u0F8C",
-    "/tibt:invertedmchucansubjoinedsign": "\u0F8F",
-    "/tibt:ivowelsign": "\u0F72",
-    "/tibt:ja": "\u0F47",
-    "/tibt:jasubjoined": "\u0F97",
-    "/tibt:ka": "\u0F40",
-    "/tibt:kasubjoined": "\u0F90",
-    "/tibt:kha": "\u0F41",
-    "/tibt:khasubjoined": "\u0F91",
-    "/tibt:kka": "\u0F6B",
-    "/tibt:kssa": "\u0F69",
-    "/tibt:kssasubjoined": "\u0FB9",
-    "/tibt:kurukha": "\u0FBE",
-    "/tibt:kurukhabzhimigcan": "\u0FBF",
-    "/tibt:la": "\u0F63",
-    "/tibt:lasubjoined": "\u0FB3",
-    "/tibt:lcetsacansign": "\u0F88",
-    "/tibt:lcetsacansubjoinedsign": "\u0F8D",
-    "/tibt:lcirtagssign": "\u0F86",
-    "/tibt:leadingmchanrtagsmark": "\u0FD9",
-    "/tibt:lhagrtagslogotypesign": "\u0F16",
-    "/tibt:lightbeatcantillationsign": "\u0FC1",
-    "/tibt:llvocalicvowelsign": "\u0F79",
-    "/tibt:lvocalicvowelsign": "\u0F78",
-    "/tibt:ma": "\u0F58",
-    "/tibt:martshessign": "\u0F3F",
-    "/tibt:masubjoined": "\u0FA8",
-    "/tibt:mchucansign": "\u0F89",
-    "/tibt:mchucansubjoinedsign": "\u0F8E",
-    "/tibt:mnyamyiggimgorgyanmark": "\u0FD1",
-    "/tibt:na": "\u0F53",
-    "/tibt:nasubjoined": "\u0FA3",
-    "/tibt:nga": "\u0F44",
-    "/tibt:ngasbzungnyizlamark": "\u0F35",
-    "/tibt:ngasbzungsgorrtagsmark": "\u0F37",
-    "/tibt:ngasubjoined": "\u0F94",
-    "/tibt:nine": "\u0F29",
-    "/tibt:nna": "\u0F4E",
-    "/tibt:nnasubjoined": "\u0F9E",
-    "/tibt:norbubzhikhyilsymbol": "\u0FCC",
-    "/tibt:norbugsumkhyilsymbol": "\u0FCB",
-    "/tibt:norbunyiskhyilsymbol": "\u0FCA",
-    "/tibt:norbusymbol": "\u0FC9",
-    "/tibt:nya": "\u0F49",
-    "/tibt:nyasubjoined": "\u0F99",
-    "/tibt:nyisshadmark": "\u0F0E",
-    "/tibt:nyistshegmark": "\u0FD2",
-    "/tibt:nyistshegshadmark": "\u0F10",
-    "/tibt:nyizlanaadasign": "\u0F82",
-    "/tibt:omsyllable": "\u0F00",
-    "/tibt:one": "\u0F21",
-    "/tibt:oovowelsign": "\u0F7D",
-    "/tibt:ovowelsign": "\u0F7C",
-    "/tibt:pa": "\u0F54",
-    "/tibt:padmagdansymbol": "\u0FC6",
-    "/tibt:palutamark": "\u0F85",
-    "/tibt:pasubjoined": "\u0FA4",
-    "/tibt:pha": "\u0F55",
-    "/tibt:phasubjoined": "\u0FA5",
-    "/tibt:phurpasymbol": "\u0FC8",
-    "/tibt:ra": "\u0F62",
-    "/tibt:rafixed": "\u0F6A",
-    "/tibt:rasubjoined": "\u0FB2",
-    "/tibt:rasubjoinedfixed": "\u0FBC",
-    "/tibt:rdeldkargcigsign": "\u0F1A",
-    "/tibt:rdeldkargnyissign": "\u0F1B",
-    "/tibt:rdeldkargsumsign": "\u0F1C",
-    "/tibt:rdeldkarrdelnagsign": "\u0F1F",
-    "/tibt:rdelnaggcigsign": "\u0F1D",
-    "/tibt:rdelnaggnyissign": "\u0F1E",
-    "/tibt:rdelnaggsumsign": "\u0FCF",
-    "/tibt:rdelnagrdeldkarsign": "\u0FCE",
-    "/tibt:rdorjergyagramsymbol": "\u0FC7",
-    "/tibt:rdorjesymbol": "\u0FC5",
-    "/tibt:reversediivowelsign": "\u0F81",
-    "/tibt:reversedivowelsign": "\u0F80",
-    "/tibt:rgyagramshadmark": "\u0F12",
-    "/tibt:rinchenspungsshadmark": "\u0F11",
-    "/tibt:rjessungarosign": "\u0F7E",
-    "/tibt:rnambcadsign": "\u0F7F",
-    "/tibt:rra": "\u0F6C",
-    "/tibt:rrvocalicvowelsign": "\u0F77",
-    "/tibt:rvocalicvowelsign": "\u0F76",
-    "/tibt:sa": "\u0F66",
-    "/tibt:sasubjoined": "\u0FB6",
-    "/tibt:sbrulshadmark": "\u0F08",
-    "/tibt:sbubchalcantillationsign": "\u0FC3",
-    "/tibt:seven": "\u0F27",
-    "/tibt:sha": "\u0F64",
-    "/tibt:shadmark": "\u0F0D",
-    "/tibt:shasubjoined": "\u0FB4",
-    "/tibt:six": "\u0F26",
-    "/tibt:snaldansign": "\u0F83",
-    "/tibt:ssa": "\u0F65",
-    "/tibt:ssasubjoined": "\u0FB5",
-    "/tibt:subjoinedAA": "\u0FB0",
-    "/tibt:svastileft": "\u0FD6",
-    "/tibt:svastileftdot": "\u0FD8",
-    "/tibt:svastiright": "\u0FD5",
-    "/tibt:svastirightdot": "\u0FD7",
-    "/tibt:ta": "\u0F4F",
-    "/tibt:tasubjoined": "\u0F9F",
-    "/tibt:tha": "\u0F50",
-    "/tibt:thasubjoined": "\u0FA0",
-    "/tibt:three": "\u0F23",
-    "/tibt:trailingmchanrtagsmark": "\u0FDA",
-    "/tibt:tsa": "\u0F59",
-    "/tibt:tsaphrumark": "\u0F39",
-    "/tibt:tsasubjoined": "\u0FA9",
-    "/tibt:tsha": "\u0F5A",
-    "/tibt:tshasubjoined": "\u0FAA",
-    "/tibt:tshegshadmark": "\u0F0F",
-    "/tibt:tta": "\u0F4A",
-    "/tibt:ttasubjoined": "\u0F9A",
-    "/tibt:ttha": "\u0F4B",
-    "/tibt:tthasubjoined": "\u0F9B",
-    "/tibt:two": "\u0F22",
-    "/tibt:uuvowelsign": "\u0F75",
-    "/tibt:uvowelsign": "\u0F74",
-    "/tibt:wa": "\u0F5D",
-    "/tibt:wasubjoined": "\u0FAD",
-    "/tibt:wasubjoinedfixed": "\u0FBA",
-    "/tibt:ya": "\u0F61",
-    "/tibt:yangrtagssign": "\u0F87",
-    "/tibt:yartshessign": "\u0F3E",
-    "/tibt:yasubjoined": "\u0FB1",
-    "/tibt:yasubjoinedfixed": "\u0FBB",
-    "/tibt:yigmgomdunmainitialmark": "\u0F04",
-    "/tibt:yigmgosgabmaclosingmark": "\u0F05",
-    "/tibt:yigmgotshegshadmamark": "\u0F07",
-    "/tibt:za": "\u0F5F",
-    "/tibt:zasubjoined": "\u0FAF",
-    "/tibt:zero": "\u0F20",
-    "/tibt:zha": "\u0F5E",
-    "/tibt:zhasubjoined": "\u0FAE",
-    "/ticirclekatakana": "\u32E0",
-    "/tickconvavediamondleftwhite": "\u27E2",
-    "/tickconvavediamondrightwhite": "\u27E3",
-    "/ticket": "\u1F3AB",
-    "/tickleftwhitesquare": "\u27E4",
-    "/tickrightwhitesquare": "\u27E5",
-    "/tifcha:hb": "\u0596",
-    "/tiger": "\u1F405",
-    "/tigerFace": "\u1F42F",
-    "/tihiragana": "\u3061",
-    "/tikatakana": "\u30C1",
-    "/tikatakanahalfwidth": "\uFF81",
-    "/tikeutacirclekorean": "\u3270",
-    "/tikeutaparenkorean": "\u3210",
-    "/tikeutcirclekorean": "\u3262",
-    "/tikeutkorean": "\u3137",
-    "/tikeutparenkorean": "\u3202",
-    "/tilde": "\u02DC",
-    "/tildebelowcmb": "\u0330",
-    "/tildecmb": "\u0303",
-    "/tildecomb": "\u0303",
-    "/tildediaeresisfunc": "\u2368",
-    "/tildedotaccent": "\u2E1E",
-    "/tildedotbelow": "\u2E1F",
-    "/tildedoublecmb": "\u0360",
-    "/tildeequalsreversed": "\u22CD",
-    "/tildelowmod": "\u02F7",
-    "/tildeoperator": "\u223C",
-    "/tildeoverlaycmb": "\u0334",
-    "/tildereversed": "\u223D",
-    "/tildering": "\u2E1B",
-    "/tildetpl": "\u224B",
-    "/tildeverticalcmb": "\u033E",
-    "/timerclock": "\u23F2",
-    "/timescircle": "\u2297",
-    "/tinsular": "\uA787",
-    "/tipehahebrew": "\u0596",
-    "/tipehalefthebrew": "\u0596",
-    "/tippigurmukhi": "\u0A70",
-    "/tiredFace": "\u1F62B",
-    "/tironiansignet": "\u204A",
-    "/tirtatumetespada": "\uA9DE",
-    "/titlocmbcyr": "\u0483",
-    "/titlocyrilliccmb": "\u0483",
-    "/tiwnarmenian": "\u057F",
-    "/tjekomicyr": "\u050F",
-    "/tlinebelow": "\u1E6F",
-    "/tmonospace": "\uFF54",
-    "/toarmenian": "\u0569",
-    "/tocirclekatakana": "\u32E3",
-    "/tocornerarrowNW": "\u21F1",
-    "/tocornerarrowSE": "\u21F2",
-    "/tohiragana": "\u3068",
-    "/toilet": "\u1F6BD",
-    "/tokatakana": "\u30C8",
-    "/tokatakanahalfwidth": "\uFF84",
-    "/tokyoTower": "\u1F5FC",
-    "/tolongvowel": "\uA9B5",
-    "/tomato": "\u1F345",
-    "/tonebarextrahighmod": "\u02E5",
-    "/tonebarextralowmod": "\u02E9",
-    "/tonebarhighmod": "\u02E6",
-    "/tonebarlowmod": "\u02E8",
-    "/tonebarmidmod": "\u02E7",
-    "/tonefive": "\u01BD",
-    "/tonehighbeginmod": "\u02F9",
-    "/tonehighendmod": "\u02FA",
-    "/tonelowbeginmod": "\u02FB",
-    "/tonelowendmod": "\u02FC",
-    "/tonesix": "\u0185",
-    "/tonetwo": "\u01A8",
-    "/tongue": "\u1F445",
-    "/tonos": "\u0384",
-    "/tonsquare": "\u3327",
-    "/topHat": "\u1F3A9",
-    "/topUpwardsArrowAbove": "\u1F51D",
-    "/topatakthai": "\u0E0F",
-    "/tortoiseshellbracketleft": "\u3014",
-    "/tortoiseshellbracketleftsmall": "\uFE5D",
-    "/tortoiseshellbracketleftvertical": "\uFE39",
-    "/tortoiseshellbracketright": "\u3015",
-    "/tortoiseshellbracketrightsmall": "\uFE5E",
-    "/tortoiseshellbracketrightvertical": "\uFE3A",
-    "/totalrunout": "\u2330",
-    "/totaothai": "\u0E15",
-    "/tpalatalhook": "\u01AB",
-    "/tparen": "\u24AF",
-    "/tparenthesized": "\u24AF",
-    "/trackball": "\u1F5B2",
-    "/tractor": "\u1F69C",
-    "/trademark": "\u2122",
-    "/trademarksans": "\uF8EA",
-    "/trademarkserif": "\uF6DB",
-    "/train": "\u1F686",
-    "/tram": "\u1F68A",
-    "/tramCar": "\u1F68B",
-    "/trapeziumwhite": "\u23E2",
-    "/tresillo": "\uA72B",
-    "/tretroflex": "\u0288",
-    "/tretroflexhook": "\u0288",
-    "/triagdn": "\u25BC",
-    "/triaglf": "\u25C4",
-    "/triagrt": "\u25BA",
-    "/triagup": "\u25B2",
-    "/triangleWithRoundedCorners": "\u1F6C6",
-    "/triangledotupwhite": "\u25EC",
-    "/triangledownblack": "\u25BC",
-    "/triangledownsmallblack": "\u25BE",
-    "/triangledownsmallwhite": "\u25BF",
-    "/triangledownwhite": "\u25BD",
-    "/trianglehalfupleftblack": "\u25ED",
-    "/trianglehalfuprightblack": "\u25EE",
-    "/triangleleftblack": "\u25C0",
-    "/triangleleftsmallblack": "\u25C2",
-    "/triangleleftsmallwhite": "\u25C3",
-    "/triangleleftwhite": "\u25C1",
-    "/triangleright": "\u22BF",
-    "/trianglerightblack": "\u25B6",
-    "/trianglerightsmallblack": "\u25B8",
-    "/trianglerightsmallwhite": "\u25B9",
-    "/trianglerightwhite": "\u25B7",
-    "/triangleupblack": "\u25B2",
-    "/triangleupsmallblack": "\u25B4",
-    "/triangleupsmallwhite": "\u25B5",
-    "/triangleupwhite": "\u25B3",
-    "/triangularFlagOnPost": "\u1F6A9",
-    "/triangularRuler": "\u1F4D0",
-    "/triangularbullet": "\u2023",
-    "/tricolon": "\u205D",
-    "/tricontainingtriwhiteanglesmall": "\u27C1",
-    "/tridentEmblem": "\u1F531",
-    "/trigramearth": "\u2637",
-    "/trigramfire": "\u2632",
-    "/trigramheaven": "\u2630",
-    "/trigramlake": "\u2631",
-    "/trigrammountain": "\u2636",
-    "/trigramthunder": "\u2633",
-    "/trigramwater": "\u2635",
-    "/trigramwind": "\u2634",
-    "/triplearrowleft": "\u21DA",
-    "/triplearrowright": "\u21DB",
-    "/tripledot": "\u061E",
-    "/trisememetrical": "\u23D7",
-    "/trns:baby": "\u1F6BC",
-    "/trolleybus": "\u1F68E",
-    "/trophy": "\u1F3C6",
-    "/tropicalDrink": "\u1F379",
-    "/tropicalFish": "\u1F420",
-    "/truckblack": "\u26DF",
-    "/true": "\u22A8",
-    "/trumpet": "\u1F3BA",
-    "/ts": "\u02A6",
-    "/tsadi": "\u05E6",
-    "/tsadi:hb": "\u05E6",
-    "/tsadidagesh": "\uFB46",
-    "/tsadidageshhebrew": "\uFB46",
-    "/tsadihebrew": "\u05E6",
-    "/tsadiwithdagesh:hb": "\uFB46",
-    "/tsecyr": "\u0446",
-    "/tsecyrillic": "\u0446",
-    "/tsere": "\u05B5",
-    "/tsere12": "\u05B5",
-    "/tsere1e": "\u05B5",
-    "/tsere2b": "\u05B5",
-    "/tsere:hb": "\u05B5",
-    "/tserehebrew": "\u05B5",
-    "/tserenarrowhebrew": "\u05B5",
-    "/tserequarterhebrew": "\u05B5",
-    "/tserewidehebrew": "\u05B5",
-    "/tshecyr": "\u045B",
-    "/tshecyrillic": "\u045B",
-    "/tsinnorit:hb": "\u05AE",
-    "/tstroke": "\u2C66",
-    "/tsuperior": "\uF6F3",
-    "/ttabengali": "\u099F",
-    "/ttadeva": "\u091F",
-    "/ttagujarati": "\u0A9F",
-    "/ttagurmukhi": "\u0A1F",
-    "/ttamahaprana": "\uA99C",
-    "/tteh": "\u0679",
-    "/tteh.fina": "\uFB67",
-    "/tteh.init": "\uFB68",
-    "/tteh.isol": "\uFB66",
-    "/tteh.medi": "\uFB69",
-    "/tteharabic": "\u0679",
-    "/tteheh": "\u067A",
-    "/tteheh.fina": "\uFB5F",
-    "/tteheh.init": "\uFB60",
-    "/tteheh.isol": "\uFB5E",
-    "/tteheh.medi": "\uFB61",
-    "/ttehfinalarabic": "\uFB67",
-    "/ttehinitialarabic": "\uFB68",
-    "/ttehmedialarabic": "\uFB69",
-    "/tthabengali": "\u09A0",
-    "/tthadeva": "\u0920",
-    "/tthagujarati": "\u0AA0",
-    "/tthagurmukhi": "\u0A20",
-    "/tturned": "\u0287",
-    "/tucirclekatakana": "\u32E1",
-    "/tugrik": "\u20AE",
-    "/tuhiragana": "\u3064",
-    "/tukatakana": "\u30C4",
-    "/tukatakanahalfwidth": "\uFF82",
-    "/tulip": "\u1F337",
-    "/tum": "\uA777",
-    "/turkishlira": "\u20BA",
-    "/turnedOkHandSign": "\u1F58F",
-    "/turnedcomma": "\u2E32",
-    "/turneddagger": "\u2E38",
-    "/turneddigitthree": "\u218B",
-    "/turneddigittwo": "\u218A",
-    "/turnedpiselehpada": "\uA9CD",
-    "/turnedsemicolon": "\u2E35",
-    "/turnedshogipieceblack": "\u26CA",
-    "/turnedshogipiecewhite": "\u26C9",
-    "/turnstiledblverticalbarright": "\u22AB",
-    "/turnstileleftrightdbl": "\u27DA",
-    "/turnstiletplverticalbarright": "\u22AA",
-    "/turtle": "\u1F422",
-    "/tusmallhiragana": "\u3063",
-    "/tusmallkatakana": "\u30C3",
-    "/tusmallkatakanahalfwidth": "\uFF6F",
-    "/twelve.roman": "\u216B",
-    "/twelve.romansmall": "\u217B",
-    "/twelvecircle": "\u246B",
-    "/twelvecircleblack": "\u24EC",
-    "/twelveparen": "\u247F",
-    "/twelveparenthesized": "\u247F",
-    "/twelveperiod": "\u2493",
-    "/twelveroman": "\u217B",
-    "/twenty-twopointtwosquare": "\u1F1A2",
-    "/twentycircle": "\u2473",
-    "/twentycircleblack": "\u24F4",
-    "/twentycirclesquare": "\u3249",
-    "/twentyeightcircle": "\u3258",
-    "/twentyfivecircle": "\u3255",
-    "/twentyfourcircle": "\u3254",
-    "/twentyhangzhou": "\u5344",
-    "/twentyninecircle": "\u3259",
-    "/twentyonecircle": "\u3251",
-    "/twentyparen": "\u2487",
-    "/twentyparenthesized": "\u2487",
-    "/twentyperiod": "\u249B",
-    "/twentysevencircle": "\u3257",
-    "/twentysixcircle": "\u3256",
-    "/twentythreecircle": "\u3253",
-    "/twentytwocircle": "\u3252",
-    "/twistedRightwardsArrows": "\u1F500",
-    "/two": "\u0032",
-    "/two.inferior": "\u2082",
-    "/two.roman": "\u2161",
-    "/two.romansmall": "\u2171",
-    "/twoButtonMouse": "\u1F5B0",
-    "/twoHearts": "\u1F495",
-    "/twoMenHoldingHands": "\u1F46C",
-    "/twoSpeechBubbles": "\u1F5EA",
-    "/twoWomenHoldingHands": "\u1F46D",
-    "/twoarabic": "\u0662",
-    "/twoasterisksalignedvertically": "\u2051",
-    "/twobengali": "\u09E8",
-    "/twocircle": "\u2461",
-    "/twocircledbl": "\u24F6",
-    "/twocircleinversesansserif": "\u278B",
-    "/twocomma": "\u1F103",
-    "/twodeva": "\u0968",
-    "/twodotenleader": "\u2025",
-    "/twodotleader": "\u2025",
-    "/twodotleadervertical": "\uFE30",
-    "/twodotpunctuation": "\u205A",
-    "/twodotsoveronedot": "\u2E2A",
-    "/twofar": "\u06F2",
-    "/twofifths": "\u2156",
-    "/twogujarati": "\u0AE8",
-    "/twogurmukhi": "\u0A68",
-    "/twohackarabic": "\u0662",
-    "/twohangzhou": "\u3022",
-    "/twoideographiccircled": "\u3281",
-    "/twoideographicparen": "\u3221",
-    "/twoinferior": "\u2082",
-    "/twoksquare": "\u1F19D",
-    "/twomonospace": "\uFF12",
-    "/twonumeratorbengali": "\u09F5",
-    "/twooldstyle": "\uF732",
-    "/twoparen": "\u2475",
-    "/twoparenthesized": "\u2475",
-    "/twoperiod": "\u2489",
-    "/twopersian": "\u06F2",
-    "/tworoman": "\u2171",
-    "/twoshortsjoinedmetrical": "\u23D6",
-    "/twoshortsoverlongmetrical": "\u23D5",
-    "/twostroke": "\u01BB",
-    "/twosuperior": "\u00B2",
-    "/twothai": "\u0E52",
-    "/twothirds": "\u2154",
-    "/twowayleftwaytrafficblack": "\u26D6",
-    "/twowayleftwaytrafficwhite": "\u26D7",
-    "/tz": "\uA729",
-    "/u": "\u0075",
-    "/u.fina": "\uFBD8",
-    "/u.isol": "\uFBD7",
-    "/uacute": "\u00FA",
-    "/uacutedblcyr": "\u04F3",
-    "/ubar": "\u0289",
-    "/ubengali": "\u0989",
-    "/ubopomofo": "\u3128",
-    "/ubracketleft": "\u2E26",
-    "/ubracketright": "\u2E27",
-    "/ubreve": "\u016D",
-    "/ucaron": "\u01D4",
-    "/ucircle": "\u24E4",
-    "/ucirclekatakana": "\u32D2",
-    "/ucircumflex": "\u00FB",
-    "/ucircumflexbelow": "\u1E77",
-    "/ucyr": "\u0443",
-    "/ucyrillic": "\u0443",
-    "/udattadeva": "\u0951",
-    "/udblacute": "\u0171",
-    "/udblgrave": "\u0215",
-    "/udeva": "\u0909",
-    "/udieresis": "\u00FC",
-    "/udieresisacute": "\u01D8",
-    "/udieresisbelow": "\u1E73",
-    "/udieresiscaron": "\u01DA",
-    "/udieresiscyr": "\u04F1",
-    "/udieresiscyrillic": "\u04F1",
-    "/udieresisgrave": "\u01DC",
-    "/udieresismacron": "\u01D6",
-    "/udotbelow": "\u1EE5",
-    "/ugrave": "\u00F9",
-    "/ugravedbl": "\u0215",
-    "/ugujarati": "\u0A89",
-    "/ugurmukhi": "\u0A09",
-    "/uhamza": "\u0677",
-    "/uhamza.isol": "\uFBDD",
-    "/uhdsquare": "\u1F1AB",
-    "/uhiragana": "\u3046",
-    "/uhoi": "\u1EE7",
-    "/uhookabove": "\u1EE7",
-    "/uhorn": "\u01B0",
-    "/uhornacute": "\u1EE9",
-    "/uhorndotbelow": "\u1EF1",
-    "/uhorngrave": "\u1EEB",
-    "/uhornhoi": "\u1EED",
-    "/uhornhookabove": "\u1EED",
-    "/uhorntilde": "\u1EEF",
-    "/uhungarumlaut": "\u0171",
-    "/uhungarumlautcyrillic": "\u04F3",
-    "/uighurkazakhkirghizalefmaksura.init": "\uFBE8",
-    "/uighurkazakhkirghizalefmaksura.medi": "\uFBE9",
-    "/uighurkirghizyeh.init_hamzaabove.medi_alefmaksura.fina": "\uFBF9",
-    "/uighurkirghizyeh.init_hamzaabove.medi_alefmaksura.medi": "\uFBFB",
-    "/uighurkirghizyeh.medi_hamzaabove.medi_alefmaksura.fina": "\uFBFA",
-    "/uinvertedbreve": "\u0217",
-    "/ukatakana": "\u30A6",
-    "/ukatakanahalfwidth": "\uFF73",
-    "/ukcyr": "\u0479",
-    "/ukcyrillic": "\u0479",
-    "/ukorean": "\u315C",
-    "/um": "\uA778",
-    "/umacron": "\u016B",
-    "/umacroncyr": "\u04EF",
-    "/umacroncyrillic": "\u04EF",
-    "/umacrondieresis": "\u1E7B",
-    "/umatragurmukhi": "\u0A41",
-    "/umbrella": "\u2602",
-    "/umbrellaonground": "\u26F1",
-    "/umbrellaraindrops": "\u2614",
-    "/umonospace": "\uFF55",
-    "/unamusedFace": "\u1F612",
-    "/unaspiratedmod": "\u02ED",
-    "/underscore": "\u005F",
-    "/underscorecenterline": "\uFE4E",
-    "/underscoredashed": "\uFE4D",
-    "/underscoredbl": "\u2017",
-    "/underscoremonospace": "\uFF3F",
-    "/underscorevertical": "\uFE33",
-    "/underscorewavy": "\uFE4F",
-    "/underscorewavyvertical": "\uFE34",
-    "/undertie": "\u203F",
-    "/undo": "\u238C",
-    "/union": "\u222A",
-    "/unionarray": "\u22C3",
-    "/uniondbl": "\u22D3",
-    "/universal": "\u2200",
-    "/unmarriedpartnership": "\u26AF",
-    "/uogonek": "\u0173",
-    "/uonsquare": "\u3306",
-    "/upPointingAirplane": "\u1F6E7",
-    "/upPointingMilitaryAirplane": "\u1F6E6",
-    "/upPointingSmallAirplane": "\u1F6E8",
-    "/uparen": "\u24B0",
-    "/uparenthesized": "\u24B0",
-    "/uparrowleftofdownarrow": "\u21C5",
-    "/upblock": "\u2580",
-    "/updblhorzsng": "\u2568",
-    "/updblleftsng": "\u255C",
-    "/updblrightsng": "\u2559",
-    "/upheavydnhorzlight": "\u2540",
-    "/upheavyhorzlight": "\u2538",
-    "/upheavyleftdnlight": "\u2526",
-    "/upheavyleftlight": "\u251A",
-    "/upheavyrightdnlight": "\u251E",
-    "/upheavyrightlight": "\u2516",
-    "/uplightdnhorzheavy": "\u2548",
-    "/uplighthorzheavy": "\u2537",
-    "/uplightleftdnheavy": "\u252A",
-    "/uplightleftheavy": "\u2519",
-    "/uplightrightdnheavy": "\u2522",
-    "/uplightrightheavy": "\u2515",
-    "/upperHalfBlock": "\u2580",
-    "/upperOneEighthBlock": "\u2594",
-    "/upperRightShadowedWhiteCircle": "\u1F53F",
-    "/upperdothebrew": "\u05C4",
-    "/upperhalfcircle": "\u25E0",
-    "/upperhalfcircleinversewhite": "\u25DA",
-    "/upperquadrantcirculararcleft": "\u25DC",
-    "/upperquadrantcirculararcright": "\u25DD",
-    "/uppertriangleleft": "\u25F8",
-    "/uppertriangleleftblack": "\u25E4",
-    "/uppertriangleright": "\u25F9",
-    "/uppertrianglerightblack": "\u25E5",
-    "/upsideDownFace": "\u1F643",
-    "/upsilon": "\u03C5",
-    "/upsilonacute": "\u1F7B",
-    "/upsilonasper": "\u1F51",
-    "/upsilonasperacute": "\u1F55",
-    "/upsilonaspergrave": "\u1F53",
-    "/upsilonaspertilde": "\u1F57",
-    "/upsilonbreve": "\u1FE0",
-    "/upsilondieresis": "\u03CB",
-    "/upsilondieresisacute": "\u1FE3",
-    "/upsilondieresisgrave": "\u1FE2",
-    "/upsilondieresistilde": "\u1FE7",
-    "/upsilondieresistonos": "\u03B0",
-    "/upsilongrave": "\u1F7A",
-    "/upsilonlatin": "\u028A",
-    "/upsilonlenis": "\u1F50",
-    "/upsilonlenisacute": "\u1F54",
-    "/upsilonlenisgrave": "\u1F52",
-    "/upsilonlenistilde": "\u1F56",
-    "/upsilontilde": "\u1FE6",
-    "/upsilontonos": "\u03CD",
-    "/upsilonwithmacron": "\u1FE1",
-    "/upsnghorzdbl": "\u2567",
-    "/upsngleftdbl": "\u255B",
-    "/upsngrightdbl": "\u2558",
-    "/uptackbelowcmb": "\u031D",
-    "/uptackmod": "\u02D4",
-    "/upwithexclamationmarksquare": "\u1F199",
-    "/uragurmukhi": "\u0A73",
-    "/uranus": "\u2645",
-    "/uring": "\u016F",
-    "/ushortcyr": "\u045E",
-    "/ushortcyrillic": "\u045E",
-    "/usmallhiragana": "\u3045",
-    "/usmallkatakana": "\u30A5",
-    "/usmallkatakanahalfwidth": "\uFF69",
-    "/usmod": "\uA770",
-    "/ustraightcyr": "\u04AF",
-    "/ustraightcyrillic": "\u04AF",
-    "/ustraightstrokecyr": "\u04B1",
-    "/ustraightstrokecyrillic": "\u04B1",
-    "/utilde": "\u0169",
-    "/utildeacute": "\u1E79",
-    "/utildebelow": "\u1E75",
-    "/uubengali": "\u098A",
-    "/uudeva": "\u090A",
-    "/uugujarati": "\u0A8A",
-    "/uugurmukhi": "\u0A0A",
-    "/uumatragurmukhi": "\u0A42",
-    "/uuvowelsignbengali": "\u09C2",
-    "/uuvowelsigndeva": "\u0942",
-    "/uuvowelsigngujarati": "\u0AC2",
-    "/uvowelsignbengali": "\u09C1",
-    "/uvowelsigndeva": "\u0941",
-    "/uvowelsigngujarati": "\u0AC1",
-    "/v": "\u0076",
-    "/vadeva": "\u0935",
-    "/vagujarati": "\u0AB5",
-    "/vagurmukhi": "\u0A35",
-    "/vakatakana": "\u30F7",
-    "/vanedownfunc": "\u2356",
-    "/vaneleftfunc": "\u2345",
-    "/vanerightfunc": "\u2346",
-    "/vaneupfunc": "\u234F",
-    "/varikajudeospanish:hb": "\uFB1E",
-    "/vav": "\u05D5",
-    "/vav:hb": "\u05D5",
-    "/vav_vav:hb": "\u05F0",
-    "/vav_yod:hb": "\u05F1",
-    "/vavdagesh": "\uFB35",
-    "/vavdagesh65": "\uFB35",
-    "/vavdageshhebrew": "\uFB35",
-    "/vavhebrew": "\u05D5",
-    "/vavholam": "\uFB4B",
-    "/vavholamhebrew": "\uFB4B",
-    "/vavvavhebrew": "\u05F0",
-    "/vavwithdagesh:hb": "\uFB35",
-    "/vavwithholam:hb": "\uFB4B",
-    "/vavyodhebrew": "\u05F1",
-    "/vcircle": "\u24E5",
-    "/vcurl": "\u2C74",
-    "/vdiagonalstroke": "\uA75F",
-    "/vdotbelow": "\u1E7F",
-    "/ve.fina": "\uFBDF",
-    "/ve.isol": "\uFBDE",
-    "/ve:abovetonecandra": "\u1CF4",
-    "/ve:anusvaraantargomukhasign": "\u1CE9",
-    "/ve:anusvarabahirgomukhasign": "\u1CEA",
-    "/ve:anusvarasignlong": "\u1CEF",
-    "/ve:anusvaraubhayatomukhasign": "\u1CF1",
-    "/ve:anusvaravamagomukhasign": "\u1CEB",
-    "/ve:anusvaravamagomukhawithtailsign": "\u1CEC",
-    "/ve:ardhavisargasign": "\u1CF2",
-    "/ve:atharvaindependentsvaritatone": "\u1CE1",
-    "/ve:atikramasign": "\u1CF7",
-    "/ve:belowtonecandra": "\u1CD8",
-    "/ve:dotbelowtone": "\u1CDD",
-    "/ve:hexiformanusvarasignlong": "\u1CEE",
-    "/ve:jihvamuliyasign": "\u1CF5",
-    "/ve:karshanatone": "\u1CD0",
-    "/ve:kathakaanudattatone": "\u1CDC",
-    "/ve:nihshvasasign": "\u1CD3",
-    "/ve:prenkhatone": "\u1CD2",
-    "/ve:rigkashmiriindependentsvaritatone": "\u1CE0",
-    "/ve:ringabovetone": "\u1CF8",
-    "/ve:ringabovetonedbl": "\u1CF9",
-    "/ve:rotatedardhavisargasign": "\u1CF3",
-    "/ve:rthanganusvarasignlong": "\u1CF0",
-    "/ve:sharatone": "\u1CD1",
-    "/ve:svaritatonedbl": "\u1CDA",
-    "/ve:svaritatonetpl": "\u1CDB",
-    "/ve:threedotsbelowtone": "\u1CDF",
-    "/ve:tiryaksign": "\u1CED",
-    "/ve:twodotsbelowtone": "\u1CDE",
-    "/ve:upadhmaniyasign": "\u1CF6",
-    "/ve:visargaanudattasign": "\u1CE5",
-    "/ve:visargaanudattasignreversed": "\u1CE6",
-    "/ve:visargaanudattawithtailsign": "\u1CE8",
-    "/ve:visargasvaritasign": "\u1CE2",
-    "/ve:visargaudattasign": "\u1CE3",
-    "/ve:visargaudattasignreversed": "\u1CE4",
-    "/ve:visargaudattawithtailsign": "\u1CE7",
-    "/ve:yajuraggravatedindependentsvaritatone": "\u1CD5",
-    "/ve:yajurindependentsvaritatone": "\u1CD6",
-    "/ve:yajurkathakaindependentsvaritaschroedertone": "\u1CD9",
-    "/ve:yajurkathakaindependentsvaritatone": "\u1CD7",
-    "/ve:yajurmidlinesvaritasign": "\u1CD4",
-    "/vecyr": "\u0432",
-    "/vecyrillic": "\u0432",
-    "/veh": "\u06A4",
-    "/veh.fina": "\uFB6B",
-    "/veh.init": "\uFB6C",
-    "/veh.isol": "\uFB6A",
-    "/veh.medi": "\uFB6D",
-    "/veharabic": "\u06A4",
-    "/vehfinalarabic": "\uFB6B",
-    "/vehinitialarabic": "\uFB6C",
-    "/vehmedialarabic": "\uFB6D",
-    "/vekatakana": "\u30F9",
-    "/vend": "\uA769",
-    "/venus": "\u2640",
-    "/versicle": "\u2123",
-    "/vert:bracketwhiteleft": "\uFE17",
-    "/vert:brakcetwhiteright": "\uFE18",
-    "/vert:colon": "\uFE13",
-    "/vert:comma": "\uFE10",
-    "/vert:ellipsishor": "\uFE19",
-    "/vert:exclam": "\uFE15",
-    "/vert:ideographiccomma": "\uFE11",
-    "/vert:ideographicfullstop": "\uFE12",
-    "/vert:question": "\uFE16",
-    "/vert:semicolon": "\uFE14",
-    "/vertdblhorzsng": "\u256B",
-    "/vertdblleftsng": "\u2562",
-    "/vertdblrightsng": "\u255F",
-    "/vertheavyhorzlight": "\u2542",
-    "/vertheavyleftlight": "\u2528",
-    "/vertheavyrightlight": "\u2520",
-    "/verticalTrafficLight": "\u1F6A6",
-    "/verticalbar": "\u007C",
-    "/verticalbardbl": "\u2016",
-    "/verticalbarhorizontalstroke": "\u27CA",
-    "/verticalbarwhitearrowonpedestalup": "\u21ED",
-    "/verticalfourdots": "\u205E",
-    "/verticalideographiciterationmark": "\u303B",
-    "/verticalkanarepeatmark": "\u3031",
-    "/verticalkanarepeatmarklowerhalf": "\u3035",
-    "/verticalkanarepeatmarkupperhalf": "\u3033",
-    "/verticalkanarepeatwithvoicedsoundmark": "\u3032",
-    "/verticalkanarepeatwithvoicedsoundmarkupperhalf": "\u3034",
-    "/verticallineabovecmb": "\u030D",
-    "/verticallinebelowcmb": "\u0329",
-    "/verticallinelowmod": "\u02CC",
-    "/verticallinemod": "\u02C8",
-    "/verticalmalestroke": "\u26A8",
-    "/verticalsdbltrokearrowleft": "\u21FA",
-    "/verticalsdbltrokearrowleftright": "\u21FC",
-    "/verticalsdbltrokearrowright": "\u21FB",
-    "/verticalstrokearrowleft": "\u21F7",
-    "/verticalstrokearrowleftright": "\u21F9",
-    "/verticalstrokearrowright": "\u21F8",
-    "/vertlighthorzheavy": "\u253F",
-    "/vertlightleftheavy": "\u2525",
-    "/vertlightrightheavy": "\u251D",
-    "/vertsnghorzdbl": "\u256A",
-    "/vertsngleftdbl": "\u2561",
-    "/vertsngrightdbl": "\u255E",
-    "/verymuchgreater": "\u22D9",
-    "/verymuchless": "\u22D8",
-    "/vesta": "\u26B6",
-    "/vewarmenian": "\u057E",
-    "/vhook": "\u028B",
-    "/vibrationMode": "\u1F4F3",
-    "/videoCamera": "\u1F4F9",
-    "/videoGame": "\u1F3AE",
-    "/videocassette": "\u1F4FC",
-    "/viewdatasquare": "\u2317",
-    "/vikatakana": "\u30F8",
-    "/violin": "\u1F3BB",
-    "/viramabengali": "\u09CD",
-    "/viramadeva": "\u094D",
-    "/viramagujarati": "\u0ACD",
-    "/virgo": "\u264D",
-    "/visargabengali": "\u0983",
-    "/visargadeva": "\u0903",
-    "/visargagujarati": "\u0A83",
-    "/visigothicz": "\uA763",
-    "/vmonospace": "\uFF56",
-    "/voarmenian": "\u0578",
-    "/vodsquare": "\u1F1AC",
-    "/voicediterationhiragana": "\u309E",
-    "/voicediterationkatakana": "\u30FE",
-    "/voicedmarkkana": "\u309B",
-    "/voicedmarkkanahalfwidth": "\uFF9E",
-    "/voicingmod": "\u02EC",
-    "/vokatakana": "\u30FA",
-    "/volapukae": "\uA79B",
-    "/volapukoe": "\uA79D",
-    "/volapukue": "\uA79F",
-    "/volcano": "\u1F30B",
-    "/volleyball": "\u1F3D0",
-    "/vovermfullwidth": "\u33DE",
-    "/vowelVabove": "\u065A",
-    "/voweldotbelow": "\u065C",
-    "/vowelinvertedVabove": "\u065B",
-    "/vparen": "\u24B1",
-    "/vparenthesized": "\u24B1",
-    "/vrighthook": "\u2C71",
-    "/vssquare": "\u1F19A",
-    "/vtilde": "\u1E7D",
-    "/vturned": "\u028C",
-    "/vuhiragana": "\u3094",
-    "/vukatakana": "\u30F4",
-    "/vwelsh": "\u1EFD",
-    "/vy": "\uA761",
-    "/w": "\u0077",
-    "/wacirclekatakana": "\u32FB",
-    "/wacute": "\u1E83",
-    "/waekorean": "\u3159",
-    "/wahiragana": "\u308F",
-    "/wakatakana": "\u30EF",
-    "/wakatakanahalfwidth": "\uFF9C",
-    "/wakorean": "\u3158",
-    "/waningCrescentMoon": "\u1F318",
-    "/waningGibbousMoon": "\u1F316",
-    "/warning": "\u26A0",
-    "/wasmallhiragana": "\u308E",
-    "/wasmallkatakana": "\u30EE",
-    "/wastebasket": "\u1F5D1",
-    "/watch": "\u231A",
-    "/waterBuffalo": "\u1F403",
-    "/waterCloset": "\u1F6BE",
-    "/waterWave": "\u1F30A",
-    "/waterideographiccircled": "\u328C",
-    "/waterideographicparen": "\u322C",
-    "/watermelon": "\u1F349",
-    "/wattosquare": "\u3357",
-    "/wavedash": "\u301C",
-    "/wavingBlackFlag": "\u1F3F4",
-    "/wavingHandSign": "\u1F44B",
-    "/wavingWhiteFlag": "\u1F3F3",
-    "/wavydash": "\u3030",
-    "/wavyhamzabelow": "\u065F",
-    "/wavyline": "\u2307",
-    "/wavyunderscorevertical": "\uFE34",
-    "/waw": "\u0648",
-    "/waw.fina": "\uFEEE",
-    "/waw.isol": "\uFEED",
-    "/wawDigitThreeAbove": "\u0779",
-    "/wawDigitTwoAbove": "\u0778",
-    "/wawarabic": "\u0648",
-    "/wawdotabove": "\u06CF",
-    "/wawfinalarabic": "\uFEEE",
-    "/wawhamza": "\u0624",
-    "/wawhamza.fina": "\uFE86",
-    "/wawhamza.isol": "\uFE85",
-    "/wawhamzaabovearabic": "\u0624",
-    "/wawhamzaabovefinalarabic": "\uFE86",
-    "/wawhighhamza": "\u0676",
-    "/wawring": "\u06C4",
-    "/wawsmall": "\u06E5",
-    "/wawtwodotsabove": "\u06CA",
-    "/waxingCrescentMoon": "\u1F312",
-    "/waxingGibbousMoon": "\u1F314",
-    "/wbfullwidth": "\u33DD",
-    "/wbsquare": "\u33DD",
-    "/wcircle": "\u24E6",
-    "/wcircumflex": "\u0175",
-    "/wcsquare": "\u1F14F",
-    "/wcsquareblack": "\u1F18F",
-    "/wdieresis": "\u1E85",
-    "/wdot": "\u1E87",
-    "/wdotaccent": "\u1E87",
-    "/wdotbelow": "\u1E89",
-    "/wearyCatFace": "\u1F640",
-    "/wearyFace": "\u1F629",
-    "/wecirclekatakana": "\u32FD",
-    "/wecyr": "\u051D",
-    "/wedding": "\u1F492",
-    "/wehiragana": "\u3091",
-    "/weierstrass": "\u2118",
-    "/weightLifter": "\u1F3CB",
-    "/wekatakana": "\u30F1",
-    "/wekorean": "\u315E",
-    "/weokorean": "\u315D",
-    "/westsyriaccross": "\u2670",
-    "/wgrave": "\u1E81",
-    "/whale": "\u1F40B",
-    "/wheelchair": "\u267F",
-    "/wheelofdharma": "\u2638",
-    "/whiteDownPointingBackhandIndex": "\u1F447",
-    "/whiteDownPointingLeftHandIndex": "\u1F597",
-    "/whiteFlower": "\u1F4AE",
-    "/whiteHardShellFloppyDisk": "\u1F5AB",
-    "/whiteLatinCross": "\u1F546",
-    "/whiteLeftPointingBackhandIndex": "\u1F448",
-    "/whitePennant": "\u1F3F1",
-    "/whiteRightPointingBackhandIndex": "\u1F449",
-    "/whiteSquareButton": "\u1F533",
-    "/whiteSun": "\u1F323",
-    "/whiteSunBehindCloud": "\u1F325",
-    "/whiteSunBehindCloudRain": "\u1F326",
-    "/whiteSunSmallCloud": "\u1F324",
-    "/whiteTouchtoneTelephone": "\u1F57E",
-    "/whiteUpPointingBackhandIndex": "\u1F446",
-    "/whitearrowdown": "\u21E9",
-    "/whitearrowfromwallright": "\u21F0",
-    "/whitearrowleft": "\u21E6",
-    "/whitearrowonpedestalup": "\u21EB",
-    "/whitearrowright": "\u21E8",
-    "/whitearrowup": "\u21E7",
-    "/whitearrowupdown": "\u21F3",
-    "/whitearrowupfrombar": "\u21EA",
-    "/whitebullet": "\u25E6",
-    "/whitecircle": "\u25CB",
-    "/whitecircleinverse": "\u25D9",
-    "/whitecornerbracketleft": "\u300E",
-    "/whitecornerbracketleftvertical": "\uFE43",
-    "/whitecornerbracketright": "\u300F",
-    "/whitecornerbracketrightvertical": "\uFE44",
-    "/whitedblarrowonpedestalup": "\u21EF",
-    "/whitedblarrowup": "\u21EE",
-    "/whitediamond": "\u25C7",
-    "/whitediamondcontainingblacksmalldiamond": "\u25C8",
-    "/whitedownpointingsmalltriangle": "\u25BF",
-    "/whitedownpointingtriangle": "\u25BD",
-    "/whiteleftpointingsmalltriangle": "\u25C3",
-    "/whiteleftpointingtriangle": "\u25C1",
-    "/whitelenticularbracketleft": "\u3016",
-    "/whitelenticularbracketright": "\u3017",
-    "/whiterightpointingsmalltriangle": "\u25B9",
-    "/whiterightpointingtriangle": "\u25B7",
-    "/whitesesamedot": "\uFE46",
-    "/whitesmallsquare": "\u25AB",
-    "/whitesmilingface": "\u263A",
-    "/whitesquare": "\u25A1",
-    "/whitesquarebracketleft": "\u301A",
-    "/whitesquarebracketright": "\u301B",
-    "/whitestar": "\u2606",
-    "/whitetelephone": "\u260F",
-    "/whitetortoiseshellbracketleft": "\u3018",
-    "/whitetortoiseshellbracketright": "\u3019",
-    "/whiteuppointingsmalltriangle": "\u25B5",
-    "/whiteuppointingtriangle": "\u25B3",
-    "/whook": "\u2C73",
-    "/wicirclekatakana": "\u32FC",
-    "/wigglylinevertical": "\u2E3E",
-    "/wignyan": "\uA983",
-    "/wihiragana": "\u3090",
-    "/wikatakana": "\u30F0",
-    "/wikorean": "\u315F",
-    "/windBlowingFace": "\u1F32C",
-    "/windChime": "\u1F390",
-    "/windupada": "\uA9C6",
-    "/wineGlass": "\u1F377",
-    "/winkingFace": "\u1F609",
-    "/wiredKeyboard": "\u1F5AE",
-    "/wmonospace": "\uFF57",
-    "/wocirclekatakana": "\u32FE",
-    "/wohiragana": "\u3092",
-    "/wokatakana": "\u30F2",
-    "/wokatakanahalfwidth": "\uFF66",
-    "/wolfFace": "\u1F43A",
-    "/woman": "\u1F469",
-    "/womanBunnyEars": "\u1F46F",
-    "/womansBoots": "\u1F462",
-    "/womansClothes": "\u1F45A",
-    "/womansHat": "\u1F452",
-    "/womansSandal": "\u1F461",
-    "/womens": "\u1F6BA",
-    "/won": "\u20A9",
-    "/wonmonospace": "\uFFE6",
-    "/woodideographiccircled": "\u328D",
-    "/woodideographicparen": "\u322D",
-    "/wordjoiner": "\u2060",
-    "/wordseparatormiddledot": "\u2E31",
-    "/worldMap": "\u1F5FA",
-    "/worriedFace": "\u1F61F",
-    "/wowaenthai": "\u0E27",
-    "/wparen": "\u24B2",
-    "/wparenthesized": "\u24B2",
-    "/wrappedPresent": "\u1F381",
-    "/wreathproduct": "\u2240",
-    "/wrench": "\u1F527",
-    "/wring": "\u1E98",
-    "/wsuperior": "\u02B7",
-    "/wsupmod": "\u02B7",
-    "/wturned": "\u028D",
-    "/wulumelikvowel": "\uA9B7",
-    "/wuluvowel": "\uA9B6",
-    "/wynn": "\u01BF",
-    "/x": "\u0078",
-    "/x.inferior": "\u2093",
-    "/xabovecmb": "\u033D",
-    "/xatailcyr": "\u04B3",
-    "/xbopomofo": "\u3112",
-    "/xcircle": "\u24E7",
-    "/xdieresis": "\u1E8D",
-    "/xdot": "\u1E8B",
-    "/xdotaccent": "\u1E8B",
-    "/xeharmenian": "\u056D",
-    "/xi": "\u03BE",
-    "/xmonospace": "\uFF58",
-    "/xor": "\u22BB",
-    "/xparen": "\u24B3",
-    "/xparenthesized": "\u24B3",
-    "/xsuperior": "\u02E3",
-    "/xsupmod": "\u02E3",
-    "/y": "\u0079",
-    "/yaadosquare": "\u334E",
-    "/yaarusquare": "\u334F",
-    "/yabengali": "\u09AF",
-    "/yacirclekatakana": "\u32F3",
-    "/yacute": "\u00FD",
-    "/yacyr": "\u044F",
-    "/yadeva": "\u092F",
-    "/yaecyr": "\u0519",
-    "/yaekorean": "\u3152",
-    "/yagujarati": "\u0AAF",
-    "/yagurmukhi": "\u0A2F",
-    "/yahiragana": "\u3084",
-    "/yakatakana": "\u30E4",
-    "/yakatakanahalfwidth": "\uFF94",
-    "/yakorean": "\u3151",
-    "/yamakkanthai": "\u0E4E",
-    "/yangtonemod": "\u02EB",
-    "/yasmallhiragana": "\u3083",
-    "/yasmallkatakana": "\u30E3",
-    "/yasmallkatakanahalfwidth": "\uFF6C",
-    "/yatcyr": "\u0463",
-    "/yatcyrillic": "\u0463",
-    "/ycircle": "\u24E8",
-    "/ycircumflex": "\u0177",
-    "/ydieresis": "\u00FF",
-    "/ydot": "\u1E8F",
-    "/ydotaccent": "\u1E8F",
-    "/ydotbelow": "\u1EF5",
-    "/yeh": "\u064A",
-    "/yeh.fina": "\uFEF2",
-    "/yeh.init": "\uFEF3",
-    "/yeh.init_alefmaksura.fina": "\uFC59",
-    "/yeh.init_hah.fina": "\uFC56",
-    "/yeh.init_hah.medi": "\uFCDB",
-    "/yeh.init_hamzaabove.medi_ae.fina": "\uFBEC",
-    "/yeh.init_hamzaabove.medi_alef.fina": "\uFBEA",
-    "/yeh.init_hamzaabove.medi_alefmaksura.fina": "\uFC03",
-    "/yeh.init_hamzaabove.medi_e.fina": "\uFBF6",
-    "/yeh.init_hamzaabove.medi_e.medi": "\uFBF8",
-    "/yeh.init_hamzaabove.medi_hah.fina": "\uFC01",
-    "/yeh.init_hamzaabove.medi_hah.medi": "\uFC98",
-    "/yeh.init_hamzaabove.medi_heh.medi": "\uFC9B",
-    "/yeh.init_hamzaabove.medi_jeem.fina": "\uFC00",
-    "/yeh.init_hamzaabove.medi_jeem.medi": "\uFC97",
-    "/yeh.init_hamzaabove.medi_khah.medi": "\uFC99",
-    "/yeh.init_hamzaabove.medi_meem.fina": "\uFC02",
-    "/yeh.init_hamzaabove.medi_meem.medi": "\uFC9A",
-    "/yeh.init_hamzaabove.medi_oe.fina": "\uFBF2",
-    "/yeh.init_hamzaabove.medi_u.fina": "\uFBF0",
-    "/yeh.init_hamzaabove.medi_waw.fina": "\uFBEE",
-    "/yeh.init_hamzaabove.medi_yeh.fina": "\uFC04",
-    "/yeh.init_hamzaabove.medi_yu.fina": "\uFBF4",
-    "/yeh.init_heh.medi": "\uFCDE",
-    "/yeh.init_jeem.fina": "\uFC55",
-    "/yeh.init_jeem.medi": "\uFCDA",
-    "/yeh.init_khah.fina": "\uFC57",
-    "/yeh.init_khah.medi": "\uFCDC",
-    "/yeh.init_meem.fina": "\uFC58",
-    "/yeh.init_meem.medi": "\uFCDD",
-    "/yeh.init_meem.medi_meem.medi": "\uFD9D",
-    "/yeh.init_yeh.fina": "\uFC5A",
-    "/yeh.isol": "\uFEF1",
-    "/yeh.medi": "\uFEF4",
-    "/yeh.medi_alefmaksura.fina": "\uFC95",
-    "/yeh.medi_hah.medi_yeh.fina": "\uFDAE",
-    "/yeh.medi_hamzaabove.medi_ae.fina": "\uFBED",
-    "/yeh.medi_hamzaabove.medi_alef.fina": "\uFBEB",
-    "/yeh.medi_hamzaabove.medi_alefmaksura.fina": "\uFC68",
-    "/yeh.medi_hamzaabove.medi_e.fina": "\uFBF7",
-    "/yeh.medi_hamzaabove.medi_heh.medi": "\uFCE0",
-    "/yeh.medi_hamzaabove.medi_meem.fina": "\uFC66",
-    "/yeh.medi_hamzaabove.medi_meem.medi": "\uFCDF",
-    "/yeh.medi_hamzaabove.medi_noon.fina": "\uFC67",
-    "/yeh.medi_hamzaabove.medi_oe.fina": "\uFBF3",
-    "/yeh.medi_hamzaabove.medi_reh.fina": "\uFC64",
-    "/yeh.medi_hamzaabove.medi_u.fina": "\uFBF1",
-    "/yeh.medi_hamzaabove.medi_waw.fina": "\uFBEF",
-    "/yeh.medi_hamzaabove.medi_yeh.fina": "\uFC69",
-    "/yeh.medi_hamzaabove.medi_yu.fina": "\uFBF5",
-    "/yeh.medi_hamzaabove.medi_zain.fina": "\uFC65",
-    "/yeh.medi_heh.medi": "\uFCF1",
-    "/yeh.medi_jeem.medi_yeh.fina": "\uFDAF",
-    "/yeh.medi_meem.fina": "\uFC93",
-    "/yeh.medi_meem.medi": "\uFCF0",
-    "/yeh.medi_meem.medi_meem.fina": "\uFD9C",
-    "/yeh.medi_meem.medi_yeh.fina": "\uFDB0",
-    "/yeh.medi_noon.fina": "\uFC94",
-    "/yeh.medi_reh.fina": "\uFC91",
-    "/yeh.medi_yeh.fina": "\uFC96",
-    "/yeh.medi_zain.fina": "\uFC92",
-    "/yehBarreeDigitThreeAbove": "\u077B",
-    "/yehBarreeDigitTwoAbove": "\u077A",
-    "/yehVabove": "\u06CE",
-    "/yehabove": "\u06E7",
-    "/yeharabic": "\u064A",
-    "/yehbarree": "\u06D2",
-    "/yehbarree.fina": "\uFBAF",
-    "/yehbarree.isol": "\uFBAE",
-    "/yehbarreearabic": "\u06D2",
-    "/yehbarreefinalarabic": "\uFBAF",
-    "/yehbarreehamza": "\u06D3",
-    "/yehbarreehamza.fina": "\uFBB1",
-    "/yehbarreehamza.isol": "\uFBB0",
-    "/yehfarsi": "\u06CC",
-    "/yehfarsi.fina": "\uFBFD",
-    "/yehfarsi.init": "\uFBFE",
-    "/yehfarsi.isol": "\uFBFC",
-    "/yehfarsi.medi": "\uFBFF",
-    "/yehfarsiinvertedV": "\u063D",
-    "/yehfarsithreedotsabove": "\u063F",
-    "/yehfarsitwodotsabove": "\u063E",
-    "/yehfinalarabic": "\uFEF2",
-    "/yehhamza": "\u0626",
-    "/yehhamza.fina": "\uFE8A",
-    "/yehhamza.init": "\uFE8B",
-    "/yehhamza.isol": "\uFE89",
-    "/yehhamza.medi": "\uFE8C",
-    "/yehhamzaabovearabic": "\u0626",
-    "/yehhamzaabovefinalarabic": "\uFE8A",
-    "/yehhamzaaboveinitialarabic": "\uFE8B",
-    "/yehhamzaabovemedialarabic": "\uFE8C",
-    "/yehhighhamza": "\u0678",
-    "/yehinitialarabic": "\uFEF3",
-    "/yehmedialarabic": "\uFEF4",
-    "/yehmeeminitialarabic": "\uFCDD",
-    "/yehmeemisolatedarabic": "\uFC58",
-    "/yehnoonfinalarabic": "\uFC94",
-    "/yehsmall": "\u06E6",
-    "/yehtail": "\u06CD",
-    "/yehthreedotsbelow": "\u06D1",
-    "/yehthreedotsbelowarabic": "\u06D1",
-    "/yekorean": "\u3156",
-    "/yellowHeart": "\u1F49B",
-    "/yen": "\u00A5",
-    "/yenmonospace": "\uFFE5",
-    "/yeokorean": "\u3155",
-    "/yeorinhieuhkorean": "\u3186",
-    "/yerachBenYomo:hb": "\u05AA",
-    "/yerahbenyomohebrew": "\u05AA",
-    "/yerahbenyomolefthebrew": "\u05AA",
-    "/yericyrillic": "\u044B",
-    "/yerudieresiscyrillic": "\u04F9",
-    "/yesieungkorean": "\u3181",
-    "/yesieungpansioskorean": "\u3183",
-    "/yesieungsioskorean": "\u3182",
-    "/yetiv:hb": "\u059A",
-    "/yetivhebrew": "\u059A",
-    "/ygrave": "\u1EF3",
-    "/yhoi": "\u1EF7",
-    "/yhook": "\u01B4",
-    "/yhookabove": "\u1EF7",
-    "/yiarmenian": "\u0575",
-    "/yicyrillic": "\u0457",
-    "/yikorean": "\u3162",
-    "/yintonemod": "\u02EA",
-    "/yinyang": "\u262F",
-    "/yiwnarmenian": "\u0582",
-    "/ylongcyr": "\u044B",
-    "/ylongdieresiscyr": "\u04F9",
-    "/yloop": "\u1EFF",
-    "/ymacron": "\u0233",
-    "/ymonospace": "\uFF59",
-    "/yocirclekatakana": "\u32F5",
-    "/yod": "\u05D9",
-    "/yod:hb": "\u05D9",
-    "/yod_yod:hb": "\u05F2",
-    "/yod_yod_patah:hb": "\uFB1F",
-    "/yoddagesh": "\uFB39",
-    "/yoddageshhebrew": "\uFB39",
-    "/yodhebrew": "\u05D9",
-    "/yodwithdagesh:hb": "\uFB39",
-    "/yodwithhiriq:hb": "\uFB1D",
-    "/yodyodhebrew": "\u05F2",
-    "/yodyodpatahhebrew": "\uFB1F",
-    "/yogh": "\u021D",
-    "/yohiragana": "\u3088",
-    "/yoikorean": "\u3189",
-    "/yokatakana": "\u30E8",
-    "/yokatakanahalfwidth": "\uFF96",
-    "/yokorean": "\u315B",
-    "/yosmallhiragana": "\u3087",
-    "/yosmallkatakana": "\u30E7",
-    "/yosmallkatakanahalfwidth": "\uFF6E",
-    "/yot": "\u03F3",
-    "/yotgreek": "\u03F3",
-    "/yoyaekorean": "\u3188",
-    "/yoyakorean": "\u3187",
-    "/yoyakthai": "\u0E22",
-    "/yoyingthai": "\u0E0D",
-    "/yparen": "\u24B4",
-    "/yparenthesized": "\u24B4",
-    "/ypogegrammeni": "\u037A",
-    "/ypogegrammenigreekcmb": "\u0345",
-    "/yr": "\u01A6",
-    "/yring": "\u1E99",
-    "/ystroke": "\u024F",
-    "/ysuperior": "\u02B8",
-    "/ysupmod": "\u02B8",
-    "/ytilde": "\u1EF9",
-    "/yturned": "\u028E",
-    "/yu.fina": "\uFBDC",
-    "/yu.isol": "\uFBDB",
-    "/yuansquare": "\u3350",
-    "/yucirclekatakana": "\u32F4",
-    "/yucyr": "\u044E",
-    "/yuhiragana": "\u3086",
-    "/yuikorean": "\u318C",
-    "/yukatakana": "\u30E6",
-    "/yukatakanahalfwidth": "\uFF95",
-    "/yukirghiz": "\u06C9",
-    "/yukirghiz.fina": "\uFBE3",
-    "/yukirghiz.isol": "\uFBE2",
-    "/yukorean": "\u3160",
-    "/yukrcyr": "\u0457",
-    "/yusbigcyr": "\u046B",
-    "/yusbigcyrillic": "\u046B",
-    "/yusbigiotifiedcyr": "\u046D",
-    "/yusbigiotifiedcyrillic": "\u046D",
-    "/yuslittlecyr": "\u0467",
-    "/yuslittlecyrillic": "\u0467",
-    "/yuslittleiotifiedcyr": "\u0469",
-    "/yuslittleiotifiedcyrillic": "\u0469",
-    "/yusmallhiragana": "\u3085",
-    "/yusmallkatakana": "\u30E5",
-    "/yusmallkatakanahalfwidth": "\uFF6D",
-    "/yuyekorean": "\u318B",
-    "/yuyeokorean": "\u318A",
-    "/yyabengali": "\u09DF",
-    "/yyadeva": "\u095F",
-    "/z": "\u007A",
-    "/zaarmenian": "\u0566",
-    "/zacute": "\u017A",
-    "/zadeva": "\u095B",
-    "/zagurmukhi": "\u0A5B",
-    "/zah": "\u0638",
-    "/zah.fina": "\uFEC6",
-    "/zah.init": "\uFEC7",
-    "/zah.init_meem.fina": "\uFC28",
-    "/zah.init_meem.medi": "\uFCB9",
-    "/zah.isol": "\uFEC5",
-    "/zah.medi": "\uFEC8",
-    "/zah.medi_meem.medi": "\uFD3B",
-    "/zaharabic": "\u0638",
-    "/zahfinalarabic": "\uFEC6",
-    "/zahinitialarabic": "\uFEC7",
-    "/zahiragana": "\u3056",
-    "/zahmedialarabic": "\uFEC8",
-    "/zain": "\u0632",
-    "/zain.fina": "\uFEB0",
-    "/zain.isol": "\uFEAF",
-    "/zainabove": "\u0617",
-    "/zainarabic": "\u0632",
-    "/zainfinalarabic": "\uFEB0",
-    "/zakatakana": "\u30B6",
-    "/zaqefGadol:hb": "\u0595",
-    "/zaqefQatan:hb": "\u0594",
-    "/zaqefgadolhebrew": "\u0595",
-    "/zaqefqatanhebrew": "\u0594",
-    "/zarqa:hb": "\u0598",
-    "/zarqahebrew": "\u0598",
-    "/zayin": "\u05D6",
-    "/zayin:hb": "\u05D6",
-    "/zayindagesh": "\uFB36",
-    "/zayindageshhebrew": "\uFB36",
-    "/zayinhebrew": "\u05D6",
-    "/zayinwithdagesh:hb": "\uFB36",
-    "/zbopomofo": "\u3117",
-    "/zcaron": "\u017E",
-    "/zcircle": "\u24E9",
-    "/zcircumflex": "\u1E91",
-    "/zcurl": "\u0291",
-    "/zdescender": "\u2C6C",
-    "/zdot": "\u017C",
-    "/zdotaccent": "\u017C",
-    "/zdotbelow": "\u1E93",
-    "/zecyr": "\u0437",
-    "/zecyrillic": "\u0437",
-    "/zedescendercyrillic": "\u0499",
-    "/zedieresiscyr": "\u04DF",
-    "/zedieresiscyrillic": "\u04DF",
-    "/zehiragana": "\u305C",
-    "/zekatakana": "\u30BC",
-    "/zero": "\u0030",
-    "/zero.inferior": "\u2080",
-    "/zero.superior": "\u2070",
-    "/zeroarabic": "\u0660",
-    "/zerobengali": "\u09E6",
-    "/zerocircle": "\u24EA",
-    "/zerocircleblack": "\u24FF",
-    "/zerocomma": "\u1F101",
-    "/zerodeva": "\u0966",
-    "/zerofar": "\u06F0",
-    "/zerofullstop": "\u1F100",
-    "/zerogujarati": "\u0AE6",
-    "/zerogurmukhi": "\u0A66",
-    "/zerohackarabic": "\u0660",
-    "/zeroinferior": "\u2080",
-    "/zeromonospace": "\uFF10",
-    "/zerooldstyle": "\uF730",
-    "/zeropersian": "\u06F0",
-    "/zerosquareabove": "\u06E0",
-    "/zerosuperior": "\u2070",
-    "/zerothai": "\u0E50",
-    "/zerothirds": "\u2189",
-    "/zerowidthjoiner": "\uFEFF",
-    "/zerowidthnobreakspace": "\uFEFF",
-    "/zerowidthnonjoiner": "\u200C",
-    "/zerowidthspace": "\u200B",
-    "/zeta": "\u03B6",
-    "/zetailcyr": "\u0499",
-    "/zhbopomofo": "\u3113",
-    "/zhearmenian": "\u056A",
-    "/zhebrevecyr": "\u04C2",
-    "/zhebrevecyrillic": "\u04C2",
-    "/zhecyr": "\u0436",
-    "/zhecyrillic": "\u0436",
-    "/zhedescendercyrillic": "\u0497",
-    "/zhedieresiscyr": "\u04DD",
-    "/zhedieresiscyrillic": "\u04DD",
-    "/zhetailcyr": "\u0497",
-    "/zhook": "\u0225",
-    "/zihiragana": "\u3058",
-    "/zikatakana": "\u30B8",
-    "/zildefunc": "\u236C",
-    "/zinorhebrew": "\u05AE",
-    "/zjekomicyr": "\u0505",
-    "/zlinebelow": "\u1E95",
-    "/zmonospace": "\uFF5A",
-    "/znotationbagmembership": "\u22FF",
-    "/zohiragana": "\u305E",
-    "/zokatakana": "\u30BE",
-    "/zparen": "\u24B5",
-    "/zparenthesized": "\u24B5",
-    "/zretroflex": "\u0290",
-    "/zretroflexhook": "\u0290",
-    "/zstroke": "\u01B6",
-    "/zswashtail": "\u0240",
-    "/zuhiragana": "\u305A",
-    "/zukatakana": "\u30BA",
-    "/zwarakay": "\u0659",
-}
-
-
-def _complete() -> None:
-    global adobe_glyphs
-    for i in range(256):
-        adobe_glyphs[f"/a{i}"] = chr(i)
-    adobe_glyphs["/.notdef"] = "□"
-
-
-_complete()
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/prompts/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/prompts/__init__.py
deleted file mode 100644
index 70a9a06c14c38bb373b8afc1d1e6b4b7febf726e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/prompts/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Prompt class."""
-
-from gpt_index.prompts.base import Prompt
-
-__all__ = ["Prompt"]
diff --git a/spaces/justest/gpt4free/g4f/.v1/gui/README.md b/spaces/justest/gpt4free/g4f/.v1/gui/README.md
deleted file mode 100644
index c0406216bd922ab43eb2241496a816e7b747d0de..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/gui/README.md
+++ /dev/null
@@ -1,78 +0,0 @@
-# gpt4free gui
-
-This code provides a Graphical User Interface (GUI) for gpt4free. Users can ask questions and get answers from GPT-4 API's, utilizing multiple API implementations. The project contains two different Streamlit applications: `streamlit_app.py` and `streamlit_chat_app.py`.
-
-In addition, a new GUI script specifically implemented using PyWebIO has been added and can be found in the pywebio-gui folder. If there are errors with the Streamlit version, you can try using the PyWebIO version instead
-
-Installation
-------------
-
-1.  Clone the repository.
-2.  Install the required dependencies with: `pip install -r requirements.txt`.
-3.  To use `streamlit_chat_app.py`, note that it depends on a pull request (PR #24) from the https://github.com/AI-Yash/st-chat/ repository, which may change in the future. The current dependency library can be found at https://github.com/AI-Yash/st-chat/archive/refs/pull/24/head.zip.
-
-Analytics Disclaimer
------
-The streamlit browser app collects heavy analytics even when running locally. This includes events for every page load, form submission including metadata on queries (like length), browser and client information including host ips. These are all transmitted to a 3rd party analytics group, Segment.com.
-
-Usage
------
-
-Choose one of the Streamlit applications to run:
-
-### streamlit\_app.py
-
-This application provides a simple interface for asking GPT-4 questions and receiving answers.
-
-To run the application:
-
-run:
-```arduino
-streamlit run gui/streamlit_app.py
-```
-<br>
-
-<img width="724" alt="image" src="https://user-images.githubusercontent.com/98614666/234232449-0d5cd092-a29d-4759-8197-e00ba712cb1a.png">   
-
-<br>
-<br>
-
-preview:  
-
-<img width="1125" alt="image" src="https://user-images.githubusercontent.com/98614666/234232398-09e9d3c5-08e6-4b8a-b4f2-0666e9790c7d.png">    
-
-
-### streamlit\_chat\_app.py
-
-This application provides a chat-like interface for asking GPT-4 questions and receiving answers. It supports multiple query methods, and users can select the desired API for their queries. The application also maintains a conversation history.
-
-To run the application:
-
-```arduino
-streamlit run streamlit_chat_app.py
-```
-
-<br>
-
-<img width="724" alt="image" src="image1.png">   
-
-<br>
-<br>
-
-preview:  
-
-<img width="1125" alt="image" src="image2.png">    
-
-Contributing
-------------
-
-Feel free to submit pull requests, report bugs, or request new features by opening issues on the GitHub repository.
-
-Bug
-----
-There is a bug in `streamlit_chat_app.py` right now that I haven't pinpointed yet, probably is really simple but havent had the time to look for it. Whenever you open a new conversation or access an old conversation it will only start prompt-answering after the second time you input to the text input, other than that, everything else seems to work accordingly.
-
-License
--------
-
-This project is licensed under the MIT License.
diff --git a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/file_processing.py b/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/file_processing.py
deleted file mode 100644
index c70df2f32aaeb017bd607e3fa8fe9662e65944e1..0000000000000000000000000000000000000000
--- a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/file_processing.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import os
-# i move all to one folder and remove the names to shorten the path files
-os.chdir("C:\\Users\\drago\\Downloads\\adl-piano-midi")
-count = 0
-for i in os.listdir("."):
-    path = i
-    for j in os.listdir(path):
-        path2 = path + "\\" + j
-        for k in os.listdir(path2):
-            path3 = path2 + "\\" + k
-            for music_name in os.listdir(path3):
-                count += 1
-                music_path = path3 + "\\" + music_name
-                os.rename(music_path, str(count) + ".mid")
\ No newline at end of file
diff --git a/spaces/kdrkdrkdr/LisaTTS/mel_processing.py b/spaces/kdrkdrkdr/LisaTTS/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/LisaTTS/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/utils/argutils.py b/spaces/keithhon/Real-Time-Voice-Cloning/utils/argutils.py
deleted file mode 100644
index db41683027173517c910e3b259f8da48207dcb38..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/utils/argutils.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from pathlib import Path
-import numpy as np
-import argparse
-
-_type_priorities = [    # In decreasing order
-    Path,
-    str,
-    int,
-    float,
-    bool,
-]
-
-def _priority(o):
-    p = next((i for i, t in enumerate(_type_priorities) if type(o) is t), None) 
-    if p is not None:
-        return p
-    p = next((i for i, t in enumerate(_type_priorities) if isinstance(o, t)), None) 
-    if p is not None:
-        return p
-    return len(_type_priorities)
-
-def print_args(args: argparse.Namespace, parser=None):
-    args = vars(args)
-    if parser is None:
-        priorities = list(map(_priority, args.values()))
-    else:
-        all_params = [a.dest for g in parser._action_groups for a in g._group_actions ]
-        priority = lambda p: all_params.index(p) if p in all_params else len(all_params)
-        priorities = list(map(priority, args.keys()))
-    
-    pad = max(map(len, args.keys())) + 3
-    indices = np.lexsort((list(args.keys()), priorities))
-    items = list(args.items())
-    
-    print("Arguments:")
-    for i in indices:
-        param, value = items[i]
-        print("    {0}:{1}{2}".format(param, ' ' * (pad - len(param)), value))
-    print("")
-    
\ No newline at end of file
diff --git a/spaces/ketangandhi/demo-space/app.py b/spaces/ketangandhi/demo-space/app.py
deleted file mode 100644
index 156c41d8363f53cd8ada8bfe9702d66e4e260b9e..0000000000000000000000000000000000000000
--- a/spaces/ketangandhi/demo-space/app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# -*- coding: utf-8 -*-
-"""gradiochatbot.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1Hoxj1hHm_kMWhG_W4Tq-7fME0Sak8G7N
-"""
-import gradio as gr
-
-def yes_man(message, history):
-    if message.endswith("?"):
-        return "Yes"
-    else:
-        return "Ask me anything!"
-
-gr.ChatInterface(
-    yes_man,
-    chatbot=gr.Chatbot(height=300),
-    textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
-    title="Yes Man",
-    description="Ask Yes Man any question",
-    theme="soft",
-    examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
-    cache_examples=True,
-    retry_btn=None,
-    undo_btn="Delete Previous",
-    clear_btn="Clear",
-).launch(share=True)
-
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/options/train_options.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/options/train_options.py
deleted file mode 100644
index 1337bfdd5f372b5c686a91b394a2aadbe5741f44..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/options/train_options.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""This script contains the training options for Deep3DFaceRecon_pytorch
-"""
-
-from .base_options import BaseOptions
-from util import util
-
-class TrainOptions(BaseOptions):
-    """This class includes training options.
-
-    It also includes shared options defined in BaseOptions.
-    """
-
-    def initialize(self, parser):
-        parser = BaseOptions.initialize(self, parser)
-        # dataset parameters
-        # for train
-        parser.add_argument('--data_root', type=str, default='./', help='dataset root')
-        parser.add_argument('--flist', type=str, default='datalist/train/masks.txt', help='list of mask names of training set')
-        parser.add_argument('--batch_size', type=int, default=32)
-        parser.add_argument('--dataset_mode', type=str, default='flist', help='chooses how datasets are loaded. [None | flist]')
-        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
-        parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
-        parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
-        parser.add_argument('--preprocess', type=str, default='shift_scale_rot_flip', help='scaling and cropping of images at load time [shift_scale_rot_flip | shift_scale | shift | shift_rot_flip ]')
-        parser.add_argument('--use_aug', type=util.str2bool, nargs='?', const=True, default=True, help='whether use data augmentation')
-
-        # for val
-        parser.add_argument('--flist_val', type=str, default='datalist/val/masks.txt', help='list of mask names of val set')
-        parser.add_argument('--batch_size_val', type=int, default=32)
-
-
-        # visualization parameters
-        parser.add_argument('--display_freq', type=int, default=1000, help='frequency of showing training results on screen')
-        parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
-        
-        # network saving and loading parameters
-        parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
-        parser.add_argument('--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs')
-        parser.add_argument('--evaluation_freq', type=int, default=5000, help='evaluation freq')
-        parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration')
-        parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
-        parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')
-        parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
-        parser.add_argument('--pretrained_name', type=str, default=None, help='resume training from another checkpoint')
-
-        # training parameters
-        parser.add_argument('--n_epochs', type=int, default=20, help='number of epochs with the initial learning rate')
-        parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam')
-        parser.add_argument('--lr_policy', type=str, default='step', help='learning rate policy. [linear | step | plateau | cosine]')
-        parser.add_argument('--lr_decay_epochs', type=int, default=10, help='multiply by a gamma every lr_decay_epochs epoches')
-
-        self.isTrain = True
-        return parser
diff --git a/spaces/kevinwang676/rvc-models-new/infer_pack/modules.py b/spaces/kevinwang676/rvc-models-new/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/rvc-models-new/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel/train/loss.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel/train/loss.py
deleted file mode 100644
index 301248cc1ef24c549499e10396ae6c3afab3ba09..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg2mel/train/loss.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from typing import Dict
-from typing import Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..utils.nets_utils import make_pad_mask
-
-
-class MaskedMSELoss(nn.Module):
-    def __init__(self, frames_per_step):
-        super().__init__()
-        self.frames_per_step = frames_per_step
-        self.mel_loss_criterion = nn.MSELoss(reduction='none')
-        # self.loss = nn.MSELoss()
-        self.stop_loss_criterion = nn.BCEWithLogitsLoss(reduction='none')   
-
-    def get_mask(self, lengths, max_len=None):
-        # lengths: [B,]
-        if max_len is None:
-            max_len = torch.max(lengths)
-        batch_size = lengths.size(0)
-        seq_range = torch.arange(0, max_len).long()
-        seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len).to(lengths.device)
-        seq_length_expand = lengths.unsqueeze(1).expand_as(seq_range_expand)
-        return (seq_range_expand < seq_length_expand).float()
-
-    def forward(self, mel_pred, mel_pred_postnet, mel_trg, lengths, 
-                stop_target, stop_pred):
-        ## process stop_target
-        B = stop_target.size(0)
-        stop_target = stop_target.reshape(B, -1, self.frames_per_step)[:, :, 0]
-        stop_lengths = torch.ceil(lengths.float() / self.frames_per_step).long()
-        stop_mask = self.get_mask(stop_lengths, int(mel_trg.size(1)/self.frames_per_step))
-
-        mel_trg.requires_grad = False
-        # (B, T, 1)
-        mel_mask = self.get_mask(lengths, mel_trg.size(1)).unsqueeze(-1)
-        # (B, T, D)
-        mel_mask = mel_mask.expand_as(mel_trg)
-        mel_loss_pre = (self.mel_loss_criterion(mel_pred, mel_trg) * mel_mask).sum() / mel_mask.sum()
-        mel_loss_post = (self.mel_loss_criterion(mel_pred_postnet, mel_trg) * mel_mask).sum() / mel_mask.sum()
-        
-        mel_loss = mel_loss_pre + mel_loss_post
-
-        # stop token loss
-        stop_loss = torch.sum(self.stop_loss_criterion(stop_pred, stop_target) * stop_mask) / stop_mask.sum()
-        
-        return mel_loss, stop_loss
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/fcn_hr18.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/fcn_hr18.py
deleted file mode 100644
index c3e299bc89ada56ca14bbffcbdb08a586b8ed9e9..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/configs/_base_/models/fcn_hr18.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://msra/hrnetv2_w18',
-    backbone=dict(
-        type='HRNet',
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        extra=dict(
-            stage1=dict(
-                num_modules=1,
-                num_branches=1,
-                block='BOTTLENECK',
-                num_blocks=(4, ),
-                num_channels=(64, )),
-            stage2=dict(
-                num_modules=1,
-                num_branches=2,
-                block='BASIC',
-                num_blocks=(4, 4),
-                num_channels=(18, 36)),
-            stage3=dict(
-                num_modules=4,
-                num_branches=3,
-                block='BASIC',
-                num_blocks=(4, 4, 4),
-                num_channels=(18, 36, 72)),
-            stage4=dict(
-                num_modules=3,
-                num_branches=4,
-                block='BASIC',
-                num_blocks=(4, 4, 4, 4),
-                num_channels=(18, 36, 72, 144)))),
-    decode_head=dict(
-        type='FCNHead',
-        in_channels=[18, 36, 72, 144],
-        in_index=(0, 1, 2, 3),
-        channels=sum([18, 36, 72, 144]),
-        input_transform='resize_concat',
-        kernel_size=1,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=-1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/kmfoda/bittensor_lmeh_evaluations/eval_bittensor.py b/spaces/kmfoda/bittensor_lmeh_evaluations/eval_bittensor.py
deleted file mode 100644
index 1b3b1312edc4b9cc2ebec6c252e85136ee9f3340..0000000000000000000000000000000000000000
--- a/spaces/kmfoda/bittensor_lmeh_evaluations/eval_bittensor.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import collections
-import lm_eval.tasks
-import random
-import time
-from datetime import datetime as dt
-import bittensor as bt
-from tqdm import tqdm
-import json
-
-import http.client
-import os
-from argparse import ArgumentParser
-
-parser = ArgumentParser()
-
-parser.add_argument("--validator", required=True, type=str, help="validator name", choices=["opentensor_foundation", "taostats"], default="float16")
-args = parser.parse_args()
-
-default_prompt = '''
-You are Chattensor.
-Chattensor is a research project by Opentensor Cortex.
-Chattensor is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Chattensor is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
-'''
-
-if args.validator == "taostats":
-    print("TAOOOOSATATS")
-    try:
-        bitapai_key = os.environ["BITAPAI_KEY"]
-        conn = http.client.HTTPSConnection("dashboard.bitapai.io")
-        headers = {
-        'Content-Type': 'application/json',
-        'X-API-KEY': bitapai_key
-        }
-    except KeyError:
-        raise RuntimeError(f"BITAPAI_KEY does not exist and chosen validator is taostats. Please set your bitapai key using export BITAPAI_KEY=x.")
-        
-
-def get_response(prompt):
-    if args.validator == "taostats":
-        payload = json.dumps({
-        "system": default_prompt,
-        "user": prompt
-        })
-        conn.request("POST", "/api/v1/prompt", payload, headers)
-        res = conn.getresponse()
-        data = res.read()
-        # print('test')
-        print(data)
-        time.sleep(1)
-        return data.decode("utf-8")
-    else:
-        return bt.prompt(prompt)
-
-# Load all the LMEH tasks
-tasks = ["hellaswag", "arc_challenge", "truthfulqa_mc", "hendrycksTest-abstract_algebra", "hendrycksTest-anatomy", "hendrycksTest-astronomy", "hendrycksTest-business_ethics", "hendrycksTest-clinical_knowledge", "hendrycksTest-college_biology", "hendrycksTest-college_chemistry", "hendrycksTest-college_computer_science", "hendrycksTest-college_mathematics", "hendrycksTest-college_medicine", "hendrycksTest-college_physics", "hendrycksTest-computer_security", "hendrycksTest-conceptual_physics", "hendrycksTest-econometrics", "hendrycksTest-electrical_engineering", "hendrycksTest-elementary_mathematics", "hendrycksTest-formal_logic", "hendrycksTest-global_facts", "hendrycksTest-high_school_biology", "hendrycksTest-high_school_chemistry", "hendrycksTest-high_school_computer_science", "hendrycksTest-high_school_european_history", "hendrycksTest-high_school_geography", "hendrycksTest-high_school_government_and_politics", "hendrycksTest-high_school_macroeconomics", "hendrycksTest-high_school_mathematics", "hendrycksTest-high_school_microeconomics", "hendrycksTest-high_school_physics", "hendrycksTest-high_school_psychology", "hendrycksTest-high_school_statistics", "hendrycksTest-high_school_us_history", "hendrycksTest-high_school_world_history", "hendrycksTest-human_aging", "hendrycksTest-human_sexuality", "hendrycksTest-international_law", "hendrycksTest-jurisprudence", "hendrycksTest-logical_fallacies", "hendrycksTest-machine_learning", "hendrycksTest-management", "hendrycksTest-marketing", "hendrycksTest-medical_genetics", "hendrycksTest-miscellaneous", "hendrycksTest-moral_disputes", "hendrycksTest-moral_scenarios", "hendrycksTest-nutrition", "hendrycksTest-philosophy", "hendrycksTest-prehistory", "hendrycksTest-professional_accounting", "hendrycksTest-professional_law", "hendrycksTest-professional_medicine", "hendrycksTest-professional_psychology", "hendrycksTest-public_relations", "hendrycksTest-security_studies", "hendrycksTest-sociology", "hendrycksTest-us_foreign_policy", "hendrycksTest-virology", "hendrycksTest-world_religions"]
-task_dict = lm_eval.tasks.get_task_dict(tasks)
-task_dict_items = [
-    (name, task)
-    for name, task in task_dict.items()
-    if (task.has_validation_docs() or task.has_test_docs())
-]
-versions = collections.defaultdict(dict)
-
-# get lists of each type of request
-for task_name, task in task_dict_items:
-    versions[task_name] = task.VERSION
-    # default to test doc, fall back to val doc if validation unavailable
-    # TODO: the test-fallback-to-val system isn't final, we should revisit it at some point
-    if task.has_test_docs():
-        task_doc_func = task.test_docs
-        task_set = "test"  # Required for caching in the decontamination
-    elif task.has_validation_docs():
-        task_set = "val"  # Required for caching in the decontamination
-        task_doc_func = task.validation_docs
-    else:
-        raise RuntimeError("Task has neither test_docs nor validation_docs")
-        # deterministically shuffle docs and chop off the first `limit` because sometimes docs are in some kind of order
-    task_docs = list(task_doc_func())
-    rnd = random.Random()
-    rnd.seed(42)
-    rnd.shuffle(task_docs)
-
-    i=0
-    for task_doc in tqdm(task_docs):
-        print(task_name)
-        print(task_doc)
-        if ("result" in task_doc) and ("inference_time" in task_doc) and ("prompt" in task_doc) and ("result" in task_doc) and (task_doc['result'] != ""):
-            continue
-
-        query = task_doc["query"] if "query" in task_doc else ""
-        choices_list = "\n".join([str(number+1) + ". " + choice for number, choice in enumerate(task_doc["choices"])]) if "choices" in task_doc else ""
-        number_list = ",".join([str(number) for number in range(1,len(task_doc["choices"])+1)]) if "choices" in task_doc else ""
-
-        if (task_name == "hellaswag") :
-            prompt = ""
-            prompt_list = list(task.training_docs())[:10]
-            for prompt_item in prompt_list:
-                prompt_item_query = prompt_item["query"]
-                prompt_item_choices_list = "\n".join([str(number+1) + ". " + choice for number, choice in enumerate(prompt_item["choices"])])
-                prompt_item_number_list = ",".join([str(number) for number in range(1,len(prompt_item["choices"])+1)])
-                prompt_item_gold = prompt_item["gold"]+1
-
-                prompt += f"""{prompt_item_query}...\n{prompt_item_choices_list}\nRespond with just one number only: {prompt_item_number_list}.\n{prompt_item_gold}\n\n"""
-
-            prompt += f"""{query}...\n{choices_list}\nRespond with just one number only: {number_list}. """
-        
-        elif (task_name == "arc_challenge"):
-            prompt = ""
-            prompt_list = list(task.training_docs())[:25]
-            for prompt_item in prompt_list:
-                prompt_item_query = prompt_item["query"]
-                prompt_item_choices_list = "\n".join([str(number+1) + ". " + choice for number, choice in enumerate(prompt_item["choices"])])
-                prompt_item_number_list = ",".join([str(number) for number in range(1,len(prompt_item["choices"])+1)])
-                prompt_item_gold = prompt_item["gold"]+1
-
-                prompt += f"""{prompt_item_query}...\n{prompt_item_choices_list}\nRespond with just one number only: {prompt_item_number_list}.\n{prompt_item_gold}\n\n"""
-
-            prompt += f"""{query}...\n{choices_list}\nRespond with just one number only: {number_list}. """
-
-
-        elif (task_name == "truthfulqa_mc"):
-            continue
-            prompt = ""
-            
-        elif ("hendrycksTest" in task_name):
-            prompt = ""
-            prompt_list = list(task.test_docs())[:5]
-            for prompt_item in prompt_list:
-                prompt_item_query = prompt_item["query"]
-
-                prompt += f"""{prompt_item_query.replace("Answer:", "Respond with just one letter only: A, B, C, D:")}\n{["A", "B", "C", "D"][prompt_item["gold"]]}\n\n"""
-
-            prompt += query.replace("Answer:", "Respond with just one letter only: A, B, C, D:")
-
-        # print(prompt)
-
-        start = time.time()
-        task_doc["result"] = get_response(prompt)
-        end = time.time()
-        task_doc["inference_time"] = end - start
-        task_doc["prompt"] = prompt
-        task_doc["datetime"] = dt.now().strftime(format = "%Y-%m-%d %H:%M:%S")
-        print(task_doc["result"])
-
-        i = i + 1
-        if ((i % 100) / 1000 == 0):
-            with open(f"""_results/few-shot/{args.validator}/{task_name}_results.json""", "w") as final:
-                json.dump(task_docs, final)
-
-    with open(f"""_results/few-shot/{args.validator}/{task_name}_results.json""", "w") as final:
-        json.dump(task_docs, final)
diff --git a/spaces/kokofixcomputers/chat-ui/src/routes/settings/+page.server.ts b/spaces/kokofixcomputers/chat-ui/src/routes/settings/+page.server.ts
deleted file mode 100644
index 582c009559edee2733ac60aa3cc9a271fb2a5130..0000000000000000000000000000000000000000
--- a/spaces/kokofixcomputers/chat-ui/src/routes/settings/+page.server.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-import { base } from "$app/paths";
-import { collections } from "$lib/server/database";
-import { redirect } from "@sveltejs/kit";
-import { z } from "zod";
-import { models, validateModel } from "$lib/server/models";
-import { authCondition } from "$lib/server/auth";
-import { DEFAULT_SETTINGS } from "$lib/types/Settings";
-
-export const actions = {
-	default: async function ({ request, locals }) {
-		const formData = await request.formData();
-
-		const { ethicsModalAccepted, ...settings } = z
-			.object({
-				shareConversationsWithModelAuthors: z
-					.boolean({ coerce: true })
-					.default(DEFAULT_SETTINGS.shareConversationsWithModelAuthors),
-				ethicsModalAccepted: z.boolean({ coerce: true }).optional(),
-				activeModel: validateModel(models),
-			})
-			.parse({
-				shareConversationsWithModelAuthors: formData.get("shareConversationsWithModelAuthors"),
-				ethicsModalAccepted: formData.get("ethicsModalAccepted"),
-				activeModel: formData.get("activeModel") ?? DEFAULT_SETTINGS.activeModel,
-			});
-
-		await collections.settings.updateOne(
-			authCondition(locals),
-			{
-				$set: {
-					...settings,
-					...(ethicsModalAccepted && { ethicsModalAcceptedAt: new Date() }),
-					updatedAt: new Date(),
-				},
-				$setOnInsert: {
-					createdAt: new Date(),
-				},
-			},
-			{
-				upsert: true,
-			}
-		);
-
-		throw redirect(303, request.headers.get("referer") || `${base}/`);
-	},
-};
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/IptcImagePlugin.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/IptcImagePlugin.py
deleted file mode 100644
index 4c47b55c1a5c7445e430a55e984de303ed4713f5..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/IptcImagePlugin.py
+++ /dev/null
@@ -1,230 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# IPTC/NAA file handling
-#
-# history:
-# 1995-10-01 fl   Created
-# 1998-03-09 fl   Cleaned up and added to PIL
-# 2002-06-18 fl   Added getiptcinfo helper
-#
-# Copyright (c) Secret Labs AB 1997-2002.
-# Copyright (c) Fredrik Lundh 1995.
-#
-# See the README file for information on usage and redistribution.
-#
-import os
-import tempfile
-
-from . import Image, ImageFile
-from ._binary import i8
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-from ._binary import o8
-
-COMPRESSION = {1: "raw", 5: "jpeg"}
-
-PAD = o8(0) * 4
-
-
-#
-# Helpers
-
-
-def i(c):
-    return i32((PAD + c)[-4:])
-
-
-def dump(c):
-    for i in c:
-        print("%02x" % i8(i), end=" ")
-    print()
-
-
-##
-# Image plugin for IPTC/NAA datastreams.  To read IPTC/NAA fields
-# from TIFF and JPEG files, use the <b>getiptcinfo</b> function.
-
-
-class IptcImageFile(ImageFile.ImageFile):
-    format = "IPTC"
-    format_description = "IPTC/NAA"
-
-    def getint(self, key):
-        return i(self.info[key])
-
-    def field(self):
-        #
-        # get a IPTC field header
-        s = self.fp.read(5)
-        if not len(s):
-            return None, 0
-
-        tag = s[1], s[2]
-
-        # syntax
-        if s[0] != 0x1C or tag[0] < 1 or tag[0] > 9:
-            msg = "invalid IPTC/NAA file"
-            raise SyntaxError(msg)
-
-        # field size
-        size = s[3]
-        if size > 132:
-            msg = "illegal field length in IPTC/NAA file"
-            raise OSError(msg)
-        elif size == 128:
-            size = 0
-        elif size > 128:
-            size = i(self.fp.read(size - 128))
-        else:
-            size = i16(s, 3)
-
-        return tag, size
-
-    def _open(self):
-        # load descriptive fields
-        while True:
-            offset = self.fp.tell()
-            tag, size = self.field()
-            if not tag or tag == (8, 10):
-                break
-            if size:
-                tagdata = self.fp.read(size)
-            else:
-                tagdata = None
-            if tag in self.info:
-                if isinstance(self.info[tag], list):
-                    self.info[tag].append(tagdata)
-                else:
-                    self.info[tag] = [self.info[tag], tagdata]
-            else:
-                self.info[tag] = tagdata
-
-        # mode
-        layers = i8(self.info[(3, 60)][0])
-        component = i8(self.info[(3, 60)][1])
-        if (3, 65) in self.info:
-            id = i8(self.info[(3, 65)][0]) - 1
-        else:
-            id = 0
-        if layers == 1 and not component:
-            self.mode = "L"
-        elif layers == 3 and component:
-            self.mode = "RGB"[id]
-        elif layers == 4 and component:
-            self.mode = "CMYK"[id]
-
-        # size
-        self._size = self.getint((3, 20)), self.getint((3, 30))
-
-        # compression
-        try:
-            compression = COMPRESSION[self.getint((3, 120))]
-        except KeyError as e:
-            msg = "Unknown IPTC image compression"
-            raise OSError(msg) from e
-
-        # tile
-        if tag == (8, 10):
-            self.tile = [
-                ("iptc", (compression, offset), (0, 0, self.size[0], self.size[1]))
-            ]
-
-    def load(self):
-        if len(self.tile) != 1 or self.tile[0][0] != "iptc":
-            return ImageFile.ImageFile.load(self)
-
-        type, tile, box = self.tile[0]
-
-        encoding, offset = tile
-
-        self.fp.seek(offset)
-
-        # Copy image data to temporary file
-        o_fd, outfile = tempfile.mkstemp(text=False)
-        o = os.fdopen(o_fd)
-        if encoding == "raw":
-            # To simplify access to the extracted file,
-            # prepend a PPM header
-            o.write("P5\n%d %d\n255\n" % self.size)
-        while True:
-            type, size = self.field()
-            if type != (8, 10):
-                break
-            while size > 0:
-                s = self.fp.read(min(size, 8192))
-                if not s:
-                    break
-                o.write(s)
-                size -= len(s)
-        o.close()
-
-        try:
-            with Image.open(outfile) as _im:
-                _im.load()
-                self.im = _im.im
-        finally:
-            try:
-                os.unlink(outfile)
-            except OSError:
-                pass
-
-
-Image.register_open(IptcImageFile.format, IptcImageFile)
-
-Image.register_extension(IptcImageFile.format, ".iim")
-
-
-def getiptcinfo(im):
-    """
-    Get IPTC information from TIFF, JPEG, or IPTC file.
-
-    :param im: An image containing IPTC data.
-    :returns: A dictionary containing IPTC information, or None if
-        no IPTC information block was found.
-    """
-    import io
-
-    from . import JpegImagePlugin, TiffImagePlugin
-
-    data = None
-
-    if isinstance(im, IptcImageFile):
-        # return info dictionary right away
-        return im.info
-
-    elif isinstance(im, JpegImagePlugin.JpegImageFile):
-        # extract the IPTC/NAA resource
-        photoshop = im.info.get("photoshop")
-        if photoshop:
-            data = photoshop.get(0x0404)
-
-    elif isinstance(im, TiffImagePlugin.TiffImageFile):
-        # get raw data from the IPTC/NAA tag (PhotoShop tags the data
-        # as 4-byte integers, so we cannot use the get method...)
-        try:
-            data = im.tag.tagdata[TiffImagePlugin.IPTC_NAA_CHUNK]
-        except (AttributeError, KeyError):
-            pass
-
-    if data is None:
-        return None  # no properties
-
-    # create an IptcImagePlugin object without initializing it
-    class FakeImage:
-        pass
-
-    im = FakeImage()
-    im.__class__ = IptcImageFile
-
-    # parse the IPTC information chunk
-    im.info = {}
-    im.fp = io.BytesIO(data)
-
-    try:
-        im._open()
-    except (IndexError, KeyError):
-        pass  # expected failure
-
-    return im.info
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/abc/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/abc/__init__.py
deleted file mode 100644
index 72c34e544e1634e4f42c005506bac9b61ab095f5..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/abc/__init__.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from __future__ import annotations
-
-__all__ = (
-    "AsyncResource",
-    "IPAddressType",
-    "IPSockAddrType",
-    "SocketAttribute",
-    "SocketStream",
-    "SocketListener",
-    "UDPSocket",
-    "UNIXSocketStream",
-    "UDPPacketType",
-    "ConnectedUDPSocket",
-    "UnreliableObjectReceiveStream",
-    "UnreliableObjectSendStream",
-    "UnreliableObjectStream",
-    "ObjectReceiveStream",
-    "ObjectSendStream",
-    "ObjectStream",
-    "ByteReceiveStream",
-    "ByteSendStream",
-    "ByteStream",
-    "AnyUnreliableByteReceiveStream",
-    "AnyUnreliableByteSendStream",
-    "AnyUnreliableByteStream",
-    "AnyByteReceiveStream",
-    "AnyByteSendStream",
-    "AnyByteStream",
-    "Listener",
-    "Process",
-    "Event",
-    "Condition",
-    "Lock",
-    "Semaphore",
-    "CapacityLimiter",
-    "CancelScope",
-    "TaskGroup",
-    "TaskStatus",
-    "TestRunner",
-    "BlockingPortal",
-)
-
-from typing import Any
-
-from ._resources import AsyncResource
-from ._sockets import (
-    ConnectedUDPSocket,
-    IPAddressType,
-    IPSockAddrType,
-    SocketAttribute,
-    SocketListener,
-    SocketStream,
-    UDPPacketType,
-    UDPSocket,
-    UNIXSocketStream,
-)
-from ._streams import (
-    AnyByteReceiveStream,
-    AnyByteSendStream,
-    AnyByteStream,
-    AnyUnreliableByteReceiveStream,
-    AnyUnreliableByteSendStream,
-    AnyUnreliableByteStream,
-    ByteReceiveStream,
-    ByteSendStream,
-    ByteStream,
-    Listener,
-    ObjectReceiveStream,
-    ObjectSendStream,
-    ObjectStream,
-    UnreliableObjectReceiveStream,
-    UnreliableObjectSendStream,
-    UnreliableObjectStream,
-)
-from ._subprocesses import Process
-from ._tasks import TaskGroup, TaskStatus
-from ._testing import TestRunner
-
-# Re-exported here, for backwards compatibility
-# isort: off
-from .._core._synchronization import CapacityLimiter, Condition, Event, Lock, Semaphore
-from .._core._tasks import CancelScope
-from ..from_thread import BlockingPortal
-
-# Re-export imports so they look like they live directly in this package
-key: str
-value: Any
-for key, value in list(locals().items()):
-    if getattr(value, "__module__", "").startswith("anyio.abc."):
-        value.__module__ = __name__
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/exceptions.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/exceptions.py
deleted file mode 100644
index ca097b1cef5f8dc697f66ee18df6c3ca6e5756de..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/exceptions.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from typing import Any, Dict, Optional, Sequence, Type
-
-from pydantic import BaseModel, ValidationError, create_model
-from pydantic.error_wrappers import ErrorList
-from starlette.exceptions import HTTPException as StarletteHTTPException
-from starlette.exceptions import WebSocketException as WebSocketException  # noqa: F401
-
-
-class HTTPException(StarletteHTTPException):
-    def __init__(
-        self,
-        status_code: int,
-        detail: Any = None,
-        headers: Optional[Dict[str, Any]] = None,
-    ) -> None:
-        super().__init__(status_code=status_code, detail=detail, headers=headers)
-
-
-RequestErrorModel: Type[BaseModel] = create_model("Request")
-WebSocketErrorModel: Type[BaseModel] = create_model("WebSocket")
-
-
-class FastAPIError(RuntimeError):
-    """
-    A generic, FastAPI-specific error.
-    """
-
-
-class RequestValidationError(ValidationError):
-    def __init__(self, errors: Sequence[ErrorList], *, body: Any = None) -> None:
-        self.body = body
-        super().__init__(errors, RequestErrorModel)
-
-
-class WebSocketRequestValidationError(ValidationError):
-    def __init__(self, errors: Sequence[ErrorList]) -> None:
-        super().__init__(errors, WebSocketErrorModel)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/testclient.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/testclient.py
deleted file mode 100644
index 4012406aa76f743c5c5d1ab8ff56d6d67cfb6653..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/testclient.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.testclient import TestClient as TestClient  # noqa
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_path.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_path.py
deleted file mode 100644
index 7cb200030611b6737de4001ce5693ef55a023004..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_path.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import io
-import unittest
-
-import importlib_resources as resources
-from . import data01
-from . import util
-
-
-class CommonTests(util.CommonTests, unittest.TestCase):
-    def execute(self, package, path):
-        with resources.as_file(resources.files(package).joinpath(path)):
-            pass
-
-
-class PathTests:
-    def test_reading(self):
-        """
-        Path should be readable.
-
-        Test also implicitly verifies the returned object is a pathlib.Path
-        instance.
-        """
-        target = resources.files(self.data) / 'utf-8.file'
-        with resources.as_file(target) as path:
-            self.assertTrue(path.name.endswith("utf-8.file"), repr(path))
-            # pathlib.Path.read_text() was introduced in Python 3.5.
-            with path.open('r', encoding='utf-8') as file:
-                text = file.read()
-            self.assertEqual('Hello, UTF-8 world!\n', text)
-
-
-class PathDiskTests(PathTests, unittest.TestCase):
-    data = data01
-
-    def test_natural_path(self):
-        """
-        Guarantee the internal implementation detail that
-        file-system-backed resources do not get the tempdir
-        treatment.
-        """
-        target = resources.files(self.data) / 'utf-8.file'
-        with resources.as_file(target) as path:
-            assert 'data' in str(path)
-
-
-class PathMemoryTests(PathTests, unittest.TestCase):
-    def setUp(self):
-        file = io.BytesIO(b'Hello, UTF-8 world!\n')
-        self.addCleanup(file.close)
-        self.data = util.create_package(
-            file=file, path=FileNotFoundError("package exists only in memory")
-        )
-        self.data.__spec__.origin = None
-        self.data.__spec__.has_location = False
-
-
-class PathZipTests(PathTests, util.ZipSetup, unittest.TestCase):
-    def test_remove_in_context_manager(self):
-        """
-        It is not an error if the file that was temporarily stashed on the
-        file system is removed inside the `with` stanza.
-        """
-        target = resources.files(self.data) / 'utf-8.file'
-        with resources.as_file(target) as path:
-            path.unlink()
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/_fontconfig_pattern.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/_fontconfig_pattern.py
deleted file mode 100644
index f0ed155c2d62fe6f6bb8f3a4b7ae63056790dbed..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/_fontconfig_pattern.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""
-A module for parsing and generating `fontconfig patterns`_.
-
-.. _fontconfig patterns:
-   https://www.freedesktop.org/software/fontconfig/fontconfig-user.html
-"""
-
-# This class logically belongs in `matplotlib.font_manager`, but placing it
-# there would have created cyclical dependency problems, because it also needs
-# to be available from `matplotlib.rcsetup` (for parsing matplotlibrc files).
-
-from functools import lru_cache, partial
-import re
-
-from pyparsing import (
-    Group, Optional, ParseException, Regex, StringEnd, Suppress, ZeroOrMore)
-
-from matplotlib import _api
-
-
-_family_punc = r'\\\-:,'
-_family_unescape = partial(re.compile(r'\\(?=[%s])' % _family_punc).sub, '')
-_family_escape = partial(re.compile(r'(?=[%s])' % _family_punc).sub, r'\\')
-_value_punc = r'\\=_:,'
-_value_unescape = partial(re.compile(r'\\(?=[%s])' % _value_punc).sub, '')
-_value_escape = partial(re.compile(r'(?=[%s])' % _value_punc).sub, r'\\')
-
-
-_CONSTANTS = {
-    'thin':           ('weight', 'light'),
-    'extralight':     ('weight', 'light'),
-    'ultralight':     ('weight', 'light'),
-    'light':          ('weight', 'light'),
-    'book':           ('weight', 'book'),
-    'regular':        ('weight', 'regular'),
-    'normal':         ('weight', 'normal'),
-    'medium':         ('weight', 'medium'),
-    'demibold':       ('weight', 'demibold'),
-    'semibold':       ('weight', 'semibold'),
-    'bold':           ('weight', 'bold'),
-    'extrabold':      ('weight', 'extra bold'),
-    'black':          ('weight', 'black'),
-    'heavy':          ('weight', 'heavy'),
-    'roman':          ('slant', 'normal'),
-    'italic':         ('slant', 'italic'),
-    'oblique':        ('slant', 'oblique'),
-    'ultracondensed': ('width', 'ultra-condensed'),
-    'extracondensed': ('width', 'extra-condensed'),
-    'condensed':      ('width', 'condensed'),
-    'semicondensed':  ('width', 'semi-condensed'),
-    'expanded':       ('width', 'expanded'),
-    'extraexpanded':  ('width', 'extra-expanded'),
-    'ultraexpanded':  ('width', 'ultra-expanded'),
-}
-
-
-@lru_cache  # The parser instance is a singleton.
-def _make_fontconfig_parser():
-    def comma_separated(elem):
-        return elem + ZeroOrMore(Suppress(",") + elem)
-
-    family = Regex(r"([^%s]|(\\[%s]))*" % (_family_punc, _family_punc))
-    size = Regex(r"([0-9]+\.?[0-9]*|\.[0-9]+)")
-    name = Regex(r"[a-z]+")
-    value = Regex(r"([^%s]|(\\[%s]))*" % (_value_punc, _value_punc))
-    # replace trailing `| name` by oneOf(_CONSTANTS) in mpl 3.9.
-    prop = Group((name + Suppress("=") + comma_separated(value)) | name)
-    return (
-        Optional(comma_separated(family)("families"))
-        + Optional("-" + comma_separated(size)("sizes"))
-        + ZeroOrMore(":" + prop("properties*"))
-        + StringEnd()
-    )
-
-
-# `parse_fontconfig_pattern` is a bottleneck during the tests because it is
-# repeatedly called when the rcParams are reset (to validate the default
-# fonts).  In practice, the cache size doesn't grow beyond a few dozen entries
-# during the test suite.
-@lru_cache
-def parse_fontconfig_pattern(pattern):
-    """
-    Parse a fontconfig *pattern* into a dict that can initialize a
-    `.font_manager.FontProperties` object.
-    """
-    parser = _make_fontconfig_parser()
-    try:
-        parse = parser.parseString(pattern)
-    except ParseException as err:
-        # explain becomes a plain method on pyparsing 3 (err.explain(0)).
-        raise ValueError("\n" + ParseException.explain(err, 0)) from None
-    parser.resetCache()
-    props = {}
-    if "families" in parse:
-        props["family"] = [*map(_family_unescape, parse["families"])]
-    if "sizes" in parse:
-        props["size"] = [*parse["sizes"]]
-    for prop in parse.get("properties", []):
-        if len(prop) == 1:
-            if prop[0] not in _CONSTANTS:
-                _api.warn_deprecated(
-                    "3.7", message=f"Support for unknown constants "
-                    f"({prop[0]!r}) is deprecated since %(since)s and "
-                    f"will be removed %(removal)s.")
-                continue
-            prop = _CONSTANTS[prop[0]]
-        k, *v = prop
-        props.setdefault(k, []).extend(map(_value_unescape, v))
-    return props
-
-
-def generate_fontconfig_pattern(d):
-    """Convert a `.FontProperties` to a fontconfig pattern string."""
-    kvs = [(k, getattr(d, f"get_{k}")())
-           for k in ["style", "variant", "weight", "stretch", "file", "size"]]
-    # Families is given first without a leading keyword.  Other entries (which
-    # are necessarily scalar) are given as key=value, skipping Nones.
-    return (",".join(_family_escape(f) for f in d.get_family())
-            + "".join(f":{k}={_value_escape(str(v))}"
-                      for k, v in kvs if v is not None))
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/_text_helpers.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/_text_helpers.py
deleted file mode 100644
index 18bfb550c90bd86fc6c79dbdd7bb0602dc040993..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/_text_helpers.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""
-Low-level text helper utilities.
-"""
-
-import dataclasses
-
-from . import _api
-from .ft2font import KERNING_DEFAULT, LOAD_NO_HINTING
-
-
-LayoutItem = dataclasses.make_dataclass(
-    "LayoutItem", ["ft_object", "char", "glyph_idx", "x", "prev_kern"])
-
-
-def warn_on_missing_glyph(codepoint):
-    _api.warn_external(
-        "Glyph {} ({}) missing from current font.".format(
-            codepoint,
-            chr(codepoint).encode("ascii", "namereplace").decode("ascii")))
-    block = ("Hebrew" if 0x0590 <= codepoint <= 0x05ff else
-             "Arabic" if 0x0600 <= codepoint <= 0x06ff else
-             "Devanagari" if 0x0900 <= codepoint <= 0x097f else
-             "Bengali" if 0x0980 <= codepoint <= 0x09ff else
-             "Gurmukhi" if 0x0a00 <= codepoint <= 0x0a7f else
-             "Gujarati" if 0x0a80 <= codepoint <= 0x0aff else
-             "Oriya" if 0x0b00 <= codepoint <= 0x0b7f else
-             "Tamil" if 0x0b80 <= codepoint <= 0x0bff else
-             "Telugu" if 0x0c00 <= codepoint <= 0x0c7f else
-             "Kannada" if 0x0c80 <= codepoint <= 0x0cff else
-             "Malayalam" if 0x0d00 <= codepoint <= 0x0d7f else
-             "Sinhala" if 0x0d80 <= codepoint <= 0x0dff else
-             None)
-    if block:
-        _api.warn_external(
-            f"Matplotlib currently does not support {block} natively.")
-
-
-def layout(string, font, *, kern_mode=KERNING_DEFAULT):
-    """
-    Render *string* with *font*.  For each character in *string*, yield a
-    (glyph-index, x-position) pair.  When such a pair is yielded, the font's
-    glyph is set to the corresponding character.
-
-    Parameters
-    ----------
-    string : str
-        The string to be rendered.
-    font : FT2Font
-        The font.
-    kern_mode : int
-        A FreeType kerning mode.
-
-    Yields
-    ------
-    glyph_index : int
-    x_position : float
-    """
-    x = 0
-    prev_glyph_idx = None
-    char_to_font = font._get_fontmap(string)
-    base_font = font
-    for char in string:
-        # This has done the fallback logic
-        font = char_to_font.get(char, base_font)
-        glyph_idx = font.get_char_index(ord(char))
-        kern = (
-            base_font.get_kerning(prev_glyph_idx, glyph_idx, kern_mode) / 64
-            if prev_glyph_idx is not None else 0.
-        )
-        x += kern
-        glyph = font.load_glyph(glyph_idx, flags=LOAD_NO_HINTING)
-        yield LayoutItem(font, char, glyph_idx, x, kern)
-        x += glyph.linearHoriAdvance / 65536
-        prev_glyph_idx = glyph_idx
diff --git a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/data/data_sampler.py b/spaces/leafShen/CodeFormer/CodeFormer/basicsr/data/data_sampler.py
deleted file mode 100644
index 575452d9f844a928f7f42296c81635cfbadec7c2..0000000000000000000000000000000000000000
--- a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/data/data_sampler.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import math
-import torch
-from torch.utils.data.sampler import Sampler
-
-
-class EnlargedSampler(Sampler):
-    """Sampler that restricts data loading to a subset of the dataset.
-
-    Modified from torch.utils.data.distributed.DistributedSampler
-    Support enlarging the dataset for iteration-based training, for saving
-    time when restart the dataloader after each epoch
-
-    Args:
-        dataset (torch.utils.data.Dataset): Dataset used for sampling.
-        num_replicas (int | None): Number of processes participating in
-            the training. It is usually the world_size.
-        rank (int | None): Rank of the current process within num_replicas.
-        ratio (int): Enlarging ratio. Default: 1.
-    """
-
-    def __init__(self, dataset, num_replicas, rank, ratio=1):
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.epoch = 0
-        self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas)
-        self.total_size = self.num_samples * self.num_replicas
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-        indices = torch.randperm(self.total_size, generator=g).tolist()
-
-        dataset_size = len(self.dataset)
-        indices = [v % dataset_size for v in indices]
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
-
-    def __len__(self):
-        return self.num_samples
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch
diff --git a/spaces/leogabraneth/text-generation-webui-main/README.md b/spaces/leogabraneth/text-generation-webui-main/README.md
deleted file mode 100644
index ca41467035f381031d7be539de42817e05646550..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/README.md
+++ /dev/null
@@ -1,442 +0,0 @@
----
-title: text-generation-webui-main
-app_file: server.py
-sdk: gradio
-sdk_version: 3.50.2
----
-# Text generation web UI
-
-A Gradio web UI for Large Language Models.
-
-Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
-
-|![Image1](https://github.com/oobabooga/screenshots/raw/main/print_instruct.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/print_chat.png) |
-|:---:|:---:|
-|![Image1](https://github.com/oobabooga/screenshots/raw/main/print_default.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/print_parameters.png) |
-
-## Features
-
-* 3 interface modes: default (two columns), notebook, and chat
-* Multiple model backends: [transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)
-* Dropdown menu for quickly switching between different models
-* LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA
-* Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others
-* 4-bit, 8-bit, and CPU inference through the transformers library
-* Use llama.cpp models with transformers samplers (`llamacpp_HF` loader)
-* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
-* [Extensions framework](https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions)
-* [Custom chat characters](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab#character)
-* Very efficient text streaming
-* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai)
-* API, including endpoints for websocket streaming ([see the examples](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples))
-
-## Documentation
-
-To learn how to use the various features, check out the Documentation: 
-
-https://github.com/oobabooga/text-generation-webui/wiki
-
-## Installation
-
-### One-click installers
-
-1) Clone or [download](https://github.com/oobabooga/text-generation-webui/archive/refs/heads/main.zip) the repository.
-2) Run the `start_linux.sh`, `start_windows.bat`, `start_macos.sh`, or `start_wsl.bat` script depending on your OS.
-3) Select your GPU vendor when asked.
-4) Have fun!
-
-#### How it works
-
-The script creates a folder called `installer_files` where it sets up a Conda environment using Miniconda. The installation is self-contained: if you want to reinstall, just delete `installer_files` and run the start script again.
-
-To launch the webui in the future after it is already installed, run the same `start` script.
-
-#### Getting updates
-
-Run `update_linux.sh`, `update_windows.bat`, `update_macos.sh`, or `update_wsl.bat`.
-
-#### Running commands
-
-If you ever need to install something manually in the `installer_files` environment, you can launch an interactive shell using the cmd script: `cmd_linux.sh`, `cmd_windows.bat`, `cmd_macos.sh`, or `cmd_wsl.bat`.
-
-#### Defining command-line flags
-
-To define persistent command-line flags like `--listen` or `--api`, edit the `CMD_FLAGS.txt` file with a text editor and add them there. Flags can also be provided directly to the start scripts, for instance, `./start-linux.sh --listen`.
-
-#### Other info
-
-* There is no need to run any of those scripts as admin/root.
-* For additional instructions about AMD setup, WSL setup, and nvcc installation, consult [the documentation](https://github.com/oobabooga/text-generation-webui/wiki).
-* The installer has been tested mostly on NVIDIA GPUs. If you can find a way to improve it for your AMD/Intel Arc/Mac Metal GPU, you are highly encouraged to submit a PR to this repository. The main file to be edited is `one_click.py`.
-* For automated installation, you can use the `GPU_CHOICE`, `USE_CUDA118`, `LAUNCH_AFTER_INSTALL`, and `INSTALL_EXTENSIONS` environment variables. For instance: `GPU_CHOICE=A USE_CUDA118=FALSE LAUNCH_AFTER_INSTALL=FALSE INSTALL_EXTENSIONS=FALSE ./start_linux.sh`.
-
-### Manual installation using Conda
-
-Recommended if you have some experience with the command-line.
-
-#### 0. Install Conda
-
-https://docs.conda.io/en/latest/miniconda.html
-
-On Linux or WSL, it can be automatically installed with these two commands ([source](https://educe-ubc.github.io/conda.html)):
-
-```
-curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
-bash Miniconda3.sh
-```
-
-#### 1. Create a new conda environment
-
-```
-conda create -n textgen python=3.11
-conda activate textgen
-```
-
-#### 2. Install Pytorch
-
-| System | GPU | Command |
-|--------|---------|---------|
-| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
-| Linux/WSL | CPU only | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu` |
-| Linux | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6` |
-| MacOS + MPS | Any | `pip3 install torch torchvision torchaudio` |
-| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
-| Windows | CPU only | `pip3 install torch torchvision torchaudio` |
-
-The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.
-
-For NVIDIA, you may also need to manually install the CUDA runtime libraries:
-
-```
-conda install -y -c "nvidia/label/cuda-12.1.0" cuda-runtime
-```
-
-#### 3. Install the web UI
-
-```
-git clone https://github.com/oobabooga/text-generation-webui
-cd text-generation-webui
-pip install -r <requirements file according to table below>
-```
-
-Requirements file to use:
-
-| GPU | CPU | requirements file to use |
-|--------|---------|---------|
-| NVIDIA | has AVX2 | `requirements.txt` |
-| NVIDIA | no AVX2 | `requirements_noavx2.txt` |
-| AMD | has AVX2 | `requirements_amd.txt` |
-| AMD | no AVX2 | `requirements_amd_noavx2.txt` |
-| CPU only | has AVX2 | `requirements_cpu_only.txt` |
-| CPU only | no AVX2 | `requirements_cpu_only_noavx2.txt` |
-| Apple | Intel | `requirements_apple_intel.txt` |
-| Apple | Apple Silicon | `requirements_apple_silicon.txt` |
-
-##### AMD GPU on Windows
-
-1) Use `requirements_cpu_only.txt` or `requirements_cpu_only_noavx2.txt` in the command above.
-
-2) Manually install llama-cpp-python using the appropriate command for your hardware: [Installation from PyPI](https://github.com/abetlen/llama-cpp-python#installation-with-hardware-acceleration).
-    * Use the `LLAMA_HIPBLAS=on` toggle.
-    * Note the [Windows remarks](https://github.com/abetlen/llama-cpp-python#windows-remarks).
-
-3) Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#install-from-source).
-    * Perform the from-source installation - there are no prebuilt ROCm packages for Windows.
-
-4) Manually install [ExLlama](https://github.com/turboderp/exllama) by simply cloning it into the `repositories` folder (it will be automatically compiled at runtime after that):
-
-```sh
-cd text-generation-webui
-git clone https://github.com/turboderp/exllama repositories/exllama
-```
-
-##### Older NVIDIA GPUs
-
-1) For Kepler GPUs and older, you will need to install CUDA 11.8 instead of 12:
-
-```
-pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
-conda install -y -c "nvidia/label/cuda-11.8.0" cuda-runtime
-```
-
-2) bitsandbytes >= 0.39 may not work. In that case, to use `--load-in-8bit`, you may have to downgrade like this:
-    * Linux: `pip install bitsandbytes==0.38.1`
-    * Windows: `pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl`
-
-##### Manual install
-
-The requirments*.txt above contain various precompiled wheels. If you wish to compile things manually, or if you need to because no suitable wheels are available for your hardware, you can use `requirements_nowheels.txt` and then install your desired loaders manually.
-
-### Alternative: Docker
-
-```
-ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
-cp docker/.env.example .env
-# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
-docker compose up --build
-```
-
-* You need to have docker compose v2.17 or higher installed. See [this guide](https://github.com/oobabooga/text-generation-webui/wiki/09-%E2%80%90-Docker) for instructions.
-* For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker).
-
-### Updating the requirements
-
-From time to time, the `requirements*.txt` changes. To update, use these commands:
-
-```
-conda activate textgen
-cd text-generation-webui
-pip install -r <requirements file that you've used> --upgrade
-```
-
-## Downloading models
-
-Models should be placed in the `text-generation-webui/models` folder. They are usually downloaded from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads).
-
-* Transformers or GPTQ models are made of several files and must be placed in a subfolder. Example:
-
-```
-text-generation-webui
-├── models
-│   ├── lmsys_vicuna-33b-v1.3
-│   │   ├── config.json
-│   │   ├── generation_config.json
-│   │   ├── pytorch_model-00001-of-00007.bin
-│   │   ├── pytorch_model-00002-of-00007.bin
-│   │   ├── pytorch_model-00003-of-00007.bin
-│   │   ├── pytorch_model-00004-of-00007.bin
-│   │   ├── pytorch_model-00005-of-00007.bin
-│   │   ├── pytorch_model-00006-of-00007.bin
-│   │   ├── pytorch_model-00007-of-00007.bin
-│   │   ├── pytorch_model.bin.index.json
-│   │   ├── special_tokens_map.json
-│   │   ├── tokenizer_config.json
-│   │   └── tokenizer.model
-```
-
-* GGUF models are a single file and should be placed directly into `models`. Example:
-
-```
-text-generation-webui
-├── models
-│   ├── llama-2-13b-chat.Q4_K_M.gguf
-```
-
-In both cases, you can use the "Model" tab of the UI to download the model from Hugging Face automatically. It is also possible to download via the command-line with `python download-model.py organization/model` (use `--help` to see all the options).
-
-#### GPT-4chan
-
-<details>
-<summary>
-Instructions
-</summary>
-
-[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
-
-* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
-* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
-
-The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version.
-
-After downloading the model, follow these steps:
-
-1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`.
-2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json).
-3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
-
-```
-python download-model.py EleutherAI/gpt-j-6B --text-only
-```
-
-When you load this model in default or notebook modes, the "HTML" tab will show the generated text in 4chan format:
-
-![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png)
-
-</details>
-
-## Starting the web UI
-
-    conda activate textgen
-    cd text-generation-webui
-    python server.py
-
-Then browse to
-
-`http://localhost:7860/?__theme=dark`
-
-Optionally, you can use the following command-line flags:
-
-#### Basic settings
-
-| Flag                                       | Description |
-|--------------------------------------------|-------------|
-| `-h`, `--help`                             | show this help message and exit |
-| `--multi-user`                             | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is likely not safe for sharing publicly. |
-| `--character CHARACTER`                    | The name of the character to load in chat mode by default. |
-| `--model MODEL`                            | Name of the model to load by default. |
-| `--lora LORA [LORA ...]`                   | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
-| `--model-dir MODEL_DIR`                    | Path to directory with all the models. |
-| `--lora-dir LORA_DIR`                      | Path to directory with all the loras. |
-| `--model-menu`                             | Show a model menu in the terminal when the web UI is first launched. |
-| `--settings SETTINGS_FILE`                 | Load the default interface settings from this yaml file. See `settings-template.yaml` for an example. If you create a file called `settings.yaml`, this file will be loaded by default without the need to use the `--settings` flag. |
-| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
-| `--verbose`                                | Print the prompts to the terminal. |
-| `--chat-buttons`                           | Show buttons on the chat tab instead of a hover menu. |
-
-#### Model loader
-
-| Flag                                       | Description |
-|--------------------------------------------|-------------|
-| `--loader LOADER`                          | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, exllama_hf, exllamav2_hf, exllama, exllamav2, autogptq, gptq-for-llama, llama.cpp, llamacpp_hf, ctransformers, autoawq. |
-
-#### Accelerate/transformers
-
-| Flag                                        | Description |
-|---------------------------------------------|-------------|
-| `--cpu`                                     | Use the CPU to generate text. Warning: Training on CPU is extremely slow. |
-| `--auto-devices`                            | Automatically split the model across the available GPU(s) and CPU. |
-|  `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB. |
-| `--cpu-memory CPU_MEMORY`                   | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above. |
-| `--disk`                                    | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
-| `--disk-cache-dir DISK_CACHE_DIR`           | Directory to save the disk cache to. Defaults to "cache". |
-| `--load-in-8bit`                            | Load the model with 8-bit precision (using bitsandbytes). |
-| `--bf16`                                    | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
-| `--no-cache`                                | Set `use_cache` to `False` while generating text. This reduces VRAM usage slightly, but it comes at a performance cost. |
-| `--xformers`                                | Use xformer's memory efficient attention. This is really old and probably doesn't do anything. |
-| `--sdp-attention`                           | Use PyTorch 2.0's SDP attention. Same as above. |
-| `--trust-remote-code`                       | Set `trust_remote_code=True` while loading the model. Necessary for some models. |
-| `--use_fast`                                | Set `use_fast=True` while loading the tokenizer. |
-
-#### Accelerate 4-bit
-
-⚠️  Requires minimum compute of 7.0 on Windows at the moment.
-
-| Flag                                        | Description |
-|---------------------------------------------|-------------|
-| `--load-in-4bit`                            | Load the model with 4-bit precision (using bitsandbytes). |
-| `--use_double_quant`                        | use_double_quant for 4-bit. |
-| `--compute_dtype COMPUTE_DTYPE`             | compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
-| `--quant_type QUANT_TYPE`                   | quant_type for 4-bit. Valid options: nf4, fp4. |
-
-#### llama.cpp
-
-| Flag        | Description |
-|-------------|-------------|
-| `--n_ctx N_CTX` | Size of the prompt context. |
-| `--threads` | Number of threads to use. |
-| `--threads-batch THREADS_BATCH` | Number of threads to use for batches/prompt processing. |
-| `--no_mul_mat_q` | Disable the mulmat kernels. |
-| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
-| `--no-mmap`   | Prevent mmap from being used. |
-| `--mlock`     | Force the system to keep the model in RAM. |
-| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. |
-| `--tensor_split TENSOR_SPLIT`       | Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17. |
-| `--llama_cpp_seed SEED`             | Seed for llama-cpp models. Default is 0 (random). |
-| `--numa`      | Activate NUMA task allocation for llama.cpp. |
-| `--cache-capacity CACHE_CAPACITY`   | Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
-
-#### ExLlama
-
-| Flag             | Description |
-|------------------|-------------|
-|`--gpu-split`     | Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7. |
-|`--max_seq_len MAX_SEQ_LEN`           | Maximum sequence length. |
-|`--cfg-cache`                         | ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. |
-
-#### AutoGPTQ
-
-| Flag             | Description |
-|------------------|-------------|
-| `--triton`                     | Use triton. |
-| `--no_inject_fused_attention`  | Disable the use of fused attention, which will use less VRAM at the cost of slower inference. |
-| `--no_inject_fused_mlp`        | Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference. |
-| `--no_use_cuda_fp16`           | This can make models faster on some systems. |
-| `--desc_act`                   | For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig. |
-| `--disable_exllama`            | Disable ExLlama kernel, which can improve inference speed on some systems. |
-
-#### GPTQ-for-LLaMa
-
-| Flag                      | Description |
-|---------------------------|-------------|
-| `--wbits WBITS`           | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
-| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
-| `--groupsize GROUPSIZE`   | Group size. |
-| `--pre_layer PRE_LAYER [PRE_LAYER ...]`  | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg `--pre_layer 30 60`. |
-| `--checkpoint CHECKPOINT` | The path to the quantized checkpoint file. If not specified, it will be automatically detected. |
-| `--monkey-patch`          | Apply the monkey patch for using LoRAs with quantized models. |
-
-#### ctransformers
-
-| Flag        | Description |
-|-------------|-------------|
-| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported. |
-
-#### DeepSpeed
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--deepspeed`                         | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
-| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
-| `--local_rank LOCAL_RANK`             | DeepSpeed: Optional argument for distributed setups. |
-
-#### RWKV
-
-| Flag                            | Description |
-|---------------------------------|-------------|
-| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
-| `--rwkv-cuda-on`                | RWKV: Compile the CUDA kernel for better performance. |
-
-#### RoPE (for llama.cpp, ExLlama, ExLlamaV2, and transformers)
-
-| Flag             | Description |
-|------------------|-------------|
-| `--alpha_value ALPHA_VALUE`           | Positional embeddings alpha factor for NTK RoPE scaling. Use either this or `compress_pos_emb`, not both. |
-| `--rope_freq_base ROPE_FREQ_BASE`     | If greater than 0, will be used instead of alpha_value. Those two are related by `rope_freq_base = 10000 * alpha_value ^ (64 / 63)`. |
-| `--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should be set to `(context length) / (model's original context length)`. Equal to `1/rope_freq_scale`. |
-
-#### Gradio
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--listen`                            | Make the web UI reachable from your local network. |
-| `--listen-port LISTEN_PORT`           | The listening port that the server will use. |
-| `--listen-host LISTEN_HOST`           | The hostname that the server will use. |
-| `--share`                             | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
-| `--auto-launch`                       | Open the web UI in the default browser upon launch. |
-| `--gradio-auth USER:PWD`              | Set Gradio authentication password in the format "username:password". Multiple credentials can also be supplied with "u1:p1,u2:p2,u3:p3". |
-| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the Gradio authentication file path. The file should contain one or more user:password pairs in the same format as above. |
-| `--ssl-keyfile SSL_KEYFILE`           | The path to the SSL certificate key file. |
-| `--ssl-certfile SSL_CERTFILE`         | The path to the SSL certificate cert file. |
-
-#### API
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--api`                               | Enable the API extension. |
-| `--public-api`                        | Create a public URL for the API using Cloudfare. |
-| `--public-api-id PUBLIC_API_ID`       | Tunnel ID for named Cloudflare Tunnel. Use together with public-api option. |
-| `--api-blocking-port BLOCKING_PORT`   | The listening port for the blocking API. |
-| `--api-streaming-port STREAMING_PORT` | The listening port for the streaming API. |
-
-#### Multimodal
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--multimodal-pipeline PIPELINE`      | The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`. |
-
-## Google Colab notebook
-
-https://colab.research.google.com/github/oobabooga/text-generation-webui/blob/main/Colab-TextGen-GPU.ipynb
-
-## Contributing
-
-If you would like to contribute to the project, check out the [Contributing guidelines](https://github.com/oobabooga/text-generation-webui/wiki/Contributing-guidelines).
-
-## Community
-
-* Subreddit: https://www.reddit.com/r/oobabooga/
-* Discord: https://discord.gg/jwZCF2dPQN
-
-## Acknowledgment
-
-In August 2023, [Andreessen Horowitz](https://a16z.com/) (a16z) provided a generous grant to encourage and support my independent work on this project. I am **extremely** grateful for their trust and recognition, which will allow me to dedicate more time towards realizing the full potential of text-generation-webui.
diff --git a/spaces/lfoppiano/document-qa/README.md b/spaces/lfoppiano/document-qa/README.md
deleted file mode 100644
index 3c17b103ecc84c87de596e6cdbf3400e17458e73..0000000000000000000000000000000000000000
--- a/spaces/lfoppiano/document-qa/README.md
+++ /dev/null
@@ -1,86 +0,0 @@
----
-title: Scientific Document Insights Q/A
-emoji: 📝
-colorFrom: yellow
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: streamlit_app.py
-pinned: false
-license: apache-2.0
----
-
-# DocumentIQA: Scientific Document Insights Q/A
-
-**Work in progress** :construction_worker: 
-
-## Introduction
-
-Question/Answering on scientific documents using LLMs (OpenAI, Mistral, ~~LLama2,~~ etc..).
-This application is the frontend for testing the RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS.
-Differently to most of the project, we focus on scientific articles. We target only the full-text using [Grobid](https://github.com/kermitt2/grobid) that provide and cleaner results than the raw PDF2Text converter (which is comparable with most of other solutions).
-
-**NER in LLM response**: The responses from the LLMs are post-processed to extract <span stype="color:yellow">physical quantities, measurements</span> (with [grobid-quantities](https://github.com/kermitt2/grobid-quantities)) and <span stype="color:blue">materials</span> mentions (with [grobid-superconductors](https://github.com/lfoppiano/grobid-superconductors)).
-
-**Demos**: 
- - (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/
- - (on the Streamlit cloud): https://document-insights.streamlit.app/
-
-## Getting started
-
-- Select the model+embedding combination you want ot use 
-- Enter your API Key ([Open AI](https://platform.openai.com/account/api-keys) or [Huggingface](https://huggingface.co/docs/hub/security-tokens)). 
-- Upload a scientific article as PDF document. You will see a spinner or loading indicator while the processing is in progress. 
-- Once the spinner stops, you can proceed to ask your questions
-
- ![screenshot2.png](docs%2Fimages%2Fscreenshot2.png)
-
-## Documentation
-
-### Context size
-Allow to change the number of blocks from the original document that are considered for responding. 
-The default size of each block is 250 tokens (which can be changed before uploading the first document). 
-With default settings, each question uses around 1000 tokens.
-
-**NOTE**: if the chat answers something like "the information is not provided in the given context", **changing the context size might be a solution**~~~~. 
-
-### Chunks size
-When uploaded, each document is split into blocks of a determined size (250 tokens by default). 
-This setting allow users to modify the size of such blocks. 
-Smaller blocks will result in smaller context, yielding more precise sections of the document. 
-Larger blocks will result in larger context less constrained around the question.
-
-### Query mode
-Indicates whether sending a question to the LLM (Language Model) or to the vector storage. 
- - LLM (default) enables question/answering related to the document content.
- - Embeddings: the response will consist of the raw text from the document related to the question (based on the embeddings). This mode helps to test why sometimes the answers are not satisfying or incomplete.
-
-### NER (Named Entities Recognition)
-
-This feature is specifically crafted for people working with scientific documents in materials science. 
-It enables to run NER on the response from the LLM, to identify materials mentions and properties (quantities, masurements).
-This feature leverages both [grobid-quantities](https://github.com/kermitt2/grobid-quanities) and [grobid-superconductors](https://github.com/lfoppiano/grobid-superconductors) external services. 
-
-
-## Development notes
-
-To release a new version: 
-
-- `bump-my-version bump patch` 
-- `git push --tags`
-
-To use docker: 
-
-- docker run `lfoppiano/document-insights-qa:latest`
-
-To install the library with Pypi: 
-
-- `pip install document-qa-engine` 
-
-
-## Acknolwedgement 
-
-This project is developed at the [National Institute for Materials Science](https://www.nims.go.jp) (NIMS) in Japan in collaboration with the [Lambard-ML-Team](https://github.com/Lambard-ML-Team).
-
-
-
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index 3c4917ddca8c44bae8dd02b73d9c61894e4cd805..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Aashiqui 2 Video Songs Hd 1080p Bluray Movie Download.md	
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--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Interchange 4th Edition.pdf.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Interchange 4th Edition.pdf</h2><br /><p><b><b>Download File</b> &#9193; <a href="https://bytlly.com/2uGyv2">https://bytlly.com/2uGyv2</a></b></p><br /><br />
-<br />
-This videos shows all new interchange keys like the 11sr, 21ws, ultra medical storage key ... Follett Library TEXT ID b28882e4 Online PDF Ebook Epub Library verschiedene artikel aus ... Cambridge Interchange 4th Edition English course. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lixq/bingo61/src/components/ui/sheet.tsx b/spaces/lixq/bingo61/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/lixq/bingo61/src/components/ui/sheet.tsx
+++ /dev/null
@@ -1,122 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SheetPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Sheet = SheetPrimitive.Root
-
-const SheetTrigger = SheetPrimitive.Trigger
-
-const SheetClose = SheetPrimitive.Close
-
-const SheetPortal = ({
-  className,
-  children,
-  ...props
-}: SheetPrimitive.DialogPortalProps) => (
-  <SheetPrimitive.Portal
-    className={cn('fixed inset-0 z-50 flex', className)}
-    {...props}
-  >
-    {children}
-  </SheetPrimitive.Portal>
-)
-SheetPortal.displayName = SheetPrimitive.Portal.displayName
-
-const SheetOverlay = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <SheetPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-SheetOverlay.displayName = SheetPrimitive.Overlay.displayName
-
-const SheetContent = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <SheetPortal>
-    <SheetPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed inset-y-0 left-0 z-50 h-full border-r bg-background p-6 shadow-lg transition ease-in-out data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:slide-out-to-left data-[state=open]:slide-in-from-left data-[state=closed]:duration-300 data-[state=open]:duration-500 sm:max-w-sm',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <SheetPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-secondary">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </SheetPrimitive.Close>
-    </SheetPrimitive.Content>
-  </SheetPortal>
-))
-SheetContent.displayName = SheetPrimitive.Content.displayName
-
-const SheetHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div className={cn('flex flex-col space-y-2', className)} {...props} />
-)
-SheetHeader.displayName = 'SheetHeader'
-
-const SheetFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-SheetFooter.displayName = 'SheetFooter'
-
-const SheetTitle = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold text-foreground', className)}
-    {...props}
-  />
-))
-SheetTitle.displayName = SheetPrimitive.Title.displayName
-
-const SheetDescription = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-SheetDescription.displayName = SheetPrimitive.Description.displayName
-
-export {
-  Sheet,
-  SheetTrigger,
-  SheetClose,
-  SheetContent,
-  SheetHeader,
-  SheetFooter,
-  SheetTitle,
-  SheetDescription
-}
diff --git a/spaces/lj1995/trump/inference_nof0.py b/spaces/lj1995/trump/inference_nof0.py
deleted file mode 100644
index 3ebd4cb7749dfa2bfe0150d9ce70519a196fb7d6..0000000000000000000000000000000000000000
--- a/spaces/lj1995/trump/inference_nof0.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import torch,pdb
-import numpy as np
-import soundfile as sf
-from models import SynthesizerTrnNoF0256
-from scipy.io import wavfile
-from fairseq import checkpoint_utils
-import torch.nn.functional as F
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model_path = "https://ibm.box.com/s/z1wgl1stco8ffooyatzdwsqn2psd9lrr"#checkpoint_best_legacy_500.pt
-print("load model(s) from {}".format(model_path))
-models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [model_path],
-    suffix="",
-)
-model = models[0]
-model = model.to(device)
-model = model.half()
-model.eval()
-
-net_g = SynthesizerTrnNoF0256(513,40,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,4,2,2,2],512,[16,16,4,4,4],0)
-weights=torch.load("trump.pt")
-net_g.load_state_dict(weights,strict=True)
-net_g.eval().to(device)
-net_g.half()
-
-
-wav_path="/bili-coeus/liujing04/vits_ch/测试素材/trump/wavs16k/云希_特朗普.wav"
-wav, sr = sf.read(wav_path)
-assert sr == 16000
-feats = torch.from_numpy(wav).float()
-if feats.dim() == 2:  # double channels
-    feats = feats.mean(-1)
-assert feats.dim() == 1, feats.dim()
-feats = feats.view(1, -1)
-padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-inputs = {
-    "source": feats.half().to(device),
-    "padding_mask": padding_mask.to(device),
-    "output_layer": 9,  # layer 9
-}
-with torch.no_grad():
-    logits = model.extract_features(**inputs)
-    feats = model.final_proj(logits[0])
-feats=F.interpolate(feats.permute(0,2,1),scale_factor=2).permute(0,2,1)
-p_len = min(feats.shape[1],10000)#太大了爆显存
-feats = feats[:,:p_len, :]
-p_len = torch.LongTensor([p_len]).to(device)
-with torch.no_grad():
-    audio = net_g.infer(feats, p_len)[0][0, 0].data.cpu().float().numpy()
-
-wavfile.write("trump_co256nof0_63k_test.wav", 32000, audio)
diff --git a/spaces/lmz/candle-whisper/build/m.d.ts b/spaces/lmz/candle-whisper/build/m.d.ts
deleted file mode 100644
index 1004ddea1690a84a504c8c356b16323e110bd0ff..0000000000000000000000000000000000000000
--- a/spaces/lmz/candle-whisper/build/m.d.ts
+++ /dev/null
@@ -1,60 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-/**
-*/
-export class Decoder {
-  free(): void;
-/**
-* @param {Uint8Array} weights
-* @param {Uint8Array} tokenizer
-* @param {Uint8Array} mel_filters
-* @param {Uint8Array} config
-* @param {boolean} quantized
-* @param {boolean} is_multilingual
-* @param {boolean} timestamps
-* @param {string | undefined} task
-* @param {string | undefined} language
-*/
-  constructor(weights: Uint8Array, tokenizer: Uint8Array, mel_filters: Uint8Array, config: Uint8Array, quantized: boolean, is_multilingual: boolean, timestamps: boolean, task?: string, language?: string);
-/**
-* @param {Uint8Array} wav_input
-* @returns {string}
-*/
-  decode(wav_input: Uint8Array): string;
-}
-
-export type InitInput = RequestInfo | URL | Response | BufferSource | WebAssembly.Module;
-
-export interface InitOutput {
-  readonly memory: WebAssembly.Memory;
-  readonly __wbg_decoder_free: (a: number) => void;
-  readonly decoder_new: (a: number, b: number, c: number, d: number, e: number, f: number, g: number, h: number, i: number, j: number, k: number, l: number, m: number, n: number, o: number, p: number) => void;
-  readonly decoder_decode: (a: number, b: number, c: number, d: number) => void;
-  readonly main: (a: number, b: number) => number;
-  readonly __wbindgen_add_to_stack_pointer: (a: number) => number;
-  readonly __wbindgen_malloc: (a: number, b: number) => number;
-  readonly __wbindgen_realloc: (a: number, b: number, c: number, d: number) => number;
-  readonly __wbindgen_free: (a: number, b: number, c: number) => void;
-  readonly __wbindgen_start: () => void;
-}
-
-export type SyncInitInput = BufferSource | WebAssembly.Module;
-/**
-* Instantiates the given `module`, which can either be bytes or
-* a precompiled `WebAssembly.Module`.
-*
-* @param {SyncInitInput} module
-*
-* @returns {InitOutput}
-*/
-export function initSync(module: SyncInitInput): InitOutput;
-
-/**
-* If `module_or_path` is {RequestInfo} or {URL}, makes a request and
-* for everything else, calls `WebAssembly.instantiate` directly.
-*
-* @param {InitInput | Promise<InitInput>} module_or_path
-*
-* @returns {Promise<InitOutput>}
-*/
-export default function __wbg_init (module_or_path?: InitInput | Promise<InitInput>): Promise<InitOutput>;
diff --git a/spaces/ma-xu/LIVE/pydiffvg/device.py b/spaces/ma-xu/LIVE/pydiffvg/device.py
deleted file mode 100644
index 420883d60130a8f21e96bae19ba6025ffd0ed55e..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pydiffvg/device.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import torch
-
-use_gpu = torch.cuda.is_available()
-device = torch.device('cuda') if use_gpu else torch.device('cpu')
-
-def set_use_gpu(v):
-    global use_gpu
-    global device
-    use_gpu = v
-    if not use_gpu:
-        device = torch.device('cpu')
-
-def get_use_gpu():
-    global use_gpu
-    return use_gpu
-
-def set_device(d):
-    global device
-    global use_gpu
-    device = d
-    use_gpu = device.type == 'cuda'
-
-def get_device():
-    global device
-    return device
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/omp/detail/sort.h b/spaces/ma-xu/LIVE/thrust/thrust/system/omp/detail/sort.h
deleted file mode 100644
index 339ce5b6e86398a9b4ccba1c9ae5b8ed79f06958..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/omp/detail/sort.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/omp/detail/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace omp
-{
-namespace detail
-{
-
-template<typename DerivedPolicy,
-         typename RandomAccessIterator,
-         typename StrictWeakOrdering>
-void stable_sort(execution_policy<DerivedPolicy> &exec,
-                 RandomAccessIterator first,
-                 RandomAccessIterator last,
-                 StrictWeakOrdering comp);
-    
-template<typename DerivedPolicy,
-         typename RandomAccessIterator1,
-         typename RandomAccessIterator2,
-         typename StrictWeakOrdering>
-void stable_sort_by_key(execution_policy<DerivedPolicy> &exec,
-                        RandomAccessIterator1 keys_first,
-                        RandomAccessIterator1 keys_last,
-                        RandomAccessIterator2 values_first,
-                        StrictWeakOrdering comp);
-
-} // end namespace detail
-} // end namespace omp
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/omp/detail/sort.inl>
-
diff --git a/spaces/merle/PROTEIN_GENERATOR/model/apply_masks.py b/spaces/merle/PROTEIN_GENERATOR/model/apply_masks.py
deleted file mode 100644
index ecb0fd70f8fd973aa4abb992188cd2335db0cc57..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/model/apply_masks.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import sys, os
-import torch
-from icecream import ic
-import random
-import numpy as np
-from kinematics import get_init_xyz
-sys.path.append('../')
-from utils.calc_dssp import annotate_sse
-
-ic.configureOutput(includeContext=True)
-
-def mask_inputs(seq, 
-        msa_masked, 
-        msa_full, 
-        xyz_t, 
-        t1d, 
-        mask_msa, 
-        input_seq_mask=None, 
-        input_str_mask=None, 
-        input_floating_mask=None, 
-        input_t1dconf_mask=None, 
-        loss_seq_mask=None, 
-        loss_str_mask=None, 
-        loss_str_mask_2d=None, 
-        dssp=False,
-        hotspots=False,
-        diffuser=None, 
-        t=None, 
-        freeze_seq_emb=False, 
-        mutate_seq=False, 
-        no_clamp_seq=False, 
-        norm_input=False,
-        contacts=None,
-        frac_provide_dssp=0.5,
-        dssp_mask_percentage=[0,100],
-        frac_provide_contacts=0.5,
-        struc_cond=False):
-    """
-    Parameters:
-        seq (torch.tensor, required): (I,L) integer sequence 
-
-        msa_masked (torch.tensor, required): (I,N_short,L,48)
-
-        msa_full  (torch,.tensor, required): (I,N_long,L,25)
-        
-        xyz_t (torch,tensor): (T,L,27,3) template crds BEFORE they go into get_init_xyz 
-        
-        t1d (torch.tensor, required): (I,L,22) this is the t1d before tacking on the chi angles 
-        
-        str_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where structure is masked at False positions 
-
-        seq_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where seq is masked at False positions
-        t1d_24: is there an extra dimension to input structure confidence?
-
-        diffuser: diffuser class
-        
-        t: time step
-
-    NOTE: in the MSA, the order is 20aa, 1x unknown, 1x mask token. We set the masked region to 22 (masked).
-        For the t1d, this has 20aa, 1x unkown, and 1x template conf. Here, we set the masked region to 21 (unknown).
-        This, we think, makes sense, as the template in normal RF training does not perfectly correspond to the MSA.
-    """
-
-    
-    
-    #ic(input_seq_mask.shape)
-    #ic(seq.shape)
-    #ic(msa_masked.shape)
-    #ic(msa_full.shape)
-    #ic(t1d.shape)
-    #ic(xyz_t.shape)
-    #ic(input_str_mask.shape)
-    #ic(mask_msa.shape)
-
-    ###########
-    seq_mask = input_seq_mask
-
-
-    ######################
-    ###sequence diffusion###
-    ######################
-    
-    str_mask     = input_str_mask
-    
-    x_0          = torch.nn.functional.one_hot(seq[0,...],num_classes=22).float()*2-1
-    seq_diffused = diffuser.q_sample(x_0,t,mask=seq_mask)
-    
-    seq_tmp=torch.argmax(seq_diffused,axis=-1).to(device=seq.device)
-    seq=seq_tmp.repeat(seq.shape[0], 1)
-
-    ###################
-    ###msa diffusion###
-    ###################
-
-    ### msa_masked ###
-    #ic(msa_masked.shape)
-    B,N,L,_=msa_masked.shape
-    msa_masked[:,0,:,:22] = seq_diffused
-
-    x_0_msa = msa_masked[0,1:,:,:22].float()*2-1
-    msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1)
-    msa_diffused = diffuser.q_sample(x_0_msa,torch.tensor([t]),mask=msa_seq_mask)
-    
-    msa_masked[:,1:,:,:22] = torch.clone(msa_diffused)
-
-    # index 44/45 is insertion/deletion
-    # index 43 is the masked token NOTE check this
-    # index 42 is the unknown token 
-    msa_masked[:,0,:,22:44] = seq_diffused
-    msa_masked[:,1:,:,22:44] = msa_diffused
-
-    # insertion/deletion stuff 
-    msa_masked[:,0,~seq_mask,44:46] = 0
-
-    ### msa_full ### 
-    ################
-    #make msa_full same size as msa_masked
-    #ic(msa_full.shape)
-    msa_full = msa_full[:,:msa_masked.shape[1],:,:]
-    msa_full[:,0,:,:22] = seq_diffused
-    msa_full[:,1:,:,:22] = msa_diffused
-
-    ### t1d ###
-    ########### 
-    # NOTE: adjusting t1d last dim (confidence) from sequence mask
-    t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device)
-    t1d[:,:,:21] = seq_diffused[...,:21]
-
-    #t1d[:,:,21] *= input_t1dconf_mask
-    #set diffused conf to 0 and everything else to 1
-    t1d[:,~seq_mask,21] = 0.0
-    t1d[:,seq_mask,21] = 1.0
-
-    t1d[:1,:,22] = 1-t/diffuser.num_timesteps
-    
-    #to do add structure confidence metric; need to expand dimensions of chkpt b4
-    #if t1d_24: JG - changed to be default
-    t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device)
-    t1d[:,~str_mask,23] = 0.0
-    t1d[:,str_mask,23] = 1.0
-
-    if dssp:
-        print(f'adding dssp {frac_provide_dssp} of time')
-        t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],4)).float()), -1).to(seq.device)
-        #dssp info
-        #mask some percentage of dssp info in range dssp_mask_percentage[0],dssp_mask_percentage[1]
-        percentage_mask=random.randint(dssp_mask_percentage[0], dssp_mask_percentage[1])
-        dssp=annotate_sse(np.array(xyz_t[0,:,1,:].squeeze()), percentage_mask=percentage_mask)
-        #dssp_unmasked = annotate_sse(np.array(xyz_t[0,:,1,:].squeeze()), percentage_mask=0)
-        if np.random.rand()>frac_provide_dssp:
-            print('masking dssp')
-            dssp[...]=0 #replace with mask token
-            dssp[:,-1]=1
-        t1d[...,24:]=dssp
-    
-    if hotspots:
-        print(f"adding hotspots {frac_provide_contacts} of time")
-        t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device)
-        #mask all contacts some fraction of the time
-        if np.random.rand()>frac_provide_contacts:
-            print('masking contacts')
-            contacts = torch.zeros(L) 
-        t1d[...,-1] = contacts
-
-    ### xyz_t ###
-    #############
-    xyz_t = get_init_xyz(xyz_t[None])
-    xyz_t = xyz_t[0]
-    #Sequence masking
-    xyz_t[:,:,3:,:] = float('nan')
-    # Structure masking
-    if struc_cond:
-        print("non-autoregressive structure conditioning")
-        r = diffuser.alphas_cumprod[t]
-        xyz_mask = (torch.rand(xyz_t.shape[1]) > r).to(torch.bool).to(seq.device)
-        xyz_mask = torch.logical_and(xyz_mask,~str_mask)
-        xyz_t[:,xyz_mask,:,:] = float('nan')
-    else:
-        xyz_t[:,~str_mask,:,:] = float('nan')
-    
-    ### mask_msa ###
-    ################
-    # NOTE: this is for loss scoring
-    mask_msa[:,:,~loss_seq_mask] = False
-    
-    out=dict(
-            seq= seq,
-            msa_masked= msa_masked,
-            msa_full= msa_full,
-            xyz_t= xyz_t,
-            t1d= t1d,
-            mask_msa= mask_msa,
-            seq_diffused= seq_diffused
-            )
-    
-    return out
diff --git a/spaces/merve/data-leak/public/base-rate/style.css b/spaces/merve/data-leak/public/base-rate/style.css
deleted file mode 100644
index 5ba8d020fda588a3e7f61ff3fab4d377aa3bd4f2..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/base-rate/style.css
+++ /dev/null
@@ -1,134 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-.tooltip {
-  top: -1000px;
-  position: fixed;
-  padding: 10px;
-  background: rgba(255, 255, 255, .90);
-  border: 1px solid lightgray;
-  pointer-events: none;
-  width: auto;
-
-}
-.tooltip-hidden{
-  opacity: 0;
-  transition: all .3s;
-  transition-delay: .1s;
-}
-
-@media (max-width: 590px){
-  div.tooltip{
-    bottom: -1px;
-    width: calc(100%);
-    left: -1px !important;
-    right: -1px !important;
-    top: auto !important;
-    width: auto !important;
-  }
-}
-
-svg{
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-  font-family: 'Google Sans', sans-serif;
-  /*pointer-events: none;*/
-  text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
-  text-shadow: 0 1px 0 rgba(255,255,255, .6), 1px 0 0 rgba(255,255,255, .6), 0 -1px 0 rgba(255,255,255, .6), -1px 0 0 rgba(255,255,255, .6);
-}
-
-
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-  max-width: 900px;
-}
-
-h1{
-}
-
-h1{
-  /*text-align: center;*/
-}
-
-h3{
-  font-size: 20px;
-}
-#big-matrix{
-  text-align: center;
-  margin-top: 40px;
-  font-family: 'Google Sans', sans-serif;
-
-}
-div.big-container{
-  display: inline-block;
-  margin: 10px;
-}
-
-#metrics{
-  text-align: center;
-}
-div.metrics-container{
-  display: inline-block;
-  margin: 10px;
-}
-
-div.metrics-container > div{
-  display: inline-block;
-  vertical-align: middle;
-  pointer-events: none;
-}
-
-
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-
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-  cursor: pointer;
-  fill-opacity: 0;
-  fill: #f0f;
-  stroke-opacity: 0;
-}
-
-svg.dragging{
-  cursor: pointer;
-}
-
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-  /*background: #000; */
-  color: #000;
-  border: 1px solid #eee;
-  width: 1em; 
-  display: inline-block;
-  padding-left: 2px;
-  padding-right: 2px;
-  font-style: normal;
-}
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-#instructions{
-  margin-top: 10px;
-  margin-bottom: 10px;
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diff --git a/spaces/merve/hidden-bias/server-side/fill-in-the-blank/py/Dockerfile b/spaces/merve/hidden-bias/server-side/fill-in-the-blank/py/Dockerfile
deleted file mode 100644
index 8fd3b0a4a1776bd3fe714d73e1e3be6107d66272..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/server-side/fill-in-the-blank/py/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-# Use the official lightweight Python image.
-# https://hub.docker.com/_/python
-FROM python:3.9-slim
-
-# Allow statements and log messages to immediately appear in the Knative logs
-ENV PYTHONUNBUFFERED True
-
-# Copy local code to the container image.
-ENV APP_HOME /app
-WORKDIR $APP_HOME
-COPY . ./
-
-# Copy requirements.txt to the docker image and install packages
-COPY requirements.txt /
-RUN pip install -r requirements.txt
-
-# Download models
-ADD https://storage.googleapis.com/uncertainty-over-space/zari-bert-cda/pytorch_model.bin zari-bert-cda/pytorch_model.bin
-ADD https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/pytorch_model.bin bert-large-uncased-whole-word-masking/pytorch_model.bin
-
-# Run the web service on container startup.
-CMD exec gunicorn --bind :$PORT --workers 1 --threads 1 --timeout 0 main:app
diff --git a/spaces/mfrashad/ClothingGAN/models/biggan/pytorch_biggan/pytorch_pretrained_biggan/__init__.py b/spaces/mfrashad/ClothingGAN/models/biggan/pytorch_biggan/pytorch_pretrained_biggan/__init__.py
deleted file mode 100644
index b570848421afd921fae635569c97d0f8f5b33c80..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/models/biggan/pytorch_biggan/pytorch_pretrained_biggan/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .config import BigGANConfig
-from .model import BigGAN
-from .file_utils import PYTORCH_PRETRAINED_BIGGAN_CACHE, cached_path
-from .utils import (truncated_noise_sample, save_as_images,
-                    convert_to_images, display_in_terminal,
-                    one_hot_from_int, one_hot_from_names)
diff --git a/spaces/mfrashad/ClothingGAN/models/stylegan/model.py b/spaces/mfrashad/ClothingGAN/models/stylegan/model.py
deleted file mode 100644
index a230961c4d1bf0bd2d1efe7972b4baa33c5d7013..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/models/stylegan/model.py
+++ /dev/null
@@ -1,456 +0,0 @@
-# Copyright 2020 Erik Härkönen. All rights reserved.
-# This file is licensed to you 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 REPRESENTATIONS
-# OF ANY KIND, either express or implied. See the License for the specific language
-# governing permissions and limitations under the License.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from collections import OrderedDict
-from pathlib import Path
-import requests
-import pickle
-import sys
-
-import numpy as np
-
-# Reimplementation of StyleGAN in PyTorch
-# Source: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
-
-class MyLinear(nn.Module):
-    """Linear layer with equalized learning rate and custom learning rate multiplier."""
-    def __init__(self, input_size, output_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True):
-        super().__init__()
-        he_std = gain * input_size**(-0.5) # He init
-        # Equalized learning rate and custom learning rate multiplier.
-        if use_wscale:
-            init_std = 1.0 / lrmul
-            self.w_mul = he_std * lrmul
-        else:
-            init_std = he_std / lrmul
-            self.w_mul = lrmul
-        self.weight = torch.nn.Parameter(torch.randn(output_size, input_size) * init_std)
-        if bias:
-            self.bias = torch.nn.Parameter(torch.zeros(output_size))
-            self.b_mul = lrmul
-        else:
-            self.bias = None
-
-    def forward(self, x):
-        bias = self.bias
-        if bias is not None:
-            bias = bias * self.b_mul
-        return F.linear(x, self.weight * self.w_mul, bias)
-
-class MyConv2d(nn.Module):
-    """Conv layer with equalized learning rate and custom learning rate multiplier."""
-    def __init__(self, input_channels, output_channels, kernel_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True,
-                intermediate=None, upscale=False):
-        super().__init__()
-        if upscale:
-            self.upscale = Upscale2d()
-        else:
-            self.upscale = None
-        he_std = gain * (input_channels * kernel_size ** 2) ** (-0.5) # He init
-        self.kernel_size = kernel_size
-        if use_wscale:
-            init_std = 1.0 / lrmul
-            self.w_mul = he_std * lrmul
-        else:
-            init_std = he_std / lrmul
-            self.w_mul = lrmul
-        self.weight = torch.nn.Parameter(torch.randn(output_channels, input_channels, kernel_size, kernel_size) * init_std)
-        if bias:
-            self.bias = torch.nn.Parameter(torch.zeros(output_channels))
-            self.b_mul = lrmul
-        else:
-            self.bias = None
-        self.intermediate = intermediate
-
-    def forward(self, x):
-        bias = self.bias
-        if bias is not None:
-            bias = bias * self.b_mul
-        
-        have_convolution = False
-        if self.upscale is not None and min(x.shape[2:]) * 2 >= 128:
-            # this is the fused upscale + conv from StyleGAN, sadly this seems incompatible with the non-fused way
-            # this really needs to be cleaned up and go into the conv...
-            w = self.weight * self.w_mul
-            w = w.permute(1, 0, 2, 3)
-            # probably applying a conv on w would be more efficient. also this quadruples the weight (average)?!
-            w = F.pad(w, (1,1,1,1))
-            w = w[:, :, 1:, 1:]+ w[:, :, :-1, 1:] + w[:, :, 1:, :-1] + w[:, :, :-1, :-1]
-            x = F.conv_transpose2d(x, w, stride=2, padding=(w.size(-1)-1)//2)
-            have_convolution = True
-        elif self.upscale is not None:
-            x = self.upscale(x)
-    
-        if not have_convolution and self.intermediate is None:
-            return F.conv2d(x, self.weight * self.w_mul, bias, padding=self.kernel_size//2)
-        elif not have_convolution:
-            x = F.conv2d(x, self.weight * self.w_mul, None, padding=self.kernel_size//2)
-        
-        if self.intermediate is not None:
-            x = self.intermediate(x)
-        if bias is not None:
-            x = x + bias.view(1, -1, 1, 1)
-        return x
-
-class NoiseLayer(nn.Module):
-    """adds noise. noise is per pixel (constant over channels) with per-channel weight"""
-    def __init__(self, channels):
-        super().__init__()
-        self.weight = nn.Parameter(torch.zeros(channels))
-        self.noise = None
-    
-    def forward(self, x, noise=None):
-        if noise is None and self.noise is None:
-            noise = torch.randn(x.size(0), 1, x.size(2), x.size(3), device=x.device, dtype=x.dtype)
-        elif noise is None:
-            # here is a little trick: if you get all the noiselayers and set each
-            # modules .noise attribute, you can have pre-defined noise.
-            # Very useful for analysis
-            noise = self.noise
-        x = x + self.weight.view(1, -1, 1, 1) * noise
-        return x
-
-class StyleMod(nn.Module):
-    def __init__(self, latent_size, channels, use_wscale):
-        super(StyleMod, self).__init__()
-        self.lin = MyLinear(latent_size,
-                            channels * 2,
-                            gain=1.0, use_wscale=use_wscale)
-        
-    def forward(self, x, latent):
-        style = self.lin(latent) # style => [batch_size, n_channels*2]
-        shape = [-1, 2, x.size(1)] + (x.dim() - 2) * [1]
-        style = style.view(shape)  # [batch_size, 2, n_channels, ...]
-        x = x * (style[:, 0] + 1.) + style[:, 1]
-        return x
-
-class PixelNormLayer(nn.Module):
-    def __init__(self, epsilon=1e-8):
-        super().__init__()
-        self.epsilon = epsilon
-    def forward(self, x):
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + self.epsilon)
-
-class BlurLayer(nn.Module):
-    def __init__(self, kernel=[1, 2, 1], normalize=True, flip=False, stride=1):
-        super(BlurLayer, self).__init__()
-        kernel=[1, 2, 1]
-        kernel = torch.tensor(kernel, dtype=torch.float32)
-        kernel = kernel[:, None] * kernel[None, :]
-        kernel = kernel[None, None]
-        if normalize:
-            kernel = kernel / kernel.sum()
-        if flip:
-            kernel = kernel[:, :, ::-1, ::-1]
-        self.register_buffer('kernel', kernel)
-        self.stride = stride
-    
-    def forward(self, x):
-        # expand kernel channels
-        kernel = self.kernel.expand(x.size(1), -1, -1, -1)
-        x = F.conv2d(
-            x,
-            kernel,
-            stride=self.stride,
-            padding=int((self.kernel.size(2)-1)/2),
-            groups=x.size(1)
-        )
-        return x
-
-def upscale2d(x, factor=2, gain=1):
-    assert x.dim() == 4
-    if gain != 1:
-        x = x * gain
-    if factor != 1:
-        shape = x.shape
-        x = x.view(shape[0], shape[1], shape[2], 1, shape[3], 1).expand(-1, -1, -1, factor, -1, factor)
-        x = x.contiguous().view(shape[0], shape[1], factor * shape[2], factor * shape[3])
-    return x
-
-class Upscale2d(nn.Module):
-    def __init__(self, factor=2, gain=1):
-        super().__init__()
-        assert isinstance(factor, int) and factor >= 1
-        self.gain = gain
-        self.factor = factor
-    def forward(self, x):
-        return upscale2d(x, factor=self.factor, gain=self.gain)
-
-class G_mapping(nn.Sequential):
-    def __init__(self, nonlinearity='lrelu', use_wscale=True):
-        act, gain = {'relu': (torch.relu, np.sqrt(2)),
-                     'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
-        layers = [
-            ('pixel_norm', PixelNormLayer()),
-            ('dense0', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense0_act', act),
-            ('dense1', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense1_act', act),
-            ('dense2', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense2_act', act),
-            ('dense3', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense3_act', act),
-            ('dense4', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense4_act', act),
-            ('dense5', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense5_act', act),
-            ('dense6', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense6_act', act),
-            ('dense7', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense7_act', act)
-        ]
-        super().__init__(OrderedDict(layers))
-        
-    def forward(self, x):
-        return super().forward(x)
-
-class Truncation(nn.Module):
-    def __init__(self, avg_latent, max_layer=8, threshold=0.7):
-        super().__init__()
-        self.max_layer = max_layer
-        self.threshold = threshold
-        self.register_buffer('avg_latent', avg_latent)
-    def forward(self, x):
-        assert x.dim() == 3
-        interp = torch.lerp(self.avg_latent, x, self.threshold)
-        do_trunc = (torch.arange(x.size(1)) < self.max_layer).view(1, -1, 1)
-        return torch.where(do_trunc, interp, x)
-
-class LayerEpilogue(nn.Module):
-    """Things to do at the end of each layer."""
-    def __init__(self, channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
-        super().__init__()
-        layers = []
-        if use_noise:
-            layers.append(('noise', NoiseLayer(channels)))
-        layers.append(('activation', activation_layer))
-        if use_pixel_norm:
-            layers.append(('pixel_norm', PixelNorm()))
-        if use_instance_norm:
-            layers.append(('instance_norm', nn.InstanceNorm2d(channels)))
-        self.top_epi = nn.Sequential(OrderedDict(layers))
-        if use_styles:
-            self.style_mod = StyleMod(dlatent_size, channels, use_wscale=use_wscale)
-        else:
-            self.style_mod = None
-    def forward(self, x, dlatents_in_slice=None):
-        x = self.top_epi(x)
-        if self.style_mod is not None:
-            x = self.style_mod(x, dlatents_in_slice)
-        else:
-            assert dlatents_in_slice is None
-        return x
-
-
-class InputBlock(nn.Module):
-    def __init__(self, nf, dlatent_size, const_input_layer, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
-        super().__init__()
-        self.const_input_layer = const_input_layer
-        self.nf = nf
-        if self.const_input_layer:
-            # called 'const' in tf
-            self.const = nn.Parameter(torch.ones(1, nf, 4, 4))
-            self.bias = nn.Parameter(torch.ones(nf))
-        else:
-            self.dense = MyLinear(dlatent_size, nf*16, gain=gain/4, use_wscale=use_wscale) # tweak gain to match the official implementation of Progressing GAN
-        self.epi1 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-        self.conv = MyConv2d(nf, nf, 3, gain=gain, use_wscale=use_wscale)
-        self.epi2 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-        
-    def forward(self, dlatents_in_range):
-        batch_size = dlatents_in_range.size(0)
-        if self.const_input_layer:
-            x = self.const.expand(batch_size, -1, -1, -1)
-            x = x + self.bias.view(1, -1, 1, 1)
-        else:
-            x = self.dense(dlatents_in_range[:, 0]).view(batch_size, self.nf, 4, 4)
-        x = self.epi1(x, dlatents_in_range[:, 0])
-        x = self.conv(x)
-        x = self.epi2(x, dlatents_in_range[:, 1])
-        return x
-
-
-class GSynthesisBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
-        # 2**res x 2**res # res = 3..resolution_log2
-        super().__init__()
-        if blur_filter:
-            blur = BlurLayer(blur_filter)
-        else:
-            blur = None
-        self.conv0_up = MyConv2d(in_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale,
-                                 intermediate=blur, upscale=True)
-        self.epi1 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-        self.conv1 = MyConv2d(out_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale)
-        self.epi2 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-            
-    def forward(self, x, dlatents_in_range):
-        x = self.conv0_up(x)
-        x = self.epi1(x, dlatents_in_range[:, 0])
-        x = self.conv1(x)
-        x = self.epi2(x, dlatents_in_range[:, 1])
-        return x
-
-class G_synthesis(nn.Module):
-    def __init__(self,
-            dlatent_size        = 512,          # Disentangled latent (W) dimensionality.
-            num_channels        = 3,            # Number of output color channels.
-            resolution          = 1024,         # Output resolution.
-            fmap_base           = 8192,         # Overall multiplier for the number of feature maps.
-            fmap_decay          = 1.0,          # log2 feature map reduction when doubling the resolution.
-            fmap_max            = 512,          # Maximum number of feature maps in any layer.
-            use_styles          = True,         # Enable style inputs?
-            const_input_layer   = True,         # First layer is a learned constant?
-            use_noise           = True,         # Enable noise inputs?
-            randomize_noise     = True,         # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables.
-            nonlinearity        = 'lrelu',      # Activation function: 'relu', 'lrelu'
-            use_wscale          = True,         # Enable equalized learning rate?
-            use_pixel_norm      = False,        # Enable pixelwise feature vector normalization?
-            use_instance_norm   = True,         # Enable instance normalization?
-            dtype               = torch.float32,  # Data type to use for activations and outputs.
-            blur_filter         = [1,2,1],      # Low-pass filter to apply when resampling activations. None = no filtering.
-        ):
-        
-        super().__init__()
-        def nf(stage):
-            return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
-        self.dlatent_size = dlatent_size
-        resolution_log2 = int(np.log2(resolution))
-        assert resolution == 2**resolution_log2 and resolution >= 4
-
-        act, gain = {'relu': (torch.relu, np.sqrt(2)),
-                     'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
-        num_layers = resolution_log2 * 2 - 2
-        num_styles = num_layers if use_styles else 1
-        torgbs = []
-        blocks = []
-        for res in range(2, resolution_log2 + 1):
-            channels = nf(res-1)
-            name = '{s}x{s}'.format(s=2**res)
-            if res == 2:
-                blocks.append((name,
-                               InputBlock(channels, dlatent_size, const_input_layer, gain, use_wscale,
-                                      use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
-                
-            else:
-                blocks.append((name,
-                               GSynthesisBlock(last_channels, channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
-            last_channels = channels
-        self.torgb = MyConv2d(channels, num_channels, 1, gain=1, use_wscale=use_wscale)
-        self.blocks = nn.ModuleDict(OrderedDict(blocks))
-        
-    def forward(self, dlatents_in):
-        # Input: Disentangled latents (W) [minibatch, num_layers, dlatent_size].
-        # lod_in = tf.cast(tf.get_variable('lod', initializer=np.float32(0), trainable=False), dtype)
-        batch_size = dlatents_in.size(0)       
-        for i, m in enumerate(self.blocks.values()):
-            if i == 0:
-                x = m(dlatents_in[:, 2*i:2*i+2])
-            else:
-                x = m(x, dlatents_in[:, 2*i:2*i+2])
-        rgb = self.torgb(x)
-        return rgb
-
-
-class StyleGAN_G(nn.Sequential):
-    def __init__(self, resolution, truncation=1.0):
-        self.resolution = resolution
-        self.layers = OrderedDict([
-            ('g_mapping', G_mapping()),
-            #('truncation', Truncation(avg_latent)),
-            ('g_synthesis', G_synthesis(resolution=resolution)),
-        ])
-        super().__init__(self.layers)
-
-    def forward(self, x, latent_is_w=False):
-        if isinstance(x, list):
-            assert len(x) == 18, 'Must provide 1 or 18 latents'
-            if not latent_is_w:
-                x = [self.layers['g_mapping'].forward(l) for l in x]
-            x = torch.stack(x, dim=1)
-        else:
-            if not latent_is_w:
-                x = self.layers['g_mapping'].forward(x)
-            x = x.unsqueeze(1).expand(-1, 18, -1)
-
-        x = self.layers['g_synthesis'].forward(x)
-
-        return x
-
-    # From: https://github.com/lernapparat/lernapparat/releases/download/v2019-02-01/
-    def load_weights(self, checkpoint):    
-        self.load_state_dict(torch.load(checkpoint))
-
-    def export_from_tf(self, pickle_path):
-        module_path = Path(__file__).parent / 'stylegan_tf'
-        sys.path.append(str(module_path.resolve()))
-
-        import dnnlib, dnnlib.tflib, pickle, torch, collections
-        dnnlib.tflib.init_tf()
-
-        weights = pickle.load(open(pickle_path,'rb'))
-        weights_pt = [collections.OrderedDict([(k, torch.from_numpy(v.value().eval())) for k,v in w.trainables.items()]) for w in weights]
-        #torch.save(weights_pt, pytorch_name)
-
-        # then on the PyTorch side run
-        state_G, state_D, state_Gs = weights_pt #torch.load('./karras2019stylegan-ffhq-1024x1024.pt')
-        def key_translate(k):
-            k = k.lower().split('/')
-            if k[0] == 'g_synthesis':
-                if not k[1].startswith('torgb'):
-                    k.insert(1, 'blocks')
-                k = '.'.join(k)
-                k = (k.replace('const.const','const').replace('const.bias','bias').replace('const.stylemod','epi1.style_mod.lin')
-                    .replace('const.noise.weight','epi1.top_epi.noise.weight')
-                    .replace('conv.noise.weight','epi2.top_epi.noise.weight')
-                    .replace('conv.stylemod','epi2.style_mod.lin')
-                    .replace('conv0_up.noise.weight', 'epi1.top_epi.noise.weight')
-                    .replace('conv0_up.stylemod','epi1.style_mod.lin')
-                    .replace('conv1.noise.weight', 'epi2.top_epi.noise.weight')
-                    .replace('conv1.stylemod','epi2.style_mod.lin')
-                    .replace('torgb_lod0','torgb'))
-            else:
-                k = '.'.join(k)
-            return k
-
-        def weight_translate(k, w):
-            k = key_translate(k)
-            if k.endswith('.weight'):
-                if w.dim() == 2:
-                    w = w.t()
-                elif w.dim() == 1:
-                    pass
-                else:
-                    assert w.dim() == 4
-                    w = w.permute(3, 2, 0, 1)
-            return w
-
-        # we delete the useless torgb filters
-        param_dict = {key_translate(k) : weight_translate(k, v) for k,v in state_Gs.items() if 'torgb_lod' not in key_translate(k)}
-        if 1:
-            sd_shapes = {k : v.shape for k,v in self.state_dict().items()}
-            param_shapes = {k : v.shape for k,v in param_dict.items() }
-
-            for k in list(sd_shapes)+list(param_shapes):
-                pds = param_shapes.get(k)
-                sds = sd_shapes.get(k)
-                if pds is None:
-                    print ("sd only", k, sds)
-                elif sds is None:
-                    print ("pd only", k, pds)
-                elif sds != pds:
-                    print ("mismatch!", k, pds, sds)
-
-        self.load_state_dict(param_dict, strict=False) # needed for the blur kernels
-        torch.save(self.state_dict(), Path(pickle_path).with_suffix('.pt'))
\ No newline at end of file
diff --git a/spaces/mm2593/Gradiospeech2Text2Story2Video/app.py b/spaces/mm2593/Gradiospeech2Text2Story2Video/app.py
deleted file mode 100644
index 802d78aff8e7fa6fc5ed4494c961c6cf4b75cebb..0000000000000000000000000000000000000000
--- a/spaces/mm2593/Gradiospeech2Text2Story2Video/app.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import io, base64
-from PIL import Image
-import numpy as np
-import tensorflow as tf
-import mediapy
-import os
-import sys
-from huggingface_hub import snapshot_download
-
-import streamlit as st
-import firebase_admin
-from firebase_admin import credentials
-from firebase_admin import firestore
-import datetime
-import tempfile
-from typing import Optional
-import numpy as np
-from TTS.utils.manage import ModelManager
-from TTS.utils.synthesizer import Synthesizer
-             
-
-# firestore singleton is a cached multiuser instance to persist shared crowdsource memory
-@st.experimental_singleton
-def get_db_firestore():
-    cred = credentials.Certificate('test.json')
-    firebase_admin.initialize_app(cred, {'projectId': u'clinical-nlp-b9117',})
-    db = firestore.client()
-    return db
-
-#start firestore singleton
-db = get_db_firestore()
-
-# create ASR ML pipeline
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-
-# create Text Classification pipeline
-classifier = pipeline("text-classification")
-
-# create text generator pipeline
-story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator")
-
-# transcribe function
-def transcribe(audio):
-    text = asr(audio)["text"]
-    return text
-
-def speech_to_text(speech):
-    text = asr(speech)["text"]
-    return text
-
-def text_to_sentiment(text):
-    sentiment = classifier(text)[0]["label"]
-    return sentiment 
-
-def upsert(text):
-    date_time =str(datetime.datetime.today())
-    doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
-    doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/Text2SpeechSentimentSave', u'last': text, u'born': date_time,})
-    saved = select('Text2SpeechSentimentSave', date_time)
-    # check it here:  https://console.firebase.google.com/u/0/project/clinical-nlp-b9117/firestore/data/~2FStreamlitSpaces
-    return saved
-      
-def select(collection, document):
-    doc_ref = db.collection(collection).document(document)
-    doc = doc_ref.get()
-    docid = ("The id is: ", doc.id)
-    contents = ("The contents are: ", doc.to_dict())
-    return contents
-          
-def selectall(text):
-    docs = db.collection('Text2SpeechSentimentSave').stream()
-    doclist=''
-    for doc in docs:
-        r=(f'{doc.id} => {doc.to_dict()}')
-        doclist += r
-    return doclist 
-
-# story gen
-def generate_story(choice, input_text):
-    query = "<BOS> <{0}> {1}".format(choice, input_text)
-    generated_text = story_gen(query)
-    generated_text = generated_text[0]['generated_text']
-    generated_text = generated_text.split('> ')[2]
-    return generated_text
-    
-# images gen
-def generate_images(text):
-    steps=50
-    width=256
-    height=256
-    num_images=4
-    diversity=6
-    image_bytes = image_gen(text, steps, width, height, num_images, diversity)
-    generated_images = []
-    for image in image_bytes[1]:
-        image_str = image[0]
-        image_str = image_str.replace("data:image/png;base64,","")
-        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
-        img = Image.open(io.BytesIO(decoded_bytes))
-        generated_images.append(img)
-    return generated_images
-    
-# reductionism - interpolate 4 images - todo - unhardcode the pattern
-def generate_interpolation(gallery):
-    times_to_interpolate = 4
-    generated_images = []
-    for image_str in gallery:
-        image_str = image_str.replace("data:image/png;base64,","")
-        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
-        img = Image.open(io.BytesIO(decoded_bytes))
-        generated_images.append(img)
-    generated_images[0].save('frame_0.png')
-    generated_images[1].save('frame_1.png')
-    generated_images[2].save('frame_2.png')
-    generated_images[3].save('frame_3.png')
-    input_frames = ["frame_0.png", "frame_1.png", "frame_2.png", "frame_3.png"]
-    frames = list(util.interpolate_recursively_from_files(input_frames, times_to_interpolate, interpolator))
-    mediapy.write_video("out.mp4", frames, fps=15)
-    return "out.mp4"
-
-# image generator
-image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")
-
-# video generator
-os.system("git clone https://github.com/google-research/frame-interpolation")
-sys.path.append("frame-interpolation")
-from eval import interpolator, util
-
-ffmpeg_path = util.get_ffmpeg_path()
-mediapy.set_ffmpeg(ffmpeg_path)
-model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
-interpolator = interpolator.Interpolator(model, None)
-
-demo = gr.Blocks()
-with demo:
-
-    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
-    text = gr.Textbox()
-    label = gr.Label()
-    saved = gr.Textbox()
-    savedAll = gr.Textbox()
-    audio = gr.Audio(label="Output", interactive=False)
-    
-    b1 = gr.Button("Recognize Speech")
-    b2 = gr.Button("Classify Sentiment")
-    b3 = gr.Button("Save Speech to Text")
-    b4 = gr.Button("Retrieve All")
-
-    input_story_type = gr.Radio(choices=['superhero', 'action', 'drama', 'horror', 'thriller', 'sci_fi'], value='sci_fi', label="Genre")
-    input_start_text = gr.Textbox(placeholder='A teddy bear outer space', label="Starting Text")
-    
-    gr.Markdown("1. Select a type of story, then write some starting text! Then hit the 'Generate Story' button to generate a story! Feel free to edit the generated story afterwards!")
-    button_gen_story = gr.Button("Generate Story")
-    gr.Markdown("2. After generating a story, hit the 'Generate Images' button to create some visuals for your story! (Can re-run multiple times!)")
-    button_gen_images = gr.Button("Generate Images")
-    gr.Markdown("3. After generating some images, hit the 'Generate Video' button to create a short video by interpolating the previously generated visuals!")
-    button_gen_video = gr.Button("Generate Video")
-    output_generated_story = gr.Textbox(label="Generated Story")
-    output_gallery = gr.Gallery(label="Generated Story Images")
-    output_interpolation = gr.Video(label="Generated Video")
-            
-    # Bind functions to buttons
-    button_gen_story.click(fn=generate_story, inputs=[input_story_type , input_start_text], outputs=output_generated_story)
-    button_gen_images.click(fn=generate_images, inputs=output_generated_story, outputs=output_gallery)
-    button_gen_video.click(fn=generate_interpolation, inputs=output_gallery, outputs=output_interpolation)
-    
-    b1.click(speech_to_text, inputs=audio_file, outputs=input_start_text )
-    b2.click(text_to_sentiment, inputs=text, outputs=label)
-    b3.click(upsert, inputs=text, outputs=saved)
-    b4.click(selectall, inputs=text, outputs=savedAll)
-    
-demo.launch(debug=True, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/mmecheri/Rakuten_Streamlit/page_descriptions/models_image_txt_initial.md b/spaces/mmecheri/Rakuten_Streamlit/page_descriptions/models_image_txt_initial.md
deleted file mode 100644
index ad8350fe448caf98b42f0e2ab355dd756c0f1461..0000000000000000000000000000000000000000
--- a/spaces/mmecheri/Rakuten_Streamlit/page_descriptions/models_image_txt_initial.md
+++ /dev/null
@@ -1,99 +0,0 @@
-Sachant la complixité de notre jeu de données Images et les ressemblances qui existent entre les différentes classes, nous avons rapidement opté pour l’utilisation des méthodes d’apprentissage par transfert(transfert learning) avec des modèles  reposant sur les  réseaux de neurones artificiels, capables d’apprentissage profond (Deep Learning). Nous avons ainsi utilisé les modèles les plus adaptés aux images : les  réseau de neurones convolutifs (CNN)
----Insersetion---
-#### Choix des modèles pré-entrainés
-
-Parmis la listes des modèles présentés [ici](https://keras.io/api/applications/),
-nous avons retenu les modèles qui ont le moins de paramètres afin de réduire les durées d’entrainement et limiter ainsi les contraintes liées au manque de ressources de calcul
----Insersetion---
->-	**Optimiseur** : Adam est un optimiseur adaptatif éprouvé et très populaire
->-	**Fonction de perte**: la « ***Categorical Cross-Entropy*** » adaptée à notre cas de  classification multi-classes
->-	**Batch size** : 32 et 64 ont été testés
->-	**Learning rate (LR)** :  modèles entrainés avec un LR à 0.001 (valeur par défaut), ensuite un de très faible valeur à 0.00001. Et enfin avec les valeurs obenues à l’issu de l’étape de l’optimtisation des Learning Rate
-
->- **Augmentation de données** appliquée aux images du jeu de données d’entrainement :
->>- Mise à l'échelle des pixels entre 0 et 1 
->>- Étirement de l'image selon un certain angle.  Donne à l’image un aspect plus écrasé ou étiré en diagonale
->>- Plage de rotation à rotation aléatoire [-45, 45]
->>- Mouvement de l'image dans le sens horizontal par rapport à la largeur de l'image
->>- Mouvement de l'image dans le sens vertical par rapport à la hauteur de l'image
->>- Plage de zoom : zoom arrière et zoom avant [-20%, +20%]
->>- Retournement de l'image horizontalement
->>- Retournement de l'image verticalement
----Insersetion---
-
-#### Etape 1 - Sélection des meilleurs modèles
-
-###### Démarche
->>>-&nbsp;Entrainer les modèles selectionnés avec des images d’entrées de diffrérentes tailles, et ce sans augmentation de données ni fine-tuning</br>
->>>-&nbsp;Utiliser le Learning rate(LR) par défaut (0.001)</br>
->>>-&nbsp;Varier le batch size  de 32 à 64</br>
->>>-&nbsp;Varier le nombre d’Epoch de 10, 20 à 40</br>
->>>-&nbsp;Noter les 5 meilleurs modèles selon la mesure F1-score weighted</br>
----Insersetion---
-###### Résultats
-Les résultats de l'ensemble des modèles testés:
----Insersetion---
-Les 5 meilleurs modèles selon F1-score Weighted sont :
----Insersetion---
-##### Etape 2 - Augmentation des données avec les meilleurs modèles obtenus à l’Etape 1
-
-###### Démarche
->>>-&nbsp;Entrainer les 5 meilleurs modèles de l’Etape 1 avec augmentation des données (sans fine-tuning  et  LR toujours par défaut à 0.001)
-###### Résultats
----Insersetion---
-
-
-##### Etape 3 - Augmentation des données + fine tuning des modèles 
-
-###### Démarche
->>>-&nbsp;Fine tuning des meilleurs modèles obtenus à l’Etape 1 </br>
->>>-(Décongélation de certaines couches de nos modèles pré-entrainés)
->>>-&nbsp;Utiliser un LR très fabile(0.00001) </br>
->>>-&nbsp;Et augmentation du nombre d’Epochs  de 40 , 50 à 60</br>
-###### Résultats
----Insersetion---
-
-
-##### Etape 4 - Optimisation LR (Sans augmentation des données ni fine-tuning)
-
-###### Démarche
->> Nous avons suivi la méthode décrite dans [cet article](https://towardsdatascience.com/how-to-optimize-learning-rate-with-tensorflow-its-easier-than-you-think-164f980a7c7b) (En utilisant le callback LearningRateScheduler): 
->>>-&nbsp;&nbsp;Commencer l’entrainement avec un très faible LR (0,00001) puis augmenter sa valeur de manière exponentielle au fur à mesure des Epochs jusqu'à atteindre ≈ 2,54</br>
->>>-&nbsp;&nbsp;Etudier les courbes des résultats (perte et accuracy en fonction du LR et des Epochs)</br>
->>>-&nbsp;&nbsp;Identifier le LR qui réalise la plus petite perte (correspond en général à la plus grande accuracy), à condition que les valeurs qui l'entourent ne soient pas trop volatiles</br>
-###### Résultats
-Voici les LR obtenus pour nos différents modèls: 
----Insersetion---
-L'accuracy :
--	L'accuracy augmente lègèrement jusqu'à l'Epoch 30 
--	Commence ensuite à baisser jusqu'à l'Epoch 63 
--	Elle fluctue très légèrement entre l'Epoch 64 et 67
--	S’ aplatie ensuite à partir de l’Epoch 68 et ce juqu'à la fin de l’entrainement
-La perte : 
--	L'exact opposé est arrivé à la perte jusqu'à l'Epoch 63 (Ce qui est logique)
--	Fluctue  légèrement entre l'Epoch 64 et 69 
--	S'aplatie  pratiquement entre l’Epoch 70 et 80 
--	Fluctue esnuite significativicement à partir de l'Epoch 80 et jusqu'à la fin del’entrainement
----Insersetion---
-Le taux d'apprentissage optimal(avec la plus faible perte) est d'environ 0,0004
----Insersetion---
-##### Etape 5 - Augmentation des données, fine-tuning avec les LRs optimisés
-
-###### Démarche
->>>-&nbsp;&nbsp;Ré-entrainer nos 5 modèles avec les LR opitimaux identifiés à l’étape précédente
----Insersetion---
-###### Résultats
----Insersetion---
->- Seulement 1% de plus au niveau du F1-score Weighted pour le modèle Xception 
->- 1% de plus en F1-score Weighted et en accuracy pour le modèle InceptionV3
->- Les deux modèles semblent avoir atteint leurs limites avec ce jeu de données Images !
->- Par contre, ils atteignent ces performances beaucoup plus rapidement avec les LRs optimisés
----Insersetion---
-#### Conclusion
->	Les 2 meilleurs résultats ont été obtenus avec :
->>-	**Xception**: LR à 0,0004 , F1-score Weighted à **0.66** 
->>-	**InceptionV3**:LR à 0,0003,  F1-score weighted **0.65** 
-(Avec augmentation des données et fine-tuning)
-
-Ces résultats dépassent largement ceux annoncés dans le modèle de référence Rakuten pour la classification basée sur les données Images(0.55)
----Insersetion---
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/backtranslation/prepare-wmt18en2de.sh b/spaces/mshukor/UnIVAL/fairseq/examples/backtranslation/prepare-wmt18en2de.sh
deleted file mode 100644
index f6fd275307db50ca84c299440ae02dce49064030..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/backtranslation/prepare-wmt18en2de.sh
+++ /dev/null
@@ -1,135 +0,0 @@
-#!/bin/bash
-# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-git clone https://github.com/rsennrich/subword-nmt.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-BPEROOT=subword-nmt/subword_nmt
-BPE_TOKENS=32000
-
-URLS=(
-    "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
-    "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
-    "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz"
-    "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz"
-    "http://data.statmt.org/wmt17/translation-task/dev.tgz"
-    "http://statmt.org/wmt14/test-full.tgz"
-)
-FILES=(
-    "training-parallel-europarl-v7.tgz"
-    "training-parallel-commoncrawl.tgz"
-    "training-parallel-nc-v13.tgz"
-    "rapid2016.tgz"
-    "dev.tgz"
-    "test-full.tgz"
-)
-CORPORA=(
-    "training/europarl-v7.de-en"
-    "commoncrawl.de-en"
-    "training-parallel-nc-v13/news-commentary-v13.de-en"
-    "rapid2016.de-en"
-)
-
-if [ ! -d "$SCRIPTS" ]; then
-    echo "Please set SCRIPTS variable correctly to point to Moses scripts."
-    exit 1
-fi
-
-OUTDIR=wmt18_en_de
-
-src=en
-tgt=de
-lang=en-de
-prep=$OUTDIR
-tmp=$prep/tmp
-orig=orig
-
-mkdir -p $orig $tmp $prep
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
-    file=${FILES[i]}
-    if [ -f $file ]; then
-        echo "$file already exists, skipping download"
-    else
-        url=${URLS[i]}
-        wget "$url"
-        if [ -f $file ]; then
-            echo "$url successfully downloaded."
-        else
-            echo "$url not successfully downloaded."
-            exit 1
-        fi
-        if [ ${file: -4} == ".tgz" ]; then
-            tar zxvf $file
-        elif [ ${file: -4} == ".tar" ]; then
-            tar xvf $file
-        fi
-    fi
-done
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
-    rm $tmp/train.tags.$lang.tok.$l
-    for f in "${CORPORA[@]}"; do
-        cat $orig/$f.$l | \
-            perl $NORM_PUNC $l | \
-            perl $REM_NON_PRINT_CHAR | \
-            perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l
-    done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
-    if [ "$l" == "$src" ]; then
-        t="src"
-    else
-        t="ref"
-    fi
-    grep '<seg id' $orig/test-full/newstest2014-deen-$t.$l.sgm | \
-        sed -e 's/<seg id="[0-9]*">\s*//g' | \
-        sed -e 's/\s*<\/seg>\s*//g' | \
-        sed -e "s/\’/\'/g" | \
-    perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l
-    echo ""
-done
-
-echo "splitting train and valid..."
-for l in $src $tgt; do
-    awk '{if (NR%100 == 0)  print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l
-    awk '{if (NR%100 != 0)  print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l
-done
-
-TRAIN=$tmp/train.de-en
-BPE_CODE=$prep/code
-rm -f $TRAIN
-for l in $src $tgt; do
-    cat $tmp/train.$l >> $TRAIN
-done
-
-echo "learn_bpe.py on ${TRAIN}..."
-python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE
-
-for L in $src $tgt; do
-    for f in train.$L valid.$L test.$L; do
-        echo "apply_bpe.py to ${f}..."
-        python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f
-    done
-done
-
-perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250
-perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250
-
-for L in $src $tgt; do
-    cp $tmp/bpe.test.$L $prep/test.$L
-done
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh b/spaces/mshukor/UnIVAL/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh
deleted file mode 100644
index e3efeb21d302ef8d9eae8f1d4b06434c593705f6..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh
+++ /dev/null
@@ -1,118 +0,0 @@
-#!/bin/bash
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-
-URLS=(
-    "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
-    "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
-    "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz"
-    "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz"
-    "http://data.statmt.org/wmt17/translation-task/dev.tgz"
-    "http://statmt.org/wmt14/test-full.tgz"
-)
-CORPORA=(
-    "training/europarl-v7.de-en"
-    "commoncrawl.de-en"
-    "training-parallel-nc-v13/news-commentary-v13.de-en"
-    "rapid2016.de-en"
-)
-
-if [ ! -d "$SCRIPTS" ]; then
-    echo "Please set SCRIPTS variable correctly to point to Moses scripts."
-    exit
-fi
-
-src=en
-tgt=de
-lang=en-de
-prep=wmt18_en_de
-tmp=$prep/tmp
-orig=orig
-dev=dev/newstest2012
-codes=32000
-bpe=bpe.32k
-
-mkdir -p $orig $tmp $prep $bpe
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
-    url=${URLS[i]}
-    file=$(basename $url)
-    if [ -f $file ]; then
-        echo "$file already exists, skipping download"
-    else
-        wget "$url"
-        if [ -f $file ]; then
-            echo "$url successfully downloaded."
-        else
-            echo "$url not successfully downloaded."
-            exit 1
-        fi
-        if [ ${file: -4} == ".tgz" ]; then
-            tar zxvf $file
-        elif [ ${file: -4} == ".tar" ]; then
-            tar xvf $file
-        fi
-    fi
-done
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
-    rm  -rf $tmp/train.tags.$lang.tok.$l
-    for f in "${CORPORA[@]}"; do
-        cat $orig/$f.$l | \
-            perl $REM_NON_PRINT_CHAR | \
-            perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/train.tags.$lang.tok.$l
-    done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
-    if [ "$l" == "$src" ]; then
-        t="src"
-    else
-        t="ref"
-    fi
-    grep '<seg id' $orig/test-full/newstest2014-deen-$t.$l.sgm | \
-        sed -e 's/<seg id="[0-9]*">\s*//g' | \
-        sed -e 's/\s*<\/seg>\s*//g' | \
-        sed -e "s/\’/\'/g" | \
-    perl $TOKENIZER -threads 8 -l $l -no-escape > $tmp/test.$l
-    echo ""
-done
-
-# apply length filtering before BPE
-perl $CLEAN -ratio 1.5 $tmp/train.tags.$lang.tok $src $tgt $tmp/train 1 100
-
-# use newstest2012 for valid
-echo "pre-processing valid data..."
-for l in $src $tgt; do
-    rm  -rf $tmp/valid.$l
-    cat $orig/$dev.$l | \
-        perl $REM_NON_PRINT_CHAR | \
-        perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/valid.$l
-done
-
-mkdir output
-mv $tmp/{train,valid,test}.{$src,$tgt} output
-
-#BPE
-git clone https://github.com/glample/fastBPE.git
-pushd fastBPE
-g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast
-popd
-fastBPE/fast learnbpe $codes output/train.$src output/train.$tgt > $bpe/codes
-for split in {train,valid,test}; do for lang in {en,de}; do fastBPE/fast applybpe $bpe/$split.$lang output/$split.$lang $bpe/codes; done; done
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/data/concat_dataset.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/data/concat_dataset.py
deleted file mode 100644
index 01a4078bb159fa44b2d1062b9a971fe7f1abd1c2..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/data/concat_dataset.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import bisect
-
-import numpy as np
-from torch.utils.data.dataloader import default_collate
-
-from . import FairseqDataset
-
-
-class ConcatDataset(FairseqDataset):
-    @staticmethod
-    def cumsum(sequence, sample_ratios):
-        r, s = [], 0
-        for e, ratio in zip(sequence, sample_ratios):
-            curr_len = int(ratio * len(e))
-            r.append(curr_len + s)
-            s += curr_len
-        return r
-
-    def __init__(self, datasets, sample_ratios=1):
-        super(ConcatDataset, self).__init__()
-        assert len(datasets) > 0, "datasets should not be an empty iterable"
-        self.datasets = list(datasets)
-        if isinstance(sample_ratios, int):
-            sample_ratios = [sample_ratios] * len(self.datasets)
-        self.sample_ratios = sample_ratios
-        self.cumulative_sizes = self.cumsum(self.datasets, sample_ratios)
-        self.real_sizes = [len(d) for d in self.datasets]
-
-    def __len__(self):
-        return self.cumulative_sizes[-1]
-
-    def __getitem__(self, idx):
-        dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
-        return self.datasets[dataset_idx][sample_idx]
-
-    def _get_dataset_and_sample_index(self, idx: int):
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
-        if dataset_idx == 0:
-            sample_idx = idx
-        else:
-            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
-        sample_idx = sample_idx % self.real_sizes[dataset_idx]
-        return dataset_idx, sample_idx
-
-    def collater(self, samples, **extra_args):
-        # For now only supports datasets with same underlying collater implementations
-        if hasattr(self.datasets[0], "collater"):
-            return self.datasets[0].collater(samples, **extra_args)
-        else:
-            return default_collate(samples, **extra_args)
-
-    def size(self, idx: int):
-        """
-        Return an example's size as a float or tuple.
-        """
-        dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
-        return self.datasets[dataset_idx].size(sample_idx)
-
-    def num_tokens(self, index: int):
-        return np.max(self.size(index))
-
-    def attr(self, attr: str, index: int):
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, index)
-        return getattr(self.datasets[dataset_idx], attr, None)
-
-    @property
-    def sizes(self):
-        _dataset_sizes = []
-        for ds, sr in zip(self.datasets, self.sample_ratios):
-            if isinstance(ds.sizes, np.ndarray):
-                _dataset_sizes.append(np.tile(ds.sizes, sr))
-            else:
-                # Only support underlying dataset with single size array.
-                assert isinstance(ds.sizes, list)
-                _dataset_sizes.append(np.tile(ds.sizes[0], sr))
-        return np.concatenate(_dataset_sizes)
-
-    @property
-    def supports_prefetch(self):
-        return all(d.supports_prefetch for d in self.datasets)
-
-    def ordered_indices(self):
-        """
-        Returns indices sorted by length. So less padding is needed.
-        """
-        if isinstance(self.sizes, np.ndarray) and len(self.sizes.shape) > 1:
-            # special handling for concatenating lang_pair_datasets
-            indices = np.arange(len(self))
-            sizes = self.sizes
-            tgt_sizes = (
-                sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
-            )
-            src_sizes = (
-                sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
-            )
-            # sort by target length, then source length
-            if tgt_sizes is not None:
-                indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
-            return indices[np.argsort(src_sizes[indices], kind="mergesort")]
-        else:
-            return np.argsort(self.sizes)
-
-    def prefetch(self, indices):
-        frm = 0
-        for to, ds in zip(self.cumulative_sizes, self.datasets):
-            real_size = len(ds)
-            if getattr(ds, "supports_prefetch", False):
-                ds.prefetch([(i - frm) % real_size for i in indices if frm <= i < to])
-            frm = to
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return all(d.can_reuse_epoch_itr_across_epochs for d in self.datasets)
-
-    def set_epoch(self, epoch):
-        super().set_epoch(epoch)
-        for ds in self.datasets:
-            if hasattr(ds, "set_epoch"):
-                ds.set_epoch(epoch)
diff --git a/spaces/mshukor/UnIVAL/models/taming/modules/.ipynb_checkpoints/util-checkpoint.py b/spaces/mshukor/UnIVAL/models/taming/modules/.ipynb_checkpoints/util-checkpoint.py
deleted file mode 100644
index 9ee16385d8b1342a2d60a5f1aa5cadcfbe934bd8..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/models/taming/modules/.ipynb_checkpoints/util-checkpoint.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-def count_params(model):
-    total_params = sum(p.numel() for p in model.parameters())
-    return total_params
-
-
-class ActNorm(nn.Module):
-    def __init__(self, num_features, logdet=False, affine=True,
-                 allow_reverse_init=False):
-        assert affine
-        super().__init__()
-        self.logdet = logdet
-        self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
-        self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
-        self.allow_reverse_init = allow_reverse_init
-
-        self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
-
-    def initialize(self, input):
-        with torch.no_grad():
-            flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
-            mean = (
-                flatten.mean(1)
-                .unsqueeze(1)
-                .unsqueeze(2)
-                .unsqueeze(3)
-                .permute(1, 0, 2, 3)
-            )
-            std = (
-                flatten.std(1)
-                .unsqueeze(1)
-                .unsqueeze(2)
-                .unsqueeze(3)
-                .permute(1, 0, 2, 3)
-            )
-
-            self.loc.data.copy_(-mean)
-            self.scale.data.copy_(1 / (std + 1e-6))
-
-    def forward(self, input, reverse=False):
-        if reverse:
-            return self.reverse(input)
-        if len(input.shape) == 2:
-            input = input[:,:,None,None]
-            squeeze = True
-        else:
-            squeeze = False
-
-        _, _, height, width = input.shape
-
-        if self.training and self.initialized.item() == 0:
-            self.initialize(input)
-            self.initialized.fill_(1)
-
-        h = self.scale * (input + self.loc)
-
-        if squeeze:
-            h = h.squeeze(-1).squeeze(-1)
-
-        if self.logdet:
-            log_abs = torch.log(torch.abs(self.scale))
-            logdet = height*width*torch.sum(log_abs)
-            logdet = logdet * torch.ones(input.shape[0]).to(input)
-            return h, logdet
-
-        return h
-
-    def reverse(self, output):
-        if self.training and self.initialized.item() == 0:
-            if not self.allow_reverse_init:
-                raise RuntimeError(
-                    "Initializing ActNorm in reverse direction is "
-                    "disabled by default. Use allow_reverse_init=True to enable."
-                )
-            else:
-                self.initialize(output)
-                self.initialized.fill_(1)
-
-        if len(output.shape) == 2:
-            output = output[:,:,None,None]
-            squeeze = True
-        else:
-            squeeze = False
-
-        h = output / self.scale - self.loc
-
-        if squeeze:
-            h = h.squeeze(-1).squeeze(-1)
-        return h
-
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-class Labelator(AbstractEncoder):
-    """Net2Net Interface for Class-Conditional Model"""
-    def __init__(self, n_classes, quantize_interface=True):
-        super().__init__()
-        self.n_classes = n_classes
-        self.quantize_interface = quantize_interface
-
-    def encode(self, c):
-        c = c[:,None]
-        if self.quantize_interface:
-            return c, None, [None, None, c.long()]
-        return c
-
-
-class SOSProvider(AbstractEncoder):
-    # for unconditional training
-    def __init__(self, sos_token, quantize_interface=True):
-        super().__init__()
-        self.sos_token = sos_token
-        self.quantize_interface = quantize_interface
-
-    def encode(self, x):
-        # get batch size from data and replicate sos_token
-        c = torch.ones(x.shape[0], 1)*self.sos_token
-        c = c.long().to(x.device)
-        if self.quantize_interface:
-            return c, None, [None, None, c]
-        return c
diff --git a/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/__init__.py b/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/__init__.py
deleted file mode 100644
index da53a4d25419f5de3252af664a7aca5551950f3a..0000000000000000000000000000000000000000
--- a/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-utils/initialization
-"""
-
-
-def notebook_init(verbose=True):
-    # Check system software and hardware
-    print('Checking setup...')
-
-    import os
-    import shutil
-
-    from utils.general import check_requirements, emojis, is_colab
-    from utils.torch_utils import select_device  # imports
-
-    check_requirements(('psutil', 'IPython'))
-    import psutil
-    from IPython import display  # to display images and clear console output
-
-    if is_colab():
-        shutil.rmtree('/content/sample_data', ignore_errors=True)  # remove colab /sample_data directory
-
-    # System info
-    if verbose:
-        gb = 1 << 30  # bytes to GiB (1024 ** 3)
-        ram = psutil.virtual_memory().total
-        total, used, free = shutil.disk_usage("/")
-        display.clear_output()
-        s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)'
-    else:
-        s = ''
-
-    select_device(newline=False)
-    print(emojis(f'Setup complete ✅ {s}'))
-    return display
diff --git a/spaces/nasa-cisto-data-science-group/satvision-base-demo/inference.py b/spaces/nasa-cisto-data-science-group/satvision-base-demo/inference.py
deleted file mode 100644
index 3f89bcde1b0f55129f37e2b43ce9cb731f78656c..0000000000000000000000000000000000000000
--- a/spaces/nasa-cisto-data-science-group/satvision-base-demo/inference.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import numpy as np
-
-import torch
-import joblib
-import numpy as np
-
-import torchvision.transforms as T
-import sys
-
-sys.path.append('pytorch-caney')
-# from pytorch_caney.models.mim.mim import build_mim_model
-
-
-class Transform:
-    """
-    torchvision transform which transforms the input imagery into
-    addition to generating a MiM mask
-    """
-
-    def __init__(self, config):
-
-        self.transform_img = \
-            T.Compose([
-                T.ToTensor(),
-                T.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE)),
-            ])
-
-        model_patch_size = config.MODEL.SWINV2.PATCH_SIZE
-
-        self.mask_generator = SimmimMaskGenerator(
-            input_size=config.DATA.IMG_SIZE,
-            mask_patch_size=config.DATA.MASK_PATCH_SIZE,
-            model_patch_size=model_patch_size,
-            mask_ratio=config.DATA.MASK_RATIO,
-        )
-
-    def __call__(self, img):
-
-        img = self.transform_img(img)
-        mask = self.mask_generator()
-
-        return img, mask
-
-
-class SimmimMaskGenerator:
-    """
-    Generates the masks for masked-image-modeling
-    """
-    def __init__(self,
-                 input_size=192,
-                 mask_patch_size=32,
-                 model_patch_size=4,
-                 mask_ratio=0.6):
-        self.input_size = input_size
-        self.mask_patch_size = mask_patch_size
-        self.model_patch_size = model_patch_size
-        self.mask_ratio = mask_ratio
-
-        assert self.input_size % self.mask_patch_size == 0
-        assert self.mask_patch_size % self.model_patch_size == 0
-
-        self.rand_size = self.input_size // self.mask_patch_size
-        self.scale = self.mask_patch_size // self.model_patch_size
-
-        self.token_count = self.rand_size ** 2
-        self.mask_count = int(np.ceil(self.token_count * self.mask_ratio))
-
-    def __call__(self):
-        mask = self.make_simmim_mask(self.token_count, self.mask_count,
-                                self.rand_size, self.scale)
-        mask = mask.repeat(self.scale, axis=0).repeat(self.scale, axis=1)
-        return mask
-    
-    @staticmethod
-    def make_simmim_mask(token_count, mask_count, rand_size, scale):
-        """JIT-compiled random mask generation
-
-        Args:
-            token_count
-            mask_count
-            rand_size
-            scale
-
-        Returns:
-            mask
-        """
-        mask_idx = np.random.permutation(token_count)[:mask_count]
-        mask = np.zeros(token_count, dtype=np.int64)
-        mask[mask_idx] = 1
-        mask = mask.reshape((rand_size, rand_size))
-        return mask
-
-
-class InferenceModel(object):
-
-    def __init__(self):
-        self.checkpoint_path = 'ckpt_epoch_800.pth'
-        self.config_path = 'simmim_pretrain__satnet_swinv2_base__img192_window12__800ep_v3_no_norm.config.sav'
-        self.architecture_path = 'model.sav'
-
-        self.config = joblib.load(self.config_path)
-        self.model = joblib.load(self.architecture_path)
-        self.load_checkpoint()
-
-        self.transform = Transform(self.config)
-
-
-    def load_checkpoint(self):
-
-
-        checkpoint = torch.load(self.checkpoint_path, map_location='cpu')
-
-        # re-map keys due to name change (only for loading provided models)
-        rpe_mlp_keys = [k for k in checkpoint['model'].keys() if "rpe_mlp" in k]
-
-        for k in rpe_mlp_keys:
-
-            checkpoint['model'][k.replace(
-                'rpe_mlp', 'cpb_mlp')] = checkpoint['model'].pop(k)
-
-        msg = self.model.load_state_dict(checkpoint['model'], strict=False)
-
-        print(msg)
-
-        del checkpoint
-
-        torch.cuda.empty_cache()
-
-    @staticmethod
-    def minmax_norm(img_arr):
-        arr_min = img_arr.min()
-        arr_max = img_arr.max()
-        img_arr_scaled = (img_arr - arr_min) / (arr_max - arr_min)
-        img_arr_scaled = img_arr_scaled * 255
-        img_arr_scaled = img_arr_scaled.astype(np.uint8)
-        return img_arr_scaled
-
-    # -------------------------------------------------------------------------
-    # load_selected_image
-    # -------------------------------------------------------------------------
-    def preprocess(self, image):
-
-        image, mask = self.transform(image)
-
-        image = image.unsqueeze(0)
-    
-        mask = torch.tensor(mask).unsqueeze(0)
-
-        print(image.size())
-        print(mask.shape)
-
-        return image, mask
-
-    # -------------------------------------------------------------------------
-    # load_selected_image
-    # -------------------------------------------------------------------------
-    def predict(self, image, mask):
-
-        with torch.no_grad():
-    
-            logits = self.model.encoder(image, mask)
-    
-            image_recon = self.model.decoder(logits)
-        
-        image_recon = image_recon.numpy()[0, :, :, :]
-
-        return image_recon
-
-    # -------------------------------------------------------------------------
-    # load_selected_image
-    # -------------------------------------------------------------------------
-    @staticmethod
-    def process_mask(mask):
-        mask = mask.repeat_interleave(4, 1).repeat_interleave(4, 2).unsqueeze(1).contiguous()
-        mask = mask[0, 0, :, :]
-        mask = np.stack([mask, mask, mask], axis=-1)
-        return mask
-
-    # -------------------------------------------------------------------------
-    # load_selected_image
-    # -------------------------------------------------------------------------
-    def infer(self, image):
-
-        image, mask = self.preprocess(image)
-
-        img_recon = self.predict(image, mask)
-
-        mask = self.process_mask(mask)
-
-        img_normed = self.minmax_norm(image.numpy()[0, :, :, :])
-
-        print(img_normed.shape)
-        rgb_image = np.stack((img_normed[0, :, :],
-                            img_normed[3, :, :],
-                            img_normed[2, :, :]),
-                            axis=-1)
-
-        img_recon = self.minmax_norm(img_recon)
-        rgb_image_recon = np.stack((img_recon[0, :, :],
-                                    img_recon[3, :, :],
-                                    img_recon[2, :, :]),
-                                    axis=-1)
-
-        rgb_masked = np.where(mask == 0, rgb_image, rgb_image_recon)
-        rgb_image_masked = np.where(mask == 1, 0, rgb_image)
-        rgb_recon_masked = rgb_masked# self.minmax_norm(rgb_masked)
-
-        return rgb_image, rgb_image_masked, rgb_recon_masked
-
-
-def infer(array_input: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-
-    masked_input = np.random.rand(256, 256, 3)
-
-    output = np.random.rand(256, 256, 3)
-
-    return masked_input, output
-
-if __name__ == '__main__':
-    inferenceModel = InferenceModel()
-
-    image = np.load('data/images/sv-demo-mod09ga-11.npy')
-    print(image.shape)
-    image = np.moveaxis(image, 0, 2)
-    print(image.shape)
-
-    inference = inferenceModel.infer(image)
\ No newline at end of file
diff --git a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/data/TrainDataset.py b/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/data/TrainDataset.py
deleted file mode 100644
index 47a639bc644ba7a26e0f2799ffb5f170eed93318..0000000000000000000000000000000000000000
--- a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/data/TrainDataset.py
+++ /dev/null
@@ -1,390 +0,0 @@
-from torch.utils.data import Dataset
-import numpy as np
-import os
-import random
-import torchvision.transforms as transforms
-from PIL import Image, ImageOps
-import cv2
-import torch
-from PIL.ImageFilter import GaussianBlur
-import trimesh
-import logging
-
-log = logging.getLogger('trimesh')
-log.setLevel(40)
-
-def load_trimesh(root_dir):
-    folders = os.listdir(root_dir)
-    meshs = {}
-    for i, f in enumerate(folders):
-        sub_name = f
-        meshs[sub_name] = trimesh.load(os.path.join(root_dir, f, '%s_100k.obj' % sub_name))
-
-    return meshs
-
-def save_samples_truncted_prob(fname, points, prob):
-    '''
-    Save the visualization of sampling to a ply file.
-    Red points represent positive predictions.
-    Green points represent negative predictions.
-    :param fname: File name to save
-    :param points: [N, 3] array of points
-    :param prob: [N, 1] array of predictions in the range [0~1]
-    :return:
-    '''
-    r = (prob > 0.5).reshape([-1, 1]) * 255
-    g = (prob < 0.5).reshape([-1, 1]) * 255
-    b = np.zeros(r.shape)
-
-    to_save = np.concatenate([points, r, g, b], axis=-1)
-    return np.savetxt(fname,
-                      to_save,
-                      fmt='%.6f %.6f %.6f %d %d %d',
-                      comments='',
-                      header=(
-                          'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format(
-                          points.shape[0])
-                      )
-
-
-class TrainDataset(Dataset):
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        return parser
-
-    def __init__(self, opt, phase='train'):
-        self.opt = opt
-        self.projection_mode = 'orthogonal'
-
-        # Path setup
-        self.root = self.opt.dataroot
-        self.RENDER = os.path.join(self.root, 'RENDER')
-        self.MASK = os.path.join(self.root, 'MASK')
-        self.PARAM = os.path.join(self.root, 'PARAM')
-        self.UV_MASK = os.path.join(self.root, 'UV_MASK')
-        self.UV_NORMAL = os.path.join(self.root, 'UV_NORMAL')
-        self.UV_RENDER = os.path.join(self.root, 'UV_RENDER')
-        self.UV_POS = os.path.join(self.root, 'UV_POS')
-        self.OBJ = os.path.join(self.root, 'GEO', 'OBJ')
-
-        self.B_MIN = np.array([-128, -28, -128])
-        self.B_MAX = np.array([128, 228, 128])
-
-        self.is_train = (phase == 'train')
-        self.load_size = self.opt.loadSize
-
-        self.num_views = self.opt.num_views
-
-        self.num_sample_inout = self.opt.num_sample_inout
-        self.num_sample_color = self.opt.num_sample_color
-
-        self.yaw_list = list(range(0,360,1))
-        self.pitch_list = [0]
-        self.subjects = self.get_subjects()
-
-        # PIL to tensor
-        self.to_tensor = transforms.Compose([
-            transforms.Resize(self.load_size),
-            transforms.ToTensor(),
-            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-        ])
-
-        # augmentation
-        self.aug_trans = transforms.Compose([
-            transforms.ColorJitter(brightness=opt.aug_bri, contrast=opt.aug_con, saturation=opt.aug_sat,
-                                   hue=opt.aug_hue)
-        ])
-
-        self.mesh_dic = load_trimesh(self.OBJ)
-
-    def get_subjects(self):
-        all_subjects = os.listdir(self.RENDER)
-        var_subjects = np.loadtxt(os.path.join(self.root, 'val.txt'), dtype=str)
-        if len(var_subjects) == 0:
-            return all_subjects
-
-        if self.is_train:
-            return sorted(list(set(all_subjects) - set(var_subjects)))
-        else:
-            return sorted(list(var_subjects))
-
-    def __len__(self):
-        return len(self.subjects) * len(self.yaw_list) * len(self.pitch_list)
-
-    def get_render(self, subject, num_views, yid=0, pid=0, random_sample=False):
-        '''
-        Return the render data
-        :param subject: subject name
-        :param num_views: how many views to return
-        :param view_id: the first view_id. If None, select a random one.
-        :return:
-            'img': [num_views, C, W, H] images
-            'calib': [num_views, 4, 4] calibration matrix
-            'extrinsic': [num_views, 4, 4] extrinsic matrix
-            'mask': [num_views, 1, W, H] masks
-        '''
-        pitch = self.pitch_list[pid]
-
-        # The ids are an even distribution of num_views around view_id
-        view_ids = [self.yaw_list[(yid + len(self.yaw_list) // num_views * offset) % len(self.yaw_list)]
-                    for offset in range(num_views)]
-        if random_sample:
-            view_ids = np.random.choice(self.yaw_list, num_views, replace=False)
-
-        calib_list = []
-        render_list = []
-        mask_list = []
-        extrinsic_list = []
-
-        for vid in view_ids:
-            param_path = os.path.join(self.PARAM, subject, '%d_%d_%02d.npy' % (vid, pitch, 0))
-            render_path = os.path.join(self.RENDER, subject, '%d_%d_%02d.jpg' % (vid, pitch, 0))
-            mask_path = os.path.join(self.MASK, subject, '%d_%d_%02d.png' % (vid, pitch, 0))
-
-            # loading calibration data
-            param = np.load(param_path, allow_pickle=True)
-            # pixel unit / world unit
-            ortho_ratio = param.item().get('ortho_ratio')
-            # world unit / model unit
-            scale = param.item().get('scale')
-            # camera center world coordinate
-            center = param.item().get('center')
-            # model rotation
-            R = param.item().get('R')
-
-            translate = -np.matmul(R, center).reshape(3, 1)
-            extrinsic = np.concatenate([R, translate], axis=1)
-            extrinsic = np.concatenate([extrinsic, np.array([0, 0, 0, 1]).reshape(1, 4)], 0)
-            # Match camera space to image pixel space
-            scale_intrinsic = np.identity(4)
-            scale_intrinsic[0, 0] = scale / ortho_ratio
-            scale_intrinsic[1, 1] = -scale / ortho_ratio
-            scale_intrinsic[2, 2] = scale / ortho_ratio
-            # Match image pixel space to image uv space
-            uv_intrinsic = np.identity(4)
-            uv_intrinsic[0, 0] = 1.0 / float(self.opt.loadSize // 2)
-            uv_intrinsic[1, 1] = 1.0 / float(self.opt.loadSize // 2)
-            uv_intrinsic[2, 2] = 1.0 / float(self.opt.loadSize // 2)
-            # Transform under image pixel space
-            trans_intrinsic = np.identity(4)
-
-            mask = Image.open(mask_path).convert('L')
-            render = Image.open(render_path).convert('RGB')
-
-            if self.is_train:
-                # Pad images
-                pad_size = int(0.1 * self.load_size)
-                render = ImageOps.expand(render, pad_size, fill=0)
-                mask = ImageOps.expand(mask, pad_size, fill=0)
-
-                w, h = render.size
-                th, tw = self.load_size, self.load_size
-
-                # random flip
-                if self.opt.random_flip and np.random.rand() > 0.5:
-                    scale_intrinsic[0, 0] *= -1
-                    render = transforms.RandomHorizontalFlip(p=1.0)(render)
-                    mask = transforms.RandomHorizontalFlip(p=1.0)(mask)
-
-                # random scale
-                if self.opt.random_scale:
-                    rand_scale = random.uniform(0.9, 1.1)
-                    w = int(rand_scale * w)
-                    h = int(rand_scale * h)
-                    render = render.resize((w, h), Image.BILINEAR)
-                    mask = mask.resize((w, h), Image.NEAREST)
-                    scale_intrinsic *= rand_scale
-                    scale_intrinsic[3, 3] = 1
-
-                # random translate in the pixel space
-                if self.opt.random_trans:
-                    dx = random.randint(-int(round((w - tw) / 10.)),
-                                        int(round((w - tw) / 10.)))
-                    dy = random.randint(-int(round((h - th) / 10.)),
-                                        int(round((h - th) / 10.)))
-                else:
-                    dx = 0
-                    dy = 0
-
-                trans_intrinsic[0, 3] = -dx / float(self.opt.loadSize // 2)
-                trans_intrinsic[1, 3] = -dy / float(self.opt.loadSize // 2)
-
-                x1 = int(round((w - tw) / 2.)) + dx
-                y1 = int(round((h - th) / 2.)) + dy
-
-                render = render.crop((x1, y1, x1 + tw, y1 + th))
-                mask = mask.crop((x1, y1, x1 + tw, y1 + th))
-
-                render = self.aug_trans(render)
-
-                # random blur
-                if self.opt.aug_blur > 0.00001:
-                    blur = GaussianBlur(np.random.uniform(0, self.opt.aug_blur))
-                    render = render.filter(blur)
-
-            intrinsic = np.matmul(trans_intrinsic, np.matmul(uv_intrinsic, scale_intrinsic))
-            calib = torch.Tensor(np.matmul(intrinsic, extrinsic)).float()
-            extrinsic = torch.Tensor(extrinsic).float()
-
-            mask = transforms.Resize(self.load_size)(mask)
-            mask = transforms.ToTensor()(mask).float()
-            mask_list.append(mask)
-
-            render = self.to_tensor(render)
-            render = mask.expand_as(render) * render
-
-            render_list.append(render)
-            calib_list.append(calib)
-            extrinsic_list.append(extrinsic)
-
-        return {
-            'img': torch.stack(render_list, dim=0),
-            'calib': torch.stack(calib_list, dim=0),
-            'extrinsic': torch.stack(extrinsic_list, dim=0),
-            'mask': torch.stack(mask_list, dim=0)
-        }
-
-    def select_sampling_method(self, subject):
-        if not self.is_train:
-            random.seed(1991)
-            np.random.seed(1991)
-            torch.manual_seed(1991)
-        mesh = self.mesh_dic[subject]
-        surface_points, _ = trimesh.sample.sample_surface(mesh, 4 * self.num_sample_inout)
-        sample_points = surface_points + np.random.normal(scale=self.opt.sigma, size=surface_points.shape)
-
-        # add random points within image space
-        length = self.B_MAX - self.B_MIN
-        random_points = np.random.rand(self.num_sample_inout // 4, 3) * length + self.B_MIN
-        sample_points = np.concatenate([sample_points, random_points], 0)
-        np.random.shuffle(sample_points)
-
-        inside = mesh.contains(sample_points)
-        inside_points = sample_points[inside]
-        outside_points = sample_points[np.logical_not(inside)]
-
-        nin = inside_points.shape[0]
-        inside_points = inside_points[
-                        :self.num_sample_inout // 2] if nin > self.num_sample_inout // 2 else inside_points
-        outside_points = outside_points[
-                         :self.num_sample_inout // 2] if nin > self.num_sample_inout // 2 else outside_points[
-                                                                                               :(self.num_sample_inout - nin)]
-
-        samples = np.concatenate([inside_points, outside_points], 0).T
-        labels = np.concatenate([np.ones((1, inside_points.shape[0])), np.zeros((1, outside_points.shape[0]))], 1)
-
-        # save_samples_truncted_prob('out.ply', samples.T, labels.T)
-        # exit()
-
-        samples = torch.Tensor(samples).float()
-        labels = torch.Tensor(labels).float()
-        
-        del mesh
-
-        return {
-            'samples': samples,
-            'labels': labels
-        }
-
-
-    def get_color_sampling(self, subject, yid, pid=0):
-        yaw = self.yaw_list[yid]
-        pitch = self.pitch_list[pid]
-        uv_render_path = os.path.join(self.UV_RENDER, subject, '%d_%d_%02d.jpg' % (yaw, pitch, 0))
-        uv_mask_path = os.path.join(self.UV_MASK, subject, '%02d.png' % (0))
-        uv_pos_path = os.path.join(self.UV_POS, subject, '%02d.exr' % (0))
-        uv_normal_path = os.path.join(self.UV_NORMAL, subject, '%02d.png' % (0))
-
-        # Segmentation mask for the uv render.
-        # [H, W] bool
-        uv_mask = cv2.imread(uv_mask_path)
-        uv_mask = uv_mask[:, :, 0] != 0
-        # UV render. each pixel is the color of the point.
-        # [H, W, 3] 0 ~ 1 float
-        uv_render = cv2.imread(uv_render_path)
-        uv_render = cv2.cvtColor(uv_render, cv2.COLOR_BGR2RGB) / 255.0
-
-        # Normal render. each pixel is the surface normal of the point.
-        # [H, W, 3] -1 ~ 1 float
-        uv_normal = cv2.imread(uv_normal_path)
-        uv_normal = cv2.cvtColor(uv_normal, cv2.COLOR_BGR2RGB) / 255.0
-        uv_normal = 2.0 * uv_normal - 1.0
-        # Position render. each pixel is the xyz coordinates of the point
-        uv_pos = cv2.imread(uv_pos_path, 2 | 4)[:, :, ::-1]
-
-        ### In these few lines we flattern the masks, positions, and normals
-        uv_mask = uv_mask.reshape((-1))
-        uv_pos = uv_pos.reshape((-1, 3))
-        uv_render = uv_render.reshape((-1, 3))
-        uv_normal = uv_normal.reshape((-1, 3))
-
-        surface_points = uv_pos[uv_mask]
-        surface_colors = uv_render[uv_mask]
-        surface_normal = uv_normal[uv_mask]
-
-        if self.num_sample_color:
-            sample_list = random.sample(range(0, surface_points.shape[0] - 1), self.num_sample_color)
-            surface_points = surface_points[sample_list].T
-            surface_colors = surface_colors[sample_list].T
-            surface_normal = surface_normal[sample_list].T
-
-        # Samples are around the true surface with an offset
-        normal = torch.Tensor(surface_normal).float()
-        samples = torch.Tensor(surface_points).float() \
-                  + torch.normal(mean=torch.zeros((1, normal.size(1))), std=self.opt.sigma).expand_as(normal) * normal
-
-        # Normalized to [-1, 1]
-        rgbs_color = 2.0 * torch.Tensor(surface_colors).float() - 1.0
-
-        return {
-            'color_samples': samples,
-            'rgbs': rgbs_color
-        }
-
-    def get_item(self, index):
-        # In case of a missing file or IO error, switch to a random sample instead
-        # try:
-        sid = index % len(self.subjects)
-        tmp = index // len(self.subjects)
-        yid = tmp % len(self.yaw_list)
-        pid = tmp // len(self.yaw_list)
-
-        # name of the subject 'rp_xxxx_xxx'
-        subject = self.subjects[sid]
-        res = {
-            'name': subject,
-            'mesh_path': os.path.join(self.OBJ, subject + '.obj'),
-            'sid': sid,
-            'yid': yid,
-            'pid': pid,
-            'b_min': self.B_MIN,
-            'b_max': self.B_MAX,
-        }
-        render_data = self.get_render(subject, num_views=self.num_views, yid=yid, pid=pid,
-                                        random_sample=self.opt.random_multiview)
-        res.update(render_data)
-
-        if self.opt.num_sample_inout:
-            sample_data = self.select_sampling_method(subject)
-            res.update(sample_data)
-        
-        # img = np.uint8((np.transpose(render_data['img'][0].numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0)
-        # rot = render_data['calib'][0,:3, :3]
-        # trans = render_data['calib'][0,:3, 3:4]
-        # pts = torch.addmm(trans, rot, sample_data['samples'][:, sample_data['labels'][0] > 0.5])  # [3, N]
-        # pts = 0.5 * (pts.numpy().T + 1.0) * render_data['img'].size(2)
-        # for p in pts:
-        #     img = cv2.circle(img, (p[0], p[1]), 2, (0,255,0), -1)
-        # cv2.imshow('test', img)
-        # cv2.waitKey(1)
-
-        if self.num_sample_color:
-            color_data = self.get_color_sampling(subject, yid=yid, pid=pid)
-            res.update(color_data)
-        return res
-        # except Exception as e:
-        #     print(e)
-        #     return self.get_item(index=random.randint(0, self.__len__() - 1))
-
-    def __getitem__(self, index):
-        return self.get_item(index)
\ No newline at end of file
diff --git a/spaces/naughtondale/monochrome/app.py b/spaces/naughtondale/monochrome/app.py
deleted file mode 100644
index a4eb75c308a66e98fccee7877a97cb3ba70a26c2..0000000000000000000000000000000000000000
--- a/spaces/naughtondale/monochrome/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import time
-
-from theme_dropdown import create_theme_dropdown  # noqa: F401
-
-import gradio as gr
-
-dropdown, js = create_theme_dropdown()
-
-with gr.Blocks(theme='gradio/monochrome') as demo:
-    with gr.Row().style(equal_height=True):
-        with gr.Column(scale=10):
-            gr.Markdown(
-                """
-                # Theme preview: `Monochrome`
-                To use this theme, set `theme='gradio/monochrome'` in `gr.Blocks()` or `gr.Interface()`.
-                You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
-                of this theme.
-                """
-            )
-        with gr.Column(scale=3):
-            with gr.Box():
-                dropdown.render()
-                toggle_dark = gr.Button(value="Toggle Dark").style(full_width=True)
-
-    dropdown.change(None, dropdown, None, _js=js)
-    toggle_dark.click(
-        None,
-        _js="""
-        () => {
-            document.body.classList.toggle('dark');
-            document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)'
-        }
-        """,
-    )
-
-    name = gr.Textbox(
-        label="Name",
-        info="Full name, including middle name. No special characters.",
-        placeholder="John Doe",
-        value="John Doe",
-        interactive=True,
-    )
-
-    with gr.Row():
-        slider1 = gr.Slider(label="Slider 1")
-        slider2 = gr.Slider(label="Slider 2")
-    gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group")
-
-    with gr.Row():
-        with gr.Column(variant="panel", scale=1):
-            gr.Markdown("## Panel 1")
-            radio = gr.Radio(
-                ["A", "B", "C"],
-                label="Radio",
-                info="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.",
-            )
-            drop = gr.Dropdown(["Option 1", "Option 2", "Option 3"], show_label=False)
-            drop_2 = gr.Dropdown(
-                ["Option A", "Option B", "Option C"],
-                multiselect=True,
-                value=["Option A"],
-                label="Dropdown",
-                interactive=True,
-            )
-            check = gr.Checkbox(label="Go")
-        with gr.Column(variant="panel", scale=2):
-            img = gr.Image(
-                "https://gradio.app/assets/img/header-image.jpg", label="Image"
-            ).style(height=320)
-            with gr.Row():
-                go_btn = gr.Button("Go", label="Primary Button", variant="primary")
-                clear_btn = gr.Button(
-                    "Clear", label="Secondary Button", variant="secondary"
-                )
-
-                def go(*args):
-                    time.sleep(3)
-                    return "https://gradio.app/assets/img/header-image.jpg"
-
-                go_btn.click(go, [radio, drop, drop_2, check, name], img, api_name="go")
-
-                def clear():
-                    time.sleep(0.2)
-                    return None
-
-                clear_btn.click(clear, None, img)
-
-            with gr.Row():
-                btn1 = gr.Button("Button 1").style(size="sm")
-                btn2 = gr.UploadButton().style(size="sm")
-                stop_btn = gr.Button("Stop", label="Stop Button", variant="stop").style(
-                    size="sm"
-                )
-
-    with gr.Row():
-        gr.Dataframe(value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], label="Dataframe")
-        gr.JSON(
-            value={"a": 1, "b": 2, "c": {"test": "a", "test2": [1, 2, 3]}}, label="JSON"
-        )
-        gr.Label(value={"cat": 0.7, "dog": 0.2, "fish": 0.1})
-        gr.File()
-    with gr.Row():
-        gr.ColorPicker()
-        gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4")
-        gr.Gallery(
-            [
-                (
-                    "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg",
-                    "lion",
-                ),
-                (
-                    "https://gradio-static-files.s3.us-west-2.amazonaws.com/logo.png",
-                    "logo",
-                ),
-                (
-                    "https://gradio-static-files.s3.us-west-2.amazonaws.com/tower.jpg",
-                    "tower",
-                ),
-            ]
-        ).style(height="200px", grid=2)
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot([("Hello", "Hi")], label="Chatbot")
-            chat_btn = gr.Button("Add messages")
-
-            def chat(history):
-                time.sleep(2)
-                yield [["How are you?", "I am good."]]
-
-            chat_btn.click(
-                lambda history: history
-                + [["How are you?", "I am good."]]
-                + (time.sleep(2) or []),
-                chatbot,
-                chatbot,
-            )
-        with gr.Column(scale=1):
-            with gr.Accordion("Advanced Settings"):
-                gr.Markdown("Hello")
-                gr.Number(label="Chatbot control 1")
-                gr.Number(label="Chatbot control 2")
-                gr.Number(label="Chatbot control 3")
-
-
-if __name__ == "__main__":
-    demo.queue().launch()
diff --git a/spaces/nazneen/interactive-model-cards/app.py b/spaces/nazneen/interactive-model-cards/app.py
deleted file mode 100644
index 146ab0cc5bad64bdf8a60d73d6f5dd9abbaa6928..0000000000000000000000000000000000000000
--- a/spaces/nazneen/interactive-model-cards/app.py
+++ /dev/null
@@ -1,182 +0,0 @@
-### LIBRARIES ###
-# # Data
-import numpy as np
-import pandas as pd
-import json
-from math import floor
-
-# Robustness Gym and Analysis
-import robustnessgym as rg
-from gensim.models.doc2vec import Doc2Vec
-from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
-import nltk
-nltk.download('punkt') #make sure that punkt is downloaded
-
-# App & Visualization
-import streamlit as st
-import altair as alt
-
-# utils
-from interactive_model_cards import utils as ut
-from interactive_model_cards import app_layout as al
-from random import sample
-from PIL import Image
-
-
-
-### LOADING DATA ###
-# model card data
-@st.experimental_memo
-def load_model_card():
-    with open("./assets/data/text_explainer/model_card.json") as f:
-        mc_text = json.load(f)
-    return mc_text
-
-
-# pre-computed robusntess gym dev bench
-# @st.experimental_singleton
-@st.cache(allow_output_mutation=True)
-def load_data():
-    # load dev bench
-    devBench = rg.DevBench.load("./assets/data/rg/sst_db.devbench")
-    return devBench
-
-
-# load model
-@st.experimental_singleton
-def load_model():
-    model = rg.HuggingfaceModel(
-        "distilbert-base-uncased-finetuned-sst-2-english", is_classifier=True
-    )
-    return model
-
-#load pre-computed embedding
-def load_embedding():
-    embedding = pd.read_pickle("./assets/models/sst_vectors.pkl")
-    return embedding
-
-#load doc2vec model
-@st.experimental_singleton
-def load_doc2vec():
-    doc2vec = Doc2Vec.load("./assets/models/sst_train.doc2vec")
-    return(doc2vec)  
-    
-
-# @st.experimental_memo
-def load_examples():
-    with open("./assets/data/user_data/example_sentence.json") as f:
-        examples = json.load(f)
-    return examples
-
-
-# loading the dataset
-def load_basic():
-    # load data
-    devBench = load_data()
-    # load model
-    model = load_model()
-    #protected_classes 
-    protected_classes = json.load(open("./assets/data/protected_terms.json"))
-    
-    return devBench, model, protected_classes
-
-@st.experimental_singleton
-def load_title():
-    img = Image.open("./assets/img/title.png")
-    return(img)
-
-
-if __name__ == "__main__":
-
-    ### STREAMLIT APP CONGFIG ###
-    st.set_page_config(layout="wide", page_title="Interactive Model Card")
-
-    # import custom styling
-    ut.init_style()
-
-    ### LOAD DATA AND SESSION VARIABLES ###
-
-    # ******* loading the mode and the data
-    with st.spinner():
-        sst_db, model,protected_classes = load_basic()
-        embedding = load_embedding()
-        doc2vec = load_doc2vec()
-
-    # load example sentences
-    sentence_examples = load_examples()
-
-    # ******* session state variables
-    if "user_data" not in st.session_state:
-        st.session_state["user_data"] = pd.DataFrame()
-    if "example_sent" not in st.session_state:
-        st.session_state["example_sent"] = "I like you. I love you"
-    if "quant_ex" not in st.session_state:
-        st.session_state["quant_ex"] = {"Overall Performance": sst_db.metrics["model"]}
-    if "selected_slice" not in st.session_state:
-        st.session_state["selected_slice"] = None
-    if "slice_terms" not in st.session_state:
-        st.session_state["slice_terms"] = {}
-    if "embedding" not in st.session_state:
-        st.session_state["embedding"] = embedding
-    if "protected_class" not in st.session_state:
-        st.session_state["protected_class"] = protected_classes
-
-
-    ### STREAMLIT APP LAYOUT###
-
-    # ******* MODEL CARD PANEL *******
-    #st.sidebar.title("Interactive Model Card")
-    img = load_title()
-    st.sidebar.image(img,width=400)
-    st.sidebar.warning("Data is not permanently collected or stored from your interactions, but is temporarily cached during usage.")
-
-    # load model card data
-    errors = st.sidebar.checkbox("Show Warnings", value=True)
-    model_card = load_model_card()
-    al.model_card_panel(model_card,errors)
-
-    lcol, rcol = st.columns([4, 8])
-
-    # ******* USER EXAMPLE DATA PANEL *******
-    st.markdown("---")
-    with lcol:
-
-        # Choose waht to show for the qunatiative analysis.
-        st.write("""<h1 style="font-size:20px;padding-top:0px;"> Quantitative Analysis</h1>""",
-                unsafe_allow_html=True)
-        
-        st.markdown("View the model's performance or visually explore the model's training and testing dataset")
-
-        data_view = st.selectbox("Show:",
-            ["Model Performance Metrics","Data Subpopulation Comparison Visualization"])
-            
-        st.markdown("Any groups you define via the *analysis actions* will be automatically added to the view")
-        st.markdown("---")
-
-        
-        # Additional Analysis Actions
-        st.write(
-            """<h1 style="font-size:18px;padding-top:5px;"> Analysis Actions</h1>""",
-            unsafe_allow_html=True,
-        )
-        al.example_panel(sentence_examples, model, sst_db,doc2vec)
-
-    # ****** GUIDANCE PANEL *****
-        with st.expander("Guidance"):
-            st.markdown(
-                "Need help understanding what you're seeing in this model card?"
-            )
-
-            st.markdown(
-                " * **[Understanding Metrics](https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks)**: A cheatsheet of model metrics"
-            )
-            st.markdown(
-                " * **[Understanding Sentiment Models](https://www.semanticscholar.org/topic/Sentiment-analysis/6011)**: An overview of sentiment analysis"
-            )
-            st.markdown(
-                "* **[Next Steps](https://docs.google.com/document/d/1r9J1NQ7eTibpXkCpcucDEPhASGbOQAMhRTBvosGu4Pk/edit?usp=sharin)**: Suggestions for follow-on actions"
-            )
-            st.markdown("Feel free to submit feedback via our [online form](https://sfdc.co/imc_feedback)")
-    
-    # ******* QUANTITATIVE DATA PANEL *******
-    al.quant_panel(sst_db, st.session_state["embedding"], rcol,data_view)
diff --git a/spaces/neongeckocom/neon-tts-plugin-coqui/app.py b/spaces/neongeckocom/neon-tts-plugin-coqui/app.py
deleted file mode 100644
index 1a4fe4ee1053fd513ed6bcf991665793659e7a41..0000000000000000000000000000000000000000
--- a/spaces/neongeckocom/neon-tts-plugin-coqui/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import tempfile
-
-import gradio as gr
-
-from neon_tts_plugin_coqui import CoquiTTS
-
-
-LANGUAGES = list(CoquiTTS.langs.keys())
-default_lang = "en"
-
-
-
-title = "🐸💬 - Neon AI Coqui AI TTS Plugin"
-description = "🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production"
-info = "more info at [Neon Coqui TTS Plugin](https://github.com/NeonGeckoCom/neon-tts-plugin-coqui), [Coqui TTS](https://github.com/coqui-ai/TTS)"
-badge = "https://visitor-badge-reloaded.herokuapp.com/badge?page_id=neongeckocom.neon-tts-plugin-coqui"
-
-
-
-coquiTTS = CoquiTTS()
-
-
-def tts(text: str, language: str):
-    print(text, language)
-    # return output
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        coquiTTS.get_tts(text, fp, speaker = {"language" : language})
-        return fp.name
-
-
-
-with gr.Blocks() as blocks:
-    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>"
-                + title
-                + "</h1>")
-    gr.Markdown(description)
-    with gr.Row():# equal_height=False
-        with gr.Column():# variant="panel"
-            textbox = gr.Textbox(
-                label="Input",
-                value=CoquiTTS.langs[default_lang]["sentence"],
-                max_lines=3,
-            )
-            radio = gr.Radio(
-                label="Language",
-                choices=LANGUAGES,
-                value=default_lang
-            )
-            with gr.Row():# mobile_collapse=False
-                submit = gr.Button("Submit", variant="primary")
-        audio = gr.Audio(label="Output", interactive=False)
-    gr.Markdown(info)
-    gr.Markdown("<center>"
-                +f'<img src={badge} alt="visitors badge"/>'
-                +"</center>")
-
-    # actions
-    submit.click(
-        tts,
-        [textbox, radio],
-        [audio],
-    )
-    radio.change(lambda lang: CoquiTTS.langs[lang]["sentence"], radio, textbox)
-
-
-
-blocks.launch()
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe Creative Suite Master Collection 6.0 LS4 ESD MACOSX-ISO.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe Creative Suite Master Collection 6.0 LS4 ESD MACOSX-ISO.md
deleted file mode 100644
index 94035d6f71e144ea0ea05c90919153fe8187abba..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Adobe Creative Suite Master Collection 6.0 LS4 ESD MACOSX-ISO.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>How to Install Adobe Creative Suite 6 Master Collection on Mac OS X</h1>
-<p>Adobe Creative Suite 6 Master Collection is a comprehensive package of professional design tools that includes Photoshop, Illustrator, InDesign, Acrobat, Flash, Dreamweaver, Premiere Pro, After Effects, and more. It offers blazing-fast performance with 64-bit native support and GPU acceleration, as well as groundbreaking new creative features that let you design for the latest devices and platforms.</p>
-<h2>Adobe Creative Suite Master Collection 6.0 LS4 ESD MACOSX-ISO</h2><br /><p><b><b>Download Zip</b> &gt; <a href="https://urlcod.com/2uIas9">https://urlcod.com/2uIas9</a></b></p><br /><br />
-<p>If you want to install Adobe Creative Suite 6 Master Collection on your Mac OS X computer, you will need to download the ISO file from a reliable source. The ISO file is a disk image that contains all the installation files and instructions. You will also need a valid serial number to activate the software.</p>
-<p>Here are the steps to install Adobe Creative Suite 6 Master Collection on Mac OS X:</p>
-<ol>
-<li>Download the ISO file from a trusted source. The file name should be something like "Adobe.Creative.Suite.6.0.Master.Collection.LS4.ESD.MACOSX-ISO.iso". The file size should be around 7.65 GB[^1^]. You can use a torrent client or a direct download link to get the file.</li>
-<li>Mount the ISO file on your Mac by double-clicking it. This will create a virtual drive on your desktop with the name "Adobe CS6 Master Collection".</li>
-<li>Open the virtual drive and double-click the "Install" icon. This will launch the Adobe Application Manager.</li>
-<li>Follow the on-screen instructions to install Adobe Creative Suite 6 Master Collection. You will need to enter your serial number when prompted. You can also choose which components you want to install and customize your preferences.</li>
-<li>Disconnect from the internet before you start the installation. This will prevent the software from checking for updates or validating your serial number online.</li>
-<li>Install the Application Manager 6.2 update after you finish the installation. You can download it from <a href="https://www.adobe.com/support/downloads/detail.jsp?ftpID=4773">here</a> for Mac OS X[^2^]. This will fix some bugs and improve the stability of the software.</li>
-<li>Install the latest updates for each component of Adobe Creative Suite 6 Master Collection. You can download them from <a href="https://www.adobe.com/downloads/updates/">here</a> for Mac OS X[^2^]. This will ensure that you have the most recent features and security patches.</li>
-<li>Enjoy your Adobe Creative Suite 6 Master Collection on your Mac OS X computer!</li>
-</ol>
-<p>Note: This article is for informational purposes only and does not provide any warranty or guarantee for the software or its installation. Please use it at your own risk and discretion.</p>
-
-<p>Now that you have installed Adobe Creative Suite 6 Master Collection on your Mac OS X computer, you can start exploring its amazing features and capabilities. Here are some tips and tricks to help you get the most out of your software:</p>
-<p></p>
-<ul>
-<li>Learn how to use the new Content-Aware tools in Photoshop CS6 Extended. These tools let you remove or move selected image objects with ease and precision, and automatically fill the gaps with matching content. You can access them from the Edit menu or the Tools panel.</li>
-<li>Experiment with the new Pattern Creation feature in Illustrator CS6. This feature lets you create seamlessly tiled vector patterns with different types of repeats and transformations. You can access it from the Object menu or the Control panel.</li>
-<li>Create stunning digital publications with InDesign CS6. You can use the new Liquid Layout and Alternate Layout features to adapt your layouts to different devices and orientations. You can also use the new Folio Builder panel to create and preview interactive content for tablets and smartphones.</li>
-<li>Edit and enhance your videos with Premiere Pro CS6 and After Effects CS6. You can use the new Multicam Editing feature to edit footage from multiple cameras with ease and accuracy. You can also use the new Warp Stabilizer feature to remove unwanted motion from your clips.</li>
-<li>Build engaging web and mobile applications with Flash Professional CS6 and Flash Builder 4.6 Premium Edition. You can use the new Sprite Sheet Generation feature to create optimized graphics for games and animations. You can also use the new Mobile Device Simulator to test your applications on various devices and platforms.</li>
-</ul>
-<p>These are just some of the many features that Adobe Creative Suite 6 Master Collection offers. You can find more information and tutorials on the official Adobe website or on various online resources. You can also join the Adobe community and share your work and ideas with other creative professionals.</p>
-<p>We hope you enjoy using Adobe Creative Suite 6 Master Collection on your Mac OS X computer!</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Candy Crush Wont Connect To Facebook On Ipod Touch 4.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Candy Crush Wont Connect To Facebook On Ipod Touch 4.md
deleted file mode 100644
index f45214f431f33989d46ad0a061522bf3817ea0d1..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Candy Crush Wont Connect To Facebook On Ipod Touch 4.md	
+++ /dev/null
@@ -1,44 +0,0 @@
-<br />
-<h1>How to Fix Candy Crush Saga Not Connecting to Facebook on iPod Touch 4</h1>
-<p>If you are a fan of Candy Crush Saga, you might have encountered a frustrating problem: the game won't connect to Facebook on your iPod Touch 4. This means you can't sync your progress, compete with your friends, or access extra features. But don't worry, there are some easy methods you can try to reconnect Candy Crush Saga to Facebook on your iPod Touch 4.</p>
-<h2>Candy Crush Wont Connect To Facebook On Ipod Touch 4</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733; <a href="https://urlcod.com/2uIauc">https://urlcod.com/2uIauc</a></b></p><br /><br />
-<p>In this article, we will show you how to fix Candy Crush Saga not connecting to Facebook on iPod Touch 4 by reinstalling the app, fixing permissions problems, clearing the cache, and other fixes. Follow these steps and you will be able to enjoy Candy Crush Saga with Facebook again.</p>
-
-<h2>Method 1: Reinstalling the App</h2>
-<p>One of the simplest ways to fix Candy Crush Saga not connecting to Facebook on iPod Touch 4 is to uninstall and reinstall the app. This will refresh the app and remove any corrupted data that might be causing the problem. Here's how to do it:</p>
-<ol>
-<li>Uninstall Candy Crush Saga from your iPod Touch 4. Tap and hold the app's icon until it wiggles, then tap the X button and confirm.</li>
-<li>Download and reinstall Candy Crush Saga from the App Store. Search for the game and tap the Get button.</li>
-<li>Open Candy Crush Saga and tap Retrieve My Progress. This will connect the game to your Facebook account and restore your saved progress. If prompted, enter your Facebook login information and password.</li>
-<li>Tap X to close the window and start playing.</li>
-</ol>
-<p>Note: As long as you have connected your Facebook account to Candy Crush Saga in the past, your progress will be safe. However, you may lose any boosters you have earned.</p>
-
-<h2>Method 2: Fixing Permissions Problems</h2>
-<p>Another possible reason why Candy Crush Saga won't connect to Facebook on your iPod Touch 4 is that the app doesn't have permission to access your Facebook account. You can fix this by renewing the permission in the Facebook app. Here's how to do it:</p>
-<ol>
-<li>Open the Facebook app on your iPod Touch 4 and tap the menu icon (three horizontal lines) at the bottom right corner.</li>
-<li>Scroll down and tap Settings & Privacy, then tap Settings.</li>
-<li>Scroll down and tap Apps and Websites, then tap Logged in with Facebook.</li>
-<li>Find Candy Crush Saga in the list and tap it.</li>
-<li>Make sure that Active is selected and that Access your name and profile picture is checked.</li>
-<li>If you see a message that says Expired or Removed under Candy Crush Saga, tap Renew Access and confirm.</li>
-<li>Go back to Candy Crush Saga and try connecting to Facebook again.</li>
-</ol>
-
-<h2>Method 3: Clearing the Cache</h2>
-<p>Sometimes, Candy Crush Saga won't connect to Facebook on your iPod Touch 4 because of a cache issue. The cache is a temporary storage of data that helps the app run faster, but it can also cause problems if it becomes outdated or corrupted. You can clear the cache by deleting or offloading the app on your iPod Touch 4. Here's how to do it:</p>
-<p></p>
-<ol>
-<li>Go to Settings on your iPod Touch 4 and tap General.</li>
-<li>Tap iPhone Storage (or iPad Storage) and find Candy Crush Saga in the list of apps.</li>
-<li>Tap Delete App or Offload App. Deleting the app will remove it completely from your device, while offloading it will keep its data and documents.</li>
-<li>If you deleted the app, download and reinstall it from the App Store. If you offloaded it, tap Reinstall App on the same screen.</li>
-<li>Open Candy Crush Saga and tap Retrieve My Progress or Save My Progress. This will connect the game to your Facebook account and restore your saved progress. If prompted, enter your Facebook login information and password.</li>
-<li>Tap X to close the window and start playing.</li>
-</ol>
-
-<h2>Other Fixes</h2>
-<p>If none of the above methods work for you, here are some other fixes you can try to fix Candy Crush Saga not connecting to Facebook on your iPod Touch 4:</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Pokemon Y Strategy Guide Pdf Download [UPD]l.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Pokemon Y Strategy Guide Pdf Download [UPD]l.md
deleted file mode 100644
index 899793a0ca6cc753ad753dd9eb069b237ee09ffc..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Pokemon Y Strategy Guide Pdf Download [UPD]l.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-<br />
-<h1>How to Download Pokemon Y Strategy Guide Pdf for Free</h1>
-<p>If you are looking for a comprehensive and easy-to-follow guide to master Pokemon Y, you might be interested in downloading the Pokemon Y Strategy Guide Pdf. This guide covers everything you need to know about the game, from the basics to the advanced tips and tricks. You will learn how to catch and train your Pokemon, how to explore the Kalos region, how to battle and trade with other players, how to complete the Pokedex, and much more.</p>
-<h2>Pokemon Y Strategy Guide Pdf Downloadl</h2><br /><p><b><b>Download File</b> &#10004;&#10004;&#10004; <a href="https://urlcod.com/2uIatD">https://urlcod.com/2uIatD</a></b></p><br /><br />
-<p>But where can you find the Pokemon Y Strategy Guide Pdf for free? There are many websites that claim to offer this guide, but some of them might be scams or viruses. You don't want to risk your device or your personal information by downloading from unreliable sources. That's why we have done the research for you and found the best and safest way to download the Pokemon Y Strategy Guide Pdf for free.</p>
-<h2>The Best Way to Download Pokemon Y Strategy Guide Pdf for Free</h2>
-<p>The best way to download the Pokemon Y Strategy Guide Pdf for free is to use a reputable and trusted website that offers legal and high-quality downloads. One of the best websites that we recommend is <a href="https://www.pokemonystrategyguidepdf.com">www.pokemonystrategyguidepdf.com</a>. This website has a huge collection of Pokemon guides, including the Pokemon Y Strategy Guide Pdf. You can download it in just a few minutes by following these simple steps:</p>
-<ol>
-<li>Go to <a href="https://www.pokemonystrategyguidepdf.com">www.pokemonystrategyguidepdf.com</a> and search for "Pokemon Y Strategy Guide Pdf" in the search box.</li>
-<li>Click on the link that says "Download Pokemon Y Strategy Guide Pdf" and you will be redirected to a secure download page.</li>
-<li>Enter your email address and click on "Download Now". You will receive a confirmation email with a download link.</li>
-<li>Click on the download link and save the file to your device. You can open it with any PDF reader or print it out if you prefer.</li>
-</ol>
-<p>That's it! You have successfully downloaded the Pokemon Y Strategy Guide Pdf for free. Now you can enjoy playing Pokemon Y with the help of this guide. You will be able to catch them all and become the best trainer in Kalos.</p>
-<p></p>
-<h2>Why You Should Download Pokemon Y Strategy Guide Pdf for Free</h2>
-<p>There are many benefits of downloading the Pokemon Y Strategy Guide Pdf for free from <a href="https://www.pokemonystrategyguidepdf.com">www.pokemonystrategyguidepdf.com</a>. Here are some of them:</p>
-<ul>
-<li>You will save money. The official Pokemon Y Strategy Guide costs around $20, but you can get it for free from this website.</li>
-<li>You will save time. You don't have to waste time searching for other websites that might not work or might harm your device. This website is fast, safe, and reliable.</li>
-<li>You will get the best quality. The Pokemon Y Strategy Guide Pdf that you download from this website is the same as the official one. It has clear images, accurate information, and easy-to-follow instructions.</li>
-<li>You will get updates. The Pokemon Y Strategy Guide Pdf that you download from this website is always updated with the latest changes and additions to the game. You will never miss out on any new features or secrets.</li>
-</ul>
-<p>So what are you waiting for? Download the Pokemon Y Strategy Guide Pdf for free from <a href="https://www.pokemonystrategyguidepdf.com">www.pokemonystrategyguidepdf.com</a> today and start your adventure in Kalos!</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Tamil Movies Vijayakanth Movie Narasimha Downloaddcinstl ((FULL)).md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Tamil Movies Vijayakanth Movie Narasimha Downloaddcinstl ((FULL)).md
deleted file mode 100644
index f7523338cd78bf4ccec175ae703823c75af8abb3..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Tamil Movies Vijayakanth Movie Narasimha Downloaddcinstl ((FULL)).md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-<h1>How to Download Narasimha, the Tamil Action Movie Starring Vijayakanth</h1>
-<p>Narasimha is a 2001 Tamil action movie directed by Thirupathisamy and starring Vijayakanth in the lead role. The movie also features an ensemble supporting cast including Isha Koppikar, Raghuvaran, Nassar, Anandaraj, Ranjith, Rahul Dev, and Vadivelu. The movie has a thrilling plot that revolves around a police officer who fights against a corrupt politician and his henchmen. The movie was a commercial success and received positive reviews from critics and audiences alike.</p>
-<p>If you are a fan of Tamil action movies and Vijayakanth, you might be wondering how to download Narasimha and watch it online. Well, you have come to the right place. In this article, we will show you some of the best ways to download Narasimha legally and safely.</p>
-<h2>Tamil Movies Vijayakanth Movie Narasimha Downloaddcinstl</h2><br /><p><b><b>Download File</b> &#10040;&#10040;&#10040; <a href="https://urlcod.com/2uIaXU">https://urlcod.com/2uIaXU</a></b></p><br /><br />
-<h2>Method 1: Use a Streaming Service</h2>
-<p>One of the easiest and most convenient ways to watch Narasimha online is to use a streaming service that offers the movie. Streaming services allow you to watch movies and shows on demand without having to download them. You can also access them from any device that has an internet connection, such as your smartphone, tablet, laptop, or smart TV.</p>
-<p>Some of the popular streaming services that offer Narasimha are:</p>
-<ul>
-<li><strong>Amazon Prime Video</strong>: Amazon Prime Video is a subscription-based service that offers a wide range of movies and shows from various genres and languages. You can watch Narasimha on Amazon Prime Video with a monthly or annual subscription. You can also download the movie for offline viewing on your device.</li>
-<li><strong>Hotstar</strong>: Hotstar is another subscription-based service that offers a large collection of movies and shows from India and abroad. You can watch Narasimha on Hotstar with a VIP or Premium subscription. You can also download the movie for offline viewing on your device.</li>
-<li><strong>YouTube</strong>: YouTube is a free video-sharing platform that hosts millions of videos from various categories and creators. You can watch Narasimha on YouTube for free with ads. You can also rent or buy the movie for ad-free viewing on YouTube.</li>
-</ul>
-<h2>Method 2: Use a Torrent Site</h2>
-<p>Another way to download Narasimha is to use a torrent site. Torrent sites are websites that allow users to share files over a peer-to-peer network. You can download movies, music, games, software, and more from torrent sites using a torrent client software.</p>
-<p>However, using torrent sites comes with some risks and drawbacks. First of all, downloading copyrighted content from torrent sites is illegal and can get you into trouble with the law. Secondly, torrent sites are often unsafe and may contain malware, viruses, or spyware that can harm your device or compromise your privacy. Thirdly, torrent sites may not have high-quality or complete files of the movie you want to download.</p>
-<p>Therefore, we do not recommend using torrent sites to download Narasimha or any other movie. However, if you still want to use torrent sites at your own risk, here are some of the popular ones:</p>
-<ul>
-<li><strong>The Pirate Bay</strong>: The Pirate Bay is one of the oldest and most popular torrent sites in the world. It has millions of torrents from various categories and languages. You can search for Narasimha on The Pirate Bay and download it using a torrent client software.</li>
-<li><strong>Kickass Torrents</strong>: Kickass Torrents is another well-known torrent site that offers a large variety of torrents from different sources and genres. You can search for Narasimha on Kickass Torrents and download it using a torrent client software.</li>
-<li><strong>TamilRockers</strong>: TamilRockers is a notorious torrent site that specializes in leaking Tamil movies online. It has a huge collection of Tamil movies from old to new releases. You can search for Narasimha on TamilRockers and download it using a torrent client software.</li>
-</ul>
-<h2>Conclusion</h2>
-<p</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ngxson/poet-cat/frontend/components/ChatInput.tsx b/spaces/ngxson/poet-cat/frontend/components/ChatInput.tsx
deleted file mode 100644
index 9dfb2c3066278164f10818a1718cb4ccd754d42d..0000000000000000000000000000000000000000
--- a/spaces/ngxson/poet-cat/frontend/components/ChatInput.tsx
+++ /dev/null
@@ -1,31 +0,0 @@
-export default function ChatInput({
-  disabled = false,
-  text = '',
-  onTextChanged = (t: string) => { },
-  onSend = () => { },
-  errorText = '',
-}) {
-  const handleSubmit = (event: any) => {
-    event.preventDefault();
-    onSend();
-  }
-
-  return <>
-    <form onSubmit={handleSubmit}>
-      {errorText && <p className="error">
-        Error: {errorText}
-      </p>}
-      <div className="input-group">
-
-        <input type="text" className="form-control" placeholder={
-          disabled ? "Cat is typing..." : "Your message..."
-        } disabled={disabled} onChange={e => onTextChanged(e.target.value)} value={text} />
-
-        <span className="input-group-btn">
-          <button className="btn btn-primary" type="button" disabled={disabled} onClick={onSend}>Send</button>
-        </span>
-
-      </div>
-    </form>
-  </>;
-}
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/meta_arch/__init__.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/meta_arch/__init__.py
deleted file mode 100644
index 6b0668157052ce7b796ef50bc7ee85361e7605b9..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/meta_arch/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .build import META_ARCH_REGISTRY, build_model  # isort:skip
-
-from .panoptic_fpn import PanopticFPN
-
-# import all the meta_arch, so they will be registered
-from .rcnn import GeneralizedRCNN, ProposalNetwork
-from .dense_detector import DenseDetector
-from .retinanet import RetinaNet
-from .fcos import FCOS
-from .semantic_seg import SEM_SEG_HEADS_REGISTRY, SemanticSegmentor, build_sem_seg_head
-
-
-__all__ = list(globals().keys())
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/LVIS/mask_rcnn_vitdet_h_100ep.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/LVIS/mask_rcnn_vitdet_h_100ep.py
deleted file mode 100644
index 0f99bad24e6702e91abe226446e7d7b00ef14df2..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/configs/LVIS/mask_rcnn_vitdet_h_100ep.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from functools import partial
-
-from detectron2.modeling.backbone.vit import get_vit_lr_decay_rate
-
-from .mask_rcnn_vitdet_b_100ep import (
-    dataloader,
-    lr_multiplier,
-    model,
-    train,
-    optimizer,
-)
-
-train.init_checkpoint = (
-    "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_huge_p14to16.pth?matching_heuristics=True"
-)
-
-model.backbone.net.embed_dim = 1280
-model.backbone.net.depth = 32
-model.backbone.net.num_heads = 16
-model.backbone.net.drop_path_rate = 0.4
-# 7, 15, 23, 31 for global attention
-model.backbone.net.window_block_indexes = (
-    list(range(0, 7)) + list(range(8, 15)) + list(range(16, 23)) + list(range(24, 31))
-)
-
-
-optimizer.lr = 1e-4
-optimizer.params.lr_factor_func = partial(get_vit_lr_decay_rate, lr_decay_rate=0.9, num_layers=32)
-optimizer.params.overrides = {}
-optimizer.params.weight_decay_norm = None
diff --git a/spaces/nlpconnect/live-wikipedia-dpr/app.py b/spaces/nlpconnect/live-wikipedia-dpr/app.py
deleted file mode 100644
index baa916b7afaa433ec074b9be87b22b7700060576..0000000000000000000000000000000000000000
--- a/spaces/nlpconnect/live-wikipedia-dpr/app.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import spacy
-import wikipedia
-from wikipedia.exceptions import DisambiguationError
-from transformers import TFAutoModel, AutoTokenizer
-import numpy as np
-import pandas as pd
-import faiss
-
-try:
-  nlp = spacy.load("en_core_web_sm")
-except:
-  spacy.cli.download("en_core_web_sm")
-  nlp = spacy.load("en_core_web_sm")
-
-wh_words = ['what', 'who', 'how', 'when', 'which']
-def get_concepts(text):
-  text = text.lower()
-  doc = nlp(text)
-  concepts = []
-  for chunk in doc.noun_chunks:
-    if chunk.text not in wh_words:
-      concepts.append(chunk.text)
-  return concepts
-
-def get_passages(text, k=100):
-    doc = nlp(text)
-    passages = []
-    passage_len = 0
-    passage = ""
-    sents = list(doc.sents)
-    for i in range(len(sents)):
-        sen = sents[i]
-        passage_len+=len(sen)
-        if passage_len >= k:
-            passages.append(passage)
-            passage = sen.text
-            passage_len = len(sen)
-            continue
-
-        elif i==(len(sents)-1):
-            passage+=" "+sen.text
-            passages.append(passage)
-            passage = ""
-            passage_len = 0
-            continue
-
-        passage+=" "+sen.text
-    return passages
-
-def get_dicts_for_dpr(concepts, n_results=20, k=100):
-  dicts = []
-  for concept in concepts:
-    wikis = wikipedia.search(concept, results=n_results)
-    print(concept, "No of Wikis: ",len(wikis))
-    for wiki in wikis:
-        try:
-          html_page = wikipedia.page(title = wiki, auto_suggest = False)
-        except DisambiguationError:
-          continue
-
-        passages = get_passages(html_page.content, k=k)
-        for passage in passages:
-          i_dicts = {}
-          i_dicts['text'] = passage
-          i_dicts['title'] = wiki
-          dicts.append(i_dicts)
-  return dicts
-
-passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
-query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
-
-p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
-q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
-
-def get_title_text_combined(passage_dicts):
-    res = []
-    for p in passage_dicts:
-        res.append(tuple((p['title'], p['text'])))
-    return res
-    
-def extracted_passage_embeddings(processed_passages, max_length=156):
-    passage_inputs = p_tokenizer.batch_encode_plus(
-                    processed_passages,
-                    add_special_tokens=True,
-                    truncation=True,
-                    padding="max_length",
-                    max_length=max_length,
-                    return_token_type_ids=True
-                )
-    passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), 
-                                                np.array(passage_inputs['attention_mask']), 
-                                                np.array(passage_inputs['token_type_ids'])], 
-                                                batch_size=64, 
-                                                verbose=1)
-    return passage_embeddings
-
-def extracted_query_embeddings(queries, max_length=64):
-    query_inputs = q_tokenizer.batch_encode_plus(
-                    queries,
-                    add_special_tokens=True,
-                    truncation=True,
-                    padding="max_length",
-                    max_length=max_length,
-                    return_token_type_ids=True
-                )
-    query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']), 
-                                                np.array(query_inputs['attention_mask']), 
-                                                np.array(query_inputs['token_type_ids'])], 
-                                                batch_size=1, 
-                                                verbose=1)
-    return query_embeddings
-    
-def search(question):
-  concepts = get_concepts(question)
-  print("concepts: ",concepts)
-  dicts = get_dicts_for_dpr(concepts, n_results=1)
-  print("dicts len: ", len(dicts))
-  processed_passages = get_title_text_combined(dicts)
-  passage_embeddings = extracted_passage_embeddings(processed_passages)
-  query_embeddings = extracted_query_embeddings([question])
-  faiss_index = faiss.IndexFlatL2(128)
-  faiss_index.add(passage_embeddings.pooler_output)
-  prob, index = faiss_index.search(query_embeddings.pooler_output, k=10)
-  return pd.DataFrame([dicts[i] for i in index[0]])
-
-import gradio as gr
-inp = gr.inputs.Textbox(lines=2, default="Who is aamir khan?", label="Question")
-out = gr.outputs.Dataframe(label="Answers")
-gr.Interface(fn=search, inputs=inp, outputs=out).launch()
\ No newline at end of file
diff --git a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/css__9cWqptSUNYq5YedfIwh33VxtugFVWjDdEsblT8GhLKI__quUjyhSfbE.css b/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/css__9cWqptSUNYq5YedfIwh33VxtugFVWjDdEsblT8GhLKI__quUjyhSfbE.css
deleted file mode 100644
index 0a35b34e3cee4de63d69eda89ffb13c59410bb6a..0000000000000000000000000000000000000000
--- a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/css__9cWqptSUNYq5YedfIwh33VxtugFVWjDdEsblT8GhLKI__quUjyhSfbE.css	
+++ /dev/null
@@ -1,2 +0,0 @@
-#autocomplete{border:1px solid;overflow:hidden;position:absolute;z-index:100;}#autocomplete ul{list-style:none;list-style-image:none;margin:0;padding:0;}#autocomplete li{background:#fff;color:#000;cursor:default;white-space:pre;zoom:1;}html.js input.form-autocomplete{background-image:url(/misc/throbber-inactive.png);background-position:100% center;background-repeat:no-repeat;}html.js input.throbbing{background-image:url(/misc/throbber-active.gif);background-position:100% center;}html.js fieldset.collapsed{border-bottom-width:0;border-left-width:0;border-right-width:0;height:1em;}html.js fieldset.collapsed .fieldset-wrapper{display:none;}fieldset.collapsible{position:relative;}fieldset.collapsible .fieldset-legend{display:block;}.form-textarea-wrapper textarea{display:block;margin:0;width:100%;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;box-sizing:border-box;}.resizable-textarea .grippie{background:#eee url(/misc/grippie.png) no-repeat center 2px;border:1px solid #ddd;border-top-width:0;cursor:s-resize;height:9px;overflow:hidden;}body.drag{cursor:move;}.draggable a.tabledrag-handle{cursor:move;float:left;height:1.7em;margin-left:-1em;overflow:hidden;text-decoration:none;}a.tabledrag-handle:hover{text-decoration:none;}a.tabledrag-handle .handle{background:url(/misc/draggable.png) no-repeat 6px 9px;height:13px;margin:-0.4em 0.5em;padding:0.42em 0.5em;width:13px;}a.tabledrag-handle-hover .handle{background-position:6px -11px;}div.indentation{float:left;height:1.7em;margin:-0.4em 0.2em -0.4em -0.4em;padding:0.42em 0 0.42em 0.6em;width:20px;}div.tree-child{background:url(/misc/tree.png) no-repeat 11px center;}div.tree-child-last{background:url(/misc/tree-bottom.png) no-repeat 11px center;}div.tree-child-horizontal{background:url(/misc/tree.png) no-repeat -11px center;}.tabledrag-toggle-weight-wrapper{text-align:right;}table.sticky-header{background-color:#fff;margin-top:0;}.progress .bar{background-color:#fff;border:1px solid;}.progress .filled{background-color:#000;height:1.5em;width:5px;}.progress .percentage{float:right;}.ajax-progress{display:inline-block;}.ajax-progress .throbber{background:transparent url(/misc/throbber-active.gif) no-repeat 0px center;float:left;height:15px;margin:2px;width:15px;}.ajax-progress .message{padding-left:20px;}tr .ajax-progress .throbber{margin:0 2px;}.ajax-progress-bar{width:16em;}.container-inline div,.container-inline label{display:inline;}.container-inline .fieldset-wrapper{display:block;}.nowrap{white-space:nowrap;}html.js .js-hide{display:none;}.element-hidden{display:none;}.element-invisible{position:absolute !important;clip:rect(1px 1px 1px 1px);clip:rect(1px,1px,1px,1px);overflow:hidden;height:1px;}.element-invisible.element-focusable:active,.element-invisible.element-focusable:focus{position:static !important;clip:auto;overflow:visible;height:auto;}.clearfix:after{content:".";display:block;height:0;clear:both;visibility:hidden;}* html .clearfix{height:1%;}*:first-child + html .clearfix{min-height:1%;}
-/*})'"*/
diff --git a/spaces/ntt123/Vietnam-male-voice-TTS/modules.py b/spaces/ntt123/Vietnam-male-voice-TTS/modules.py
deleted file mode 100644
index f1f6b020927b2076cd682731d5ae6cf889a7abfe..0000000000000000000000000000000000000000
--- a/spaces/ntt123/Vietnam-male-voice-TTS/modules.py
+++ /dev/null
@@ -1,356 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import Conv1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, weight_norm
-
-import commons
-from commons import get_padding, init_weights
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
diff --git a/spaces/ofig/live-lm-critic/critic/PIE/word_level_perturb.py b/spaces/ofig/live-lm-critic/critic/PIE/word_level_perturb.py
deleted file mode 100644
index fc25a32ef641938a9277dc3e251e1e7df7da6d31..0000000000000000000000000000000000000000
--- a/spaces/ofig/live-lm-critic/critic/PIE/word_level_perturb.py
+++ /dev/null
@@ -1,237 +0,0 @@
-"""
-Word-level perturbation generator.
-
-Originally by https://github.com/awasthiabhijeet/PIE/tree/master/errorify
-"""
-import os
-import math
-import pickle
-import random
-import editdistance
-from numpy.random import choice as npchoice
-from collections import defaultdict
-
-
-try:
-    dir_path = os.path.dirname(os.path.realpath(__file__))
-except:
-    dir_path = '.'
-
-VERBS = pickle.load(open(f'{dir_path}/verbs.p', 'rb'))
-COMMON_INSERTS = set(pickle.load(open(f'{dir_path}/common_inserts.p', 'rb'))) #common inserts *to fix a sent*
-COMMON_DELETES = pickle.load(open(f'{dir_path}/common_deletes.p','rb')) #common deletes *to fix a sent*
-_COMMON_REPLACES = pickle.load(open(f'{dir_path}/common_replaces.p', 'rb')) #common replacements *to errorify a sent*
-
-
-
-COMMON_REPLACES = {}
-for src in _COMMON_REPLACES:
-    for tgt in _COMMON_REPLACES[src]:
-        if (src=="'re" and tgt=="are") or (tgt=="'re" and src=="are"):
-            continue
-        ED = editdistance.eval(tgt, src)
-        if ED > 2:
-            continue
-        longer = max(len(src), len(tgt))
-        if float(ED)/longer >= 0.5:
-            continue
-        if tgt not in COMMON_REPLACES:
-            COMMON_REPLACES[tgt] = {}
-        COMMON_REPLACES[tgt][src] = _COMMON_REPLACES[src][tgt]
-
-
-VERBS_refine = defaultdict(list)
-for src in VERBS:
-    for tgt in VERBS[src]:
-        ED = editdistance.eval(tgt, src)
-        if ED > 2:
-            continue
-        longer = max(len(src), len(tgt))
-        if float(ED)/longer >= 0.5:
-            continue
-        VERBS_refine[src].append(tgt)
-
-
-
-class WordLevelPerturber_all:
-    def __init__(self, sentence: str):
-        self.original_sentence = sentence.rstrip()
-        self.sentence = self.original_sentence
-        self.tokenized = None
-        self.tokenize()
-
-    def tokenize(self):
-        self.tokenized = self.sentence.split()
-
-    def orig(self):
-        return self.original_sentence
-
-    def _insert(self):
-        """Insert a commonly deleted word."""
-        if len(self.tokenized) > 0:
-            insertable = list(range(len(self.tokenized)))
-            index = random.choice(insertable)
-            plist = list(COMMON_DELETES.values())
-            plistsum = sum(plist)
-            plist = [x / plistsum for x in plist]
-            # Choose a word
-            ins_word = npchoice(list(COMMON_DELETES.keys()), p=plist)
-            self.tokenized.insert(index,ins_word)
-        return ' '.join(self.tokenized)
-
-    def _mod_verb(self, redir=True):
-        if len(self.tokenized) > 0:
-            verbs = [i for i, w in enumerate(self.tokenized) if w in VERBS]
-            if not verbs:
-                if redir:
-                    return self._replace(redir=False)
-                return self.sentence
-            index = random.choice(verbs)
-            word = self.tokenized[index]
-            if not VERBS[word]:
-                return self.sentence
-            repl = random.choice(VERBS[word])
-            self.tokenized[index] = repl
-        return ' '.join(self.tokenized)
-
-    def _delete(self):
-        """Delete a commonly inserted word."""
-        if len(self.tokenized) > 1:
-            toks_len = len(self.tokenized)
-            toks = self.tokenized
-            deletable = [i for i, w in enumerate(toks) if w in COMMON_INSERTS]
-            if not deletable:
-                return self.sentence
-            index = random.choice(deletable)
-            del self.tokenized[index]
-        return ' '.join(self.tokenized)
-
-    def _replace(self, redir=True):
-        if len(self.tokenized) > 0:
-            deletable = [i for i, w in enumerate(self.tokenized) if (w in COMMON_REPLACES)]
-            if not deletable:
-                if redir:
-                    return self._mod_verb(redir=False)
-                return self.sentence
-            index = random.choice(deletable)
-            word = self.tokenized[index]
-            if not COMMON_REPLACES[word]:
-                return self.sentence
-            # Normalize probabilities
-            plist = list(COMMON_REPLACES[word].values())
-            plistsum = sum(plist)
-            plist = [x / plistsum for x in plist]
-            # Choose a word
-            repl = npchoice(list(COMMON_REPLACES[word].keys()), p=plist)
-            self.tokenized[index] = repl
-        return ' '.join(self.tokenized)
-
-    def perturb(self):
-        count = 1
-        orig_sent = self.sentence
-        for x in range(count):
-            perturb_probs = [.30,.30,.30,.10]
-            perturb_fun = npchoice([self._insert, self._mod_verb, self._replace, self._delete],p=perturb_probs)
-            self.sentence = perturb_fun()
-            self.tokenize()
-        res_sentence = self.sentence
-        self.sentence = self.original_sentence
-        self.tokenize()
-        return res_sentence
-
-
-class WordLevelPerturber_refine:
-    def __init__(self, sentence: str):
-        self.original_sentence = sentence.rstrip()
-        self.sentence = self.original_sentence
-        self.tokenized = None
-        self.tokenize()
-
-    def tokenize(self):
-        self.tokenized = self.sentence.split()
-
-    def orig(self):
-        return self.original_sentence
-
-    def _insert(self):
-        """Insert a commonly deleted word."""
-        if len(self.tokenized) > 0:
-            insertable = list(range(len(self.tokenized)))
-            index = random.choice(insertable)
-            plist = list(COMMON_DELETES.values())
-            plistsum = sum(plist)
-            plist = [x / plistsum for x in plist]
-            # Choose a word
-            ins_word = npchoice(list(COMMON_DELETES.keys()), p=plist)
-            self.tokenized.insert(index,ins_word)
-        return ' '.join(self.tokenized)
-
-    def _mod_verb(self, redir=True):
-        if len(self.tokenized) > 0:
-            verbs = [i for i, w in enumerate(self.tokenized) if w in VERBS_refine]
-            if not verbs:
-                if redir:
-                    return self._replace(redir=False)
-                return self.sentence
-            index = random.choice(verbs)
-            word = self.tokenized[index]
-            if not VERBS_refine[word]:
-                return self.sentence
-            repl = random.choice(VERBS_refine[word])
-            self.tokenized[index] = repl
-
-        return ' '.join(self.tokenized)
-
-    def _delete(self):
-        """Delete a commonly inserted word."""
-        if len(self.tokenized) > 1:
-            toks_len = len(self.tokenized)
-            toks = self.tokenized
-            deletable = [i for i, w in enumerate(toks) if (w in COMMON_INSERTS) and (i>0 and toks[i-1].lower() == toks[i].lower())]
-            if not deletable:
-                return self.sentence
-            index = random.choice(deletable)
-            del self.tokenized[index]
-        return ' '.join(self.tokenized)
-
-    def _replace(self, redir=True):
-        def _keep(i,w):
-            if w.lower() in {"not", "n't"}:
-                return True
-            return False
-
-        if len(self.tokenized) > 0:
-            deletable = [i for i, w in enumerate(self.tokenized) if (w in COMMON_REPLACES) and (not _keep(i,w))]
-            if not deletable:
-                if redir:
-                    return self._mod_verb(redir=False)
-                return self.sentence
-
-            index = random.choice(deletable)
-            word = self.tokenized[index]
-            if not COMMON_REPLACES[word]:
-                return self.sentence
-
-            # Normalize probabilities
-            plist = list(COMMON_REPLACES[word].values())
-            plistsum = sum(plist)
-            plist = [x / plistsum for x in plist]
-
-            # Choose a word
-            repl = npchoice(list(COMMON_REPLACES[word].keys()), p=plist)
-            self.tokenized[index] = repl
-
-        return ' '.join(self.tokenized)
-
-    def perturb(self):
-        count = 1
-        orig_sent = self.sentence
-        for x in range(count):
-            perturb_probs = [.30,.30,.30,.10]
-            perturb_fun = npchoice([self._insert, self._mod_verb, self._replace, self._delete],p=perturb_probs)
-            self.sentence = perturb_fun()
-            self.tokenize()
-        res_sentence = self.sentence
-        self.sentence = self.original_sentence
-        self.tokenize()
-        return res_sentence
diff --git a/spaces/patgpt4/MusicGen/audiocraft/quantization/core_vq.py b/spaces/patgpt4/MusicGen/audiocraft/quantization/core_vq.py
deleted file mode 100644
index e1896bb1788a945a1f7be6369abb255ecf72c7a0..0000000000000000000000000000000000000000
--- a/spaces/patgpt4/MusicGen/audiocraft/quantization/core_vq.py
+++ /dev/null
@@ -1,400 +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 typing as tp
-
-from einops import rearrange, repeat
-import flashy
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-
-
-def exists(val: tp.Optional[tp.Any]) -> bool:
-    return val is not None
-
-
-def default(val: tp.Any, d: tp.Any) -> tp.Any:
-    return val if exists(val) else d
-
-
-def l2norm(t):
-    return F.normalize(t, p=2, dim=-1)
-
-
-def ema_inplace(moving_avg, new, decay: float):
-    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
-
-
-def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
-    return (x + epsilon) / (x.sum() + n_categories * epsilon)
-
-
-def uniform_init(*shape: int):
-    t = torch.empty(shape)
-    nn.init.kaiming_uniform_(t)
-    return t
-
-
-def sample_vectors(samples, num: int):
-    num_samples, device = samples.shape[0], samples.device
-
-    if num_samples >= num:
-        indices = torch.randperm(num_samples, device=device)[:num]
-    else:
-        indices = torch.randint(0, num_samples, (num,), device=device)
-
-    return samples[indices]
-
-
-def kmeans(samples, num_clusters: int, num_iters: int = 10):
-    dim, dtype = samples.shape[-1], samples.dtype
-
-    means = sample_vectors(samples, num_clusters)
-
-    for _ in range(num_iters):
-        diffs = rearrange(samples, "n d -> n () d") - rearrange(
-            means, "c d -> () c d"
-        )
-        dists = -(diffs ** 2).sum(dim=-1)
-
-        buckets = dists.max(dim=-1).indices
-        bins = torch.bincount(buckets, minlength=num_clusters)
-        zero_mask = bins == 0
-        bins_min_clamped = bins.masked_fill(zero_mask, 1)
-
-        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
-        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
-        new_means = new_means / bins_min_clamped[..., None]
-
-        means = torch.where(zero_mask[..., None], means, new_means)
-
-    return means, bins
-
-
-def orthgonal_loss_fn(t):
-    # eq (2) from https://arxiv.org/abs/2112.00384
-    n = t.shape[0]
-    normed_codes = l2norm(t)
-    identity = torch.eye(n, device=t.device)
-    cosine_sim = einsum("i d, j d -> i j", normed_codes, normed_codes)
-    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)
-
-
-class EuclideanCodebook(nn.Module):
-    """Codebook with Euclidean distance.
-
-    Args:
-        dim (int): Dimension.
-        codebook_size (int): Codebook size.
-        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
-            If set to true, run the k-means algorithm on the first training batch and use
-            the learned centroids as initialization.
-        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        kmeans_init: int = False,
-        kmeans_iters: int = 10,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        threshold_ema_dead_code: int = 2,
-    ):
-        super().__init__()
-        self.decay = decay
-        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
-        embed = init_fn(codebook_size, dim)
-
-        self.codebook_size = codebook_size
-
-        self.kmeans_iters = kmeans_iters
-        self.epsilon = epsilon
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-
-        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
-        self.register_buffer("cluster_size", torch.zeros(codebook_size))
-        self.register_buffer("embed", embed)
-        self.register_buffer("embed_avg", embed.clone())
-
-    @torch.jit.ignore
-    def init_embed_(self, data):
-        if self.inited:
-            return
-
-        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
-        self.embed.data.copy_(embed)
-        self.embed_avg.data.copy_(embed.clone())
-        self.cluster_size.data.copy_(cluster_size)
-        self.inited.data.copy_(torch.Tensor([True]))
-        # Make sure all buffers across workers are in sync after initialization
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def replace_(self, samples, mask):
-        modified_codebook = torch.where(
-            mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
-        )
-        self.embed.data.copy_(modified_codebook)
-
-    def expire_codes_(self, batch_samples):
-        if self.threshold_ema_dead_code == 0:
-            return
-
-        expired_codes = self.cluster_size < self.threshold_ema_dead_code
-        if not torch.any(expired_codes):
-            return
-
-        batch_samples = rearrange(batch_samples, "... d -> (...) d")
-        self.replace_(batch_samples, mask=expired_codes)
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def preprocess(self, x):
-        x = rearrange(x, "... d -> (...) d")
-        return x
-
-    def quantize(self, x):
-        embed = self.embed.t()
-        dist = -(
-            x.pow(2).sum(1, keepdim=True)
-            - 2 * x @ embed
-            + embed.pow(2).sum(0, keepdim=True)
-        )
-        embed_ind = dist.max(dim=-1).indices
-        return embed_ind
-
-    def postprocess_emb(self, embed_ind, shape):
-        return embed_ind.view(*shape[:-1])
-
-    def dequantize(self, embed_ind):
-        quantize = F.embedding(embed_ind, self.embed)
-        return quantize
-
-    def encode(self, x):
-        shape = x.shape
-        # pre-process
-        x = self.preprocess(x)
-        # quantize
-        embed_ind = self.quantize(x)
-        # post-process
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        return embed_ind
-
-    def decode(self, embed_ind):
-        quantize = self.dequantize(embed_ind)
-        return quantize
-
-    def forward(self, x):
-        shape, dtype = x.shape, x.dtype
-        x = self.preprocess(x)
-        self.init_embed_(x)
-
-        embed_ind = self.quantize(x)
-        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        quantize = self.dequantize(embed_ind)
-
-        if self.training:
-            # We do the expiry of code at that point as buffers are in sync
-            # and all the workers will take the same decision.
-            self.expire_codes_(x)
-            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
-            embed_sum = x.t() @ embed_onehot
-            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
-            cluster_size = (
-                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
-                * self.cluster_size.sum()
-            )
-            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
-            self.embed.data.copy_(embed_normalized)
-
-        return quantize, embed_ind
-
-
-class VectorQuantization(nn.Module):
-    """Vector quantization implementation.
-    Currently supports only euclidean distance.
-
-    Args:
-        dim (int): Dimension
-        codebook_size (int): Codebook size
-        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int):
-        channels_last (bool): Channels are the last dimension in the input tensors.
-        commitment_weight (float): Weight for commitment loss.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider
-            for orthogonal regulariation.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        codebook_dim: tp.Optional[int] = None,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        kmeans_init: bool = False,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        channels_last: bool = False,
-        commitment_weight: float = 1.,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        _codebook_dim: int = default(codebook_dim, dim)
-
-        requires_projection = _codebook_dim != dim
-        self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity())
-        self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity())
-
-        self.epsilon = epsilon
-        self.commitment_weight = commitment_weight
-
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-
-        self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
-                                           kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
-                                           decay=decay, epsilon=epsilon,
-                                           threshold_ema_dead_code=threshold_ema_dead_code)
-        self.codebook_size = codebook_size
-
-        self.channels_last = channels_last
-
-    @property
-    def codebook(self):
-        return self._codebook.embed
-
-    @property
-    def inited(self):
-        return self._codebook.inited
-
-    def _preprocess(self, x):
-        if not self.channels_last:
-            x = rearrange(x, "b d n -> b n d")
-        return x
-
-    def _postprocess(self, quantize):
-        if not self.channels_last:
-            quantize = rearrange(quantize, "b n d -> b d n")
-        return quantize
-
-    def encode(self, x):
-        x = self._preprocess(x)
-        x = self.project_in(x)
-        embed_in = self._codebook.encode(x)
-        return embed_in
-
-    def decode(self, embed_ind):
-        quantize = self._codebook.decode(embed_ind)
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-        return quantize
-
-    def forward(self, x):
-        device = x.device
-        x = self._preprocess(x)
-
-        x = self.project_in(x)
-        quantize, embed_ind = self._codebook(x)
-
-        if self.training:
-            quantize = x + (quantize - x).detach()
-
-        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
-
-        if self.training:
-            if self.commitment_weight > 0:
-                commit_loss = F.mse_loss(quantize.detach(), x)
-                loss = loss + commit_loss * self.commitment_weight
-
-            if self.orthogonal_reg_weight > 0:
-                codebook = self.codebook
-
-                if self.orthogonal_reg_active_codes_only:
-                    # only calculate orthogonal loss for the activated codes for this batch
-                    unique_code_ids = torch.unique(embed_ind)
-                    codebook = codebook[unique_code_ids]
-
-                num_codes = codebook.shape[0]
-                if exists(self.orthogonal_reg_max_codes) and num_codes > self.orthogonal_reg_max_codes:
-                    rand_ids = torch.randperm(num_codes, device=device)[:self.orthogonal_reg_max_codes]
-                    codebook = codebook[rand_ids]
-
-                orthogonal_reg_loss = orthgonal_loss_fn(codebook)
-                loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight
-
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-
-        return quantize, embed_ind, loss
-
-
-class ResidualVectorQuantization(nn.Module):
-    """Residual vector quantization implementation.
-
-    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
-    """
-    def __init__(self, *, num_quantizers, **kwargs):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
-        )
-
-    def forward(self, x, n_q: tp.Optional[int] = None):
-        quantized_out = 0.0
-        residual = x
-
-        all_losses = []
-        all_indices = []
-
-        n_q = n_q or len(self.layers)
-
-        for i, layer in enumerate(self.layers[:n_q]):
-            quantized, indices, loss = layer(residual)
-            residual = residual - quantized
-            quantized_out = quantized_out + quantized
-            all_indices.append(indices)
-            all_losses.append(loss)
-
-        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
-        return quantized_out, out_indices, out_losses
-
-    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
-        residual = x
-        all_indices = []
-        n_q = n_q or len(self.layers)
-        for layer in self.layers[:n_q]:
-            indices = layer.encode(residual)
-            quantized = layer.decode(indices)
-            residual = residual - quantized
-            all_indices.append(indices)
-        out_indices = torch.stack(all_indices)
-        return out_indices
-
-    def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
-        quantized_out = torch.tensor(0.0, device=q_indices.device)
-        for i, indices in enumerate(q_indices):
-            layer = self.layers[i]
-            quantized = layer.decode(indices)
-            quantized_out = quantized_out + quantized
-        return quantized_out
diff --git a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/autoanchor.py b/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/autoanchor.py
deleted file mode 100644
index cdcecd855a5103ebdc7033308dd372320f9e10e8..0000000000000000000000000000000000000000
--- a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/autoanchor.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-AutoAnchor utils
-"""
-
-import random
-
-import numpy as np
-import torch
-import yaml
-from tqdm.auto import tqdm
-
-from utils.general import LOGGER, colorstr, emojis
-
-PREFIX = colorstr('AutoAnchor: ')
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = m.anchors.prod(-1).mean(-1).view(-1)  # mean anchor area per output layer
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da and (da.sign() != ds.sign()):  # same order
-        LOGGER.info(f'{PREFIX}Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1 / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1 / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    stride = m.stride.to(m.anchors.device).view(-1, 1, 1)  # model strides
-    anchors = m.anchors.clone() * stride  # current anchors
-    bpr, aat = metric(anchors.cpu().view(-1, 2))
-    s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
-    if bpr > 0.98:  # threshold to recompute
-        LOGGER.info(emojis(f'{s}Current anchors are a good fit to dataset ✅'))
-    else:
-        LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...'))
-        na = m.anchors.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            LOGGER.info(f'{PREFIX}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchors[:] = anchors.clone().view_as(m.anchors)
-            check_anchor_order(m)  # must be in pixel-space (not grid-space)
-            m.anchors /= stride
-            s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)'
-        else:
-            s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)'
-        LOGGER.info(emojis(s))
-
-
-def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            dataset: path to data.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    from scipy.cluster.vq import kmeans
-
-    npr = np.random
-    thr = 1 / thr
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k, verbose=True):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
-            f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
-            f'past_thr={x[x > thr].mean():.3f}-mean: '
-        for i, x in enumerate(k):
-            s += '%i,%i, ' % (round(x[0]), round(x[1]))
-        if verbose:
-            LOGGER.info(s[:-2])
-        return k
-
-    if isinstance(dataset, str):  # *.yaml file
-        with open(dataset, errors='ignore') as f:
-            data_dict = yaml.safe_load(f)  # model dict
-        from utils.datasets import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found: {i} of {len(wh0)} labels are < 3 pixels in size')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans init
-    try:
-        LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
-        assert n <= len(wh)  # apply overdetermined constraint
-        s = wh.std(0)  # sigmas for whitening
-        k = kmeans(wh / s, n, iter=30)[0] * s  # points
-        assert n == len(k)  # kmeans may return fewer points than requested if wh is insufficient or too similar
-    except Exception:
-        LOGGER.warning(f'{PREFIX}WARNING: switching strategies from kmeans to random init')
-        k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size  # random init
-    wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
-    k = print_results(k, verbose=False)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k, verbose)
-
-    return print_results(k)
diff --git a/spaces/pkiage/fast_arbitrary_image_style_transfer/src/model/model.py b/spaces/pkiage/fast_arbitrary_image_style_transfer/src/model/model.py
deleted file mode 100644
index 5759c67ea99457271cd491dd9dde86150f699c53..0000000000000000000000000000000000000000
--- a/spaces/pkiage/fast_arbitrary_image_style_transfer/src/model/model.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-import tensorflow_hub as hub
-
-from ..image_utils import tensor_to_image
-
-
-@st.cache
-def load_model():
-    hub_handle = 'https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2'
-    # return hub_model
-    return hub.load(hub_handle)
-    
-
-
-def stylize_content_image(model, content_image_file, style_image_file):
-    try:
-        stylized_image = model(tf.constant(
-            content_image_file), tf.constant(style_image_file))[0]
-        return tensor_to_image(stylized_image)  # stylized image
-    except:
-        stylized_image = model(tf.constant(
-            tf.convert_to_tensor(content_image_file[:, :, :, :3])
-        ),
-            tf.constant(
-                tf.convert_to_tensor(style_image_file[:, :, :, :3])
-        )
-        )[0]
-        return tensor_to_image(stylized_image)  # stylized image
diff --git a/spaces/priyaaa22/gen1/app.py b/spaces/priyaaa22/gen1/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/priyaaa22/gen1/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_core/_subprocesses.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_core/_subprocesses.py
deleted file mode 100644
index 1a26ac8c7ff908341c25d2464972160fbe170a65..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/_core/_subprocesses.py
+++ /dev/null
@@ -1,135 +0,0 @@
-from __future__ import annotations
-
-from io import BytesIO
-from os import PathLike
-from subprocess import DEVNULL, PIPE, CalledProcessError, CompletedProcess
-from typing import (
-    IO,
-    Any,
-    AsyncIterable,
-    Mapping,
-    Sequence,
-    cast,
-)
-
-from ..abc import Process
-from ._eventloop import get_asynclib
-from ._tasks import create_task_group
-
-
-async def run_process(
-    command: str | bytes | Sequence[str | bytes],
-    *,
-    input: bytes | None = None,
-    stdout: int | IO[Any] | None = PIPE,
-    stderr: int | IO[Any] | None = PIPE,
-    check: bool = True,
-    cwd: str | bytes | PathLike[str] | None = None,
-    env: Mapping[str, str] | None = None,
-    start_new_session: bool = False,
-) -> CompletedProcess[bytes]:
-    """
-    Run an external command in a subprocess and wait until it completes.
-
-    .. seealso:: :func:`subprocess.run`
-
-    :param command: either a string to pass to the shell, or an iterable of strings containing the
-        executable name or path and its arguments
-    :param input: bytes passed to the standard input of the subprocess
-    :param stdout: either :data:`subprocess.PIPE` or :data:`subprocess.DEVNULL`
-    :param stderr: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL` or
-        :data:`subprocess.STDOUT`
-    :param check: if ``True``, raise :exc:`~subprocess.CalledProcessError` if the process
-        terminates with a return code other than 0
-    :param cwd: If not ``None``, change the working directory to this before running the command
-    :param env: if not ``None``, this mapping replaces the inherited environment variables from the
-        parent process
-    :param start_new_session: if ``true`` the setsid() system call will be made in the child
-        process prior to the execution of the subprocess. (POSIX only)
-    :return: an object representing the completed process
-    :raises ~subprocess.CalledProcessError: if ``check`` is ``True`` and the process exits with a
-        nonzero return code
-
-    """
-
-    async def drain_stream(stream: AsyncIterable[bytes], index: int) -> None:
-        buffer = BytesIO()
-        async for chunk in stream:
-            buffer.write(chunk)
-
-        stream_contents[index] = buffer.getvalue()
-
-    async with await open_process(
-        command,
-        stdin=PIPE if input else DEVNULL,
-        stdout=stdout,
-        stderr=stderr,
-        cwd=cwd,
-        env=env,
-        start_new_session=start_new_session,
-    ) as process:
-        stream_contents: list[bytes | None] = [None, None]
-        try:
-            async with create_task_group() as tg:
-                if process.stdout:
-                    tg.start_soon(drain_stream, process.stdout, 0)
-                if process.stderr:
-                    tg.start_soon(drain_stream, process.stderr, 1)
-                if process.stdin and input:
-                    await process.stdin.send(input)
-                    await process.stdin.aclose()
-
-                await process.wait()
-        except BaseException:
-            process.kill()
-            raise
-
-    output, errors = stream_contents
-    if check and process.returncode != 0:
-        raise CalledProcessError(cast(int, process.returncode), command, output, errors)
-
-    return CompletedProcess(command, cast(int, process.returncode), output, errors)
-
-
-async def open_process(
-    command: str | bytes | Sequence[str | bytes],
-    *,
-    stdin: int | IO[Any] | None = PIPE,
-    stdout: int | IO[Any] | None = PIPE,
-    stderr: int | IO[Any] | None = PIPE,
-    cwd: str | bytes | PathLike[str] | None = None,
-    env: Mapping[str, str] | None = None,
-    start_new_session: bool = False,
-) -> Process:
-    """
-    Start an external command in a subprocess.
-
-    .. seealso:: :class:`subprocess.Popen`
-
-    :param command: either a string to pass to the shell, or an iterable of strings containing the
-        executable name or path and its arguments
-    :param stdin: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`, a
-        file-like object, or ``None``
-    :param stdout: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`,
-        a file-like object, or ``None``
-    :param stderr: one of :data:`subprocess.PIPE`, :data:`subprocess.DEVNULL`,
-        :data:`subprocess.STDOUT`, a file-like object, or ``None``
-    :param cwd: If not ``None``, the working directory is changed before executing
-    :param env: If env is not ``None``, it must be a mapping that defines the environment
-        variables for the new process
-    :param start_new_session: if ``true`` the setsid() system call will be made in the child
-        process prior to the execution of the subprocess. (POSIX only)
-    :return: an asynchronous process object
-
-    """
-    shell = isinstance(command, str)
-    return await get_asynclib().open_process(
-        command,
-        shell=shell,
-        stdin=stdin,
-        stdout=stdout,
-        stderr=stderr,
-        cwd=cwd,
-        env=env,
-        start_new_session=start_new_session,
-    )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_async/http_proxy.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_async/http_proxy.py
deleted file mode 100644
index 4aa7d8741a95dcf18d3f623b855d0fda0c8a7f65..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_async/http_proxy.py
+++ /dev/null
@@ -1,368 +0,0 @@
-import logging
-import ssl
-from base64 import b64encode
-from typing import Iterable, List, Mapping, Optional, Sequence, Tuple, Union
-
-from .._backends.base import SOCKET_OPTION, AsyncNetworkBackend
-from .._exceptions import ProxyError
-from .._models import (
-    URL,
-    Origin,
-    Request,
-    Response,
-    enforce_bytes,
-    enforce_headers,
-    enforce_url,
-)
-from .._ssl import default_ssl_context
-from .._synchronization import AsyncLock
-from .._trace import Trace
-from .connection import AsyncHTTPConnection
-from .connection_pool import AsyncConnectionPool
-from .http11 import AsyncHTTP11Connection
-from .interfaces import AsyncConnectionInterface
-
-HeadersAsSequence = Sequence[Tuple[Union[bytes, str], Union[bytes, str]]]
-HeadersAsMapping = Mapping[Union[bytes, str], Union[bytes, str]]
-
-
-logger = logging.getLogger("httpcore.proxy")
-
-
-def merge_headers(
-    default_headers: Optional[Sequence[Tuple[bytes, bytes]]] = None,
-    override_headers: Optional[Sequence[Tuple[bytes, bytes]]] = None,
-) -> List[Tuple[bytes, bytes]]:
-    """
-    Append default_headers and override_headers, de-duplicating if a key exists
-    in both cases.
-    """
-    default_headers = [] if default_headers is None else list(default_headers)
-    override_headers = [] if override_headers is None else list(override_headers)
-    has_override = set(key.lower() for key, value in override_headers)
-    default_headers = [
-        (key, value)
-        for key, value in default_headers
-        if key.lower() not in has_override
-    ]
-    return default_headers + override_headers
-
-
-def build_auth_header(username: bytes, password: bytes) -> bytes:
-    userpass = username + b":" + password
-    return b"Basic " + b64encode(userpass)
-
-
-class AsyncHTTPProxy(AsyncConnectionPool):
-    """
-    A connection pool that sends requests via an HTTP proxy.
-    """
-
-    def __init__(
-        self,
-        proxy_url: Union[URL, bytes, str],
-        proxy_auth: Optional[Tuple[Union[bytes, str], Union[bytes, str]]] = None,
-        proxy_headers: Union[HeadersAsMapping, HeadersAsSequence, None] = None,
-        ssl_context: Optional[ssl.SSLContext] = None,
-        proxy_ssl_context: Optional[ssl.SSLContext] = None,
-        max_connections: Optional[int] = 10,
-        max_keepalive_connections: Optional[int] = None,
-        keepalive_expiry: Optional[float] = None,
-        http1: bool = True,
-        http2: bool = False,
-        retries: int = 0,
-        local_address: Optional[str] = None,
-        uds: Optional[str] = None,
-        network_backend: Optional[AsyncNetworkBackend] = None,
-        socket_options: Optional[Iterable[SOCKET_OPTION]] = None,
-    ) -> None:
-        """
-        A connection pool for making HTTP requests.
-
-        Parameters:
-            proxy_url: The URL to use when connecting to the proxy server.
-                For example `"http://127.0.0.1:8080/"`.
-            proxy_auth: Any proxy authentication as a two-tuple of
-                (username, password). May be either bytes or ascii-only str.
-            proxy_headers: Any HTTP headers to use for the proxy requests.
-                For example `{"Proxy-Authorization": "Basic <username>:<password>"}`.
-            ssl_context: An SSL context to use for verifying connections.
-                If not specified, the default `httpcore.default_ssl_context()`
-                will be used.
-            proxy_ssl_context: The same as `ssl_context`, but for a proxy server rather than a remote origin.
-            max_connections: The maximum number of concurrent HTTP connections that
-                the pool should allow. Any attempt to send a request on a pool that
-                would exceed this amount will block until a connection is available.
-            max_keepalive_connections: The maximum number of idle HTTP connections
-                that will be maintained in the pool.
-            keepalive_expiry: The duration in seconds that an idle HTTP connection
-                may be maintained for before being expired from the pool.
-            http1: A boolean indicating if HTTP/1.1 requests should be supported
-                by the connection pool. Defaults to True.
-            http2: A boolean indicating if HTTP/2 requests should be supported by
-                the connection pool. Defaults to False.
-            retries: The maximum number of retries when trying to establish
-                a connection.
-            local_address: Local address to connect from. Can also be used to
-                connect using a particular address family. Using
-                `local_address="0.0.0.0"` will connect using an `AF_INET` address
-                (IPv4), while using `local_address="::"` will connect using an
-                `AF_INET6` address (IPv6).
-            uds: Path to a Unix Domain Socket to use instead of TCP sockets.
-            network_backend: A backend instance to use for handling network I/O.
-        """
-        super().__init__(
-            ssl_context=ssl_context,
-            max_connections=max_connections,
-            max_keepalive_connections=max_keepalive_connections,
-            keepalive_expiry=keepalive_expiry,
-            http1=http1,
-            http2=http2,
-            network_backend=network_backend,
-            retries=retries,
-            local_address=local_address,
-            uds=uds,
-            socket_options=socket_options,
-        )
-
-        self._proxy_url = enforce_url(proxy_url, name="proxy_url")
-        if (
-            self._proxy_url.scheme == b"http" and proxy_ssl_context is not None
-        ):  # pragma: no cover
-            raise RuntimeError(
-                "The `proxy_ssl_context` argument is not allowed for the http scheme"
-            )
-
-        self._ssl_context = ssl_context
-        self._proxy_ssl_context = proxy_ssl_context
-        self._proxy_headers = enforce_headers(proxy_headers, name="proxy_headers")
-        if proxy_auth is not None:
-            username = enforce_bytes(proxy_auth[0], name="proxy_auth")
-            password = enforce_bytes(proxy_auth[1], name="proxy_auth")
-            authorization = build_auth_header(username, password)
-            self._proxy_headers = [
-                (b"Proxy-Authorization", authorization)
-            ] + self._proxy_headers
-
-    def create_connection(self, origin: Origin) -> AsyncConnectionInterface:
-        if origin.scheme == b"http":
-            return AsyncForwardHTTPConnection(
-                proxy_origin=self._proxy_url.origin,
-                proxy_headers=self._proxy_headers,
-                remote_origin=origin,
-                keepalive_expiry=self._keepalive_expiry,
-                network_backend=self._network_backend,
-                proxy_ssl_context=self._proxy_ssl_context,
-            )
-        return AsyncTunnelHTTPConnection(
-            proxy_origin=self._proxy_url.origin,
-            proxy_headers=self._proxy_headers,
-            remote_origin=origin,
-            ssl_context=self._ssl_context,
-            proxy_ssl_context=self._proxy_ssl_context,
-            keepalive_expiry=self._keepalive_expiry,
-            http1=self._http1,
-            http2=self._http2,
-            network_backend=self._network_backend,
-        )
-
-
-class AsyncForwardHTTPConnection(AsyncConnectionInterface):
-    def __init__(
-        self,
-        proxy_origin: Origin,
-        remote_origin: Origin,
-        proxy_headers: Union[HeadersAsMapping, HeadersAsSequence, None] = None,
-        keepalive_expiry: Optional[float] = None,
-        network_backend: Optional[AsyncNetworkBackend] = None,
-        socket_options: Optional[Iterable[SOCKET_OPTION]] = None,
-        proxy_ssl_context: Optional[ssl.SSLContext] = None,
-    ) -> None:
-        self._connection = AsyncHTTPConnection(
-            origin=proxy_origin,
-            keepalive_expiry=keepalive_expiry,
-            network_backend=network_backend,
-            socket_options=socket_options,
-            ssl_context=proxy_ssl_context,
-        )
-        self._proxy_origin = proxy_origin
-        self._proxy_headers = enforce_headers(proxy_headers, name="proxy_headers")
-        self._remote_origin = remote_origin
-
-    async def handle_async_request(self, request: Request) -> Response:
-        headers = merge_headers(self._proxy_headers, request.headers)
-        url = URL(
-            scheme=self._proxy_origin.scheme,
-            host=self._proxy_origin.host,
-            port=self._proxy_origin.port,
-            target=bytes(request.url),
-        )
-        proxy_request = Request(
-            method=request.method,
-            url=url,
-            headers=headers,
-            content=request.stream,
-            extensions=request.extensions,
-        )
-        return await self._connection.handle_async_request(proxy_request)
-
-    def can_handle_request(self, origin: Origin) -> bool:
-        return origin == self._remote_origin
-
-    async def aclose(self) -> None:
-        await self._connection.aclose()
-
-    def info(self) -> str:
-        return self._connection.info()
-
-    def is_available(self) -> bool:
-        return self._connection.is_available()
-
-    def has_expired(self) -> bool:
-        return self._connection.has_expired()
-
-    def is_idle(self) -> bool:
-        return self._connection.is_idle()
-
-    def is_closed(self) -> bool:
-        return self._connection.is_closed()
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} [{self.info()}]>"
-
-
-class AsyncTunnelHTTPConnection(AsyncConnectionInterface):
-    def __init__(
-        self,
-        proxy_origin: Origin,
-        remote_origin: Origin,
-        ssl_context: Optional[ssl.SSLContext] = None,
-        proxy_ssl_context: Optional[ssl.SSLContext] = None,
-        proxy_headers: Optional[Sequence[Tuple[bytes, bytes]]] = None,
-        keepalive_expiry: Optional[float] = None,
-        http1: bool = True,
-        http2: bool = False,
-        network_backend: Optional[AsyncNetworkBackend] = None,
-        socket_options: Optional[Iterable[SOCKET_OPTION]] = None,
-    ) -> None:
-        self._connection: AsyncConnectionInterface = AsyncHTTPConnection(
-            origin=proxy_origin,
-            keepalive_expiry=keepalive_expiry,
-            network_backend=network_backend,
-            socket_options=socket_options,
-            ssl_context=proxy_ssl_context,
-        )
-        self._proxy_origin = proxy_origin
-        self._remote_origin = remote_origin
-        self._ssl_context = ssl_context
-        self._proxy_ssl_context = proxy_ssl_context
-        self._proxy_headers = enforce_headers(proxy_headers, name="proxy_headers")
-        self._keepalive_expiry = keepalive_expiry
-        self._http1 = http1
-        self._http2 = http2
-        self._connect_lock = AsyncLock()
-        self._connected = False
-
-    async def handle_async_request(self, request: Request) -> Response:
-        timeouts = request.extensions.get("timeout", {})
-        timeout = timeouts.get("connect", None)
-
-        async with self._connect_lock:
-            if not self._connected:
-                target = b"%b:%d" % (self._remote_origin.host, self._remote_origin.port)
-
-                connect_url = URL(
-                    scheme=self._proxy_origin.scheme,
-                    host=self._proxy_origin.host,
-                    port=self._proxy_origin.port,
-                    target=target,
-                )
-                connect_headers = merge_headers(
-                    [(b"Host", target), (b"Accept", b"*/*")], self._proxy_headers
-                )
-                connect_request = Request(
-                    method=b"CONNECT",
-                    url=connect_url,
-                    headers=connect_headers,
-                    extensions=request.extensions,
-                )
-                connect_response = await self._connection.handle_async_request(
-                    connect_request
-                )
-
-                if connect_response.status < 200 or connect_response.status > 299:
-                    reason_bytes = connect_response.extensions.get("reason_phrase", b"")
-                    reason_str = reason_bytes.decode("ascii", errors="ignore")
-                    msg = "%d %s" % (connect_response.status, reason_str)
-                    await self._connection.aclose()
-                    raise ProxyError(msg)
-
-                stream = connect_response.extensions["network_stream"]
-
-                # Upgrade the stream to SSL
-                ssl_context = (
-                    default_ssl_context()
-                    if self._ssl_context is None
-                    else self._ssl_context
-                )
-                alpn_protocols = ["http/1.1", "h2"] if self._http2 else ["http/1.1"]
-                ssl_context.set_alpn_protocols(alpn_protocols)
-
-                kwargs = {
-                    "ssl_context": ssl_context,
-                    "server_hostname": self._remote_origin.host.decode("ascii"),
-                    "timeout": timeout,
-                }
-                async with Trace("start_tls", logger, request, kwargs) as trace:
-                    stream = await stream.start_tls(**kwargs)
-                    trace.return_value = stream
-
-                # Determine if we should be using HTTP/1.1 or HTTP/2
-                ssl_object = stream.get_extra_info("ssl_object")
-                http2_negotiated = (
-                    ssl_object is not None
-                    and ssl_object.selected_alpn_protocol() == "h2"
-                )
-
-                # Create the HTTP/1.1 or HTTP/2 connection
-                if http2_negotiated or (self._http2 and not self._http1):
-                    from .http2 import AsyncHTTP2Connection
-
-                    self._connection = AsyncHTTP2Connection(
-                        origin=self._remote_origin,
-                        stream=stream,
-                        keepalive_expiry=self._keepalive_expiry,
-                    )
-                else:
-                    self._connection = AsyncHTTP11Connection(
-                        origin=self._remote_origin,
-                        stream=stream,
-                        keepalive_expiry=self._keepalive_expiry,
-                    )
-
-                self._connected = True
-        return await self._connection.handle_async_request(request)
-
-    def can_handle_request(self, origin: Origin) -> bool:
-        return origin == self._remote_origin
-
-    async def aclose(self) -> None:
-        await self._connection.aclose()
-
-    def info(self) -> str:
-        return self._connection.info()
-
-    def is_available(self) -> bool:
-        return self._connection.is_available()
-
-    def has_expired(self) -> bool:
-        return self._connection.has_expired()
-
-    def is_idle(self) -> bool:
-        return self._connection.is_idle()
-
-    def is_closed(self) -> bool:
-        return self._connection.is_closed()
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} [{self.info()}]>"
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/include/numpy/arrayobject.h b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/include/numpy/arrayobject.h
deleted file mode 100644
index da47bb09627aefe8501abb39b8d2a9225985e3fc..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/include/numpy/arrayobject.h
+++ /dev/null
@@ -1,12 +0,0 @@
-#ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_
-#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_
-#define Py_ARRAYOBJECT_H
-
-#include "ndarrayobject.h"
-#include "npy_interrupt.h"
-
-#ifdef NPY_NO_PREFIX
-#include "noprefix.h"
-#endif
-
-#endif  /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ */
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/linalg/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/linalg/__init__.py
deleted file mode 100644
index 93943de3896c135dde00080d30fd6cbe55a86e5d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/linalg/__init__.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""
-``numpy.linalg``
-================
-
-The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient
-low level implementations of standard linear algebra algorithms. Those
-libraries may be provided by NumPy itself using C versions of a subset of their
-reference implementations but, when possible, highly optimized libraries that
-take advantage of specialized processor functionality are preferred. Examples
-of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries
-are multithreaded and processor dependent, environmental variables and external
-packages such as threadpoolctl may be needed to control the number of threads
-or specify the processor architecture.
-
-- OpenBLAS: https://www.openblas.net/
-- threadpoolctl: https://github.com/joblib/threadpoolctl
-
-Please note that the most-used linear algebra functions in NumPy are present in
-the main ``numpy`` namespace rather than in ``numpy.linalg``.  There are:
-``dot``, ``vdot``, ``inner``, ``outer``, ``matmul``, ``tensordot``, ``einsum``,
-``einsum_path`` and ``kron``.
-
-Functions present in numpy.linalg are listed below.
-
-
-Matrix and vector products
---------------------------
-
-   multi_dot
-   matrix_power
-
-Decompositions
---------------
-
-   cholesky
-   qr
-   svd
-
-Matrix eigenvalues
-------------------
-
-   eig
-   eigh
-   eigvals
-   eigvalsh
-
-Norms and other numbers
------------------------
-
-   norm
-   cond
-   det
-   matrix_rank
-   slogdet
-
-Solving equations and inverting matrices
-----------------------------------------
-
-   solve
-   tensorsolve
-   lstsq
-   inv
-   pinv
-   tensorinv
-
-Exceptions
-----------
-
-   LinAlgError
-
-"""
-# To get sub-modules
-from . import linalg
-from .linalg import *
-
-__all__ = linalg.__all__.copy()
-
-from numpy._pytesttester import PytestTester
-test = PytestTester(__name__)
-del PytestTester
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_series_transform.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_series_transform.py
deleted file mode 100644
index 82592c4711ece5a7f4b6d421d743e1adbd78c345..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/apply/test_series_transform.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    DataFrame,
-    MultiIndex,
-    Series,
-    concat,
-)
-import pandas._testing as tm
-
-
-@pytest.mark.parametrize(
-    "args, kwargs, increment",
-    [((), {}, 0), ((), {"a": 1}, 1), ((2, 3), {}, 32), ((1,), {"c": 2}, 201)],
-)
-def test_agg_args(args, kwargs, increment):
-    # GH 43357
-    def f(x, a=0, b=0, c=0):
-        return x + a + 10 * b + 100 * c
-
-    s = Series([1, 2])
-    result = s.transform(f, 0, *args, **kwargs)
-    expected = s + increment
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "ops, names",
-    [
-        ([np.sqrt], ["sqrt"]),
-        ([np.abs, np.sqrt], ["absolute", "sqrt"]),
-        (np.array([np.sqrt]), ["sqrt"]),
-        (np.array([np.abs, np.sqrt]), ["absolute", "sqrt"]),
-    ],
-)
-def test_transform_listlike(string_series, ops, names):
-    # GH 35964
-    with np.errstate(all="ignore"):
-        expected = concat([op(string_series) for op in ops], axis=1)
-        expected.columns = names
-        result = string_series.transform(ops)
-        tm.assert_frame_equal(result, expected)
-
-
-def test_transform_listlike_func_with_args():
-    # GH 50624
-
-    s = Series([1, 2, 3])
-
-    def foo1(x, a=1, c=0):
-        return x + a + c
-
-    def foo2(x, b=2, c=0):
-        return x + b + c
-
-    msg = r"foo1\(\) got an unexpected keyword argument 'b'"
-    with pytest.raises(TypeError, match=msg):
-        s.transform([foo1, foo2], 0, 3, b=3, c=4)
-
-    result = s.transform([foo1, foo2], 0, 3, c=4)
-    expected = DataFrame({"foo1": [8, 9, 10], "foo2": [8, 9, 10]})
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("box", [dict, Series])
-def test_transform_dictlike(string_series, box):
-    # GH 35964
-    with np.errstate(all="ignore"):
-        expected = concat([np.sqrt(string_series), np.abs(string_series)], axis=1)
-    expected.columns = ["foo", "bar"]
-    result = string_series.transform(box({"foo": np.sqrt, "bar": np.abs}))
-    tm.assert_frame_equal(result, expected)
-
-
-def test_transform_dictlike_mixed():
-    # GH 40018 - mix of lists and non-lists in values of a dictionary
-    df = Series([1, 4])
-    result = df.transform({"b": ["sqrt", "abs"], "c": "sqrt"})
-    expected = DataFrame(
-        [[1.0, 1, 1.0], [2.0, 4, 2.0]],
-        columns=MultiIndex([("b", "c"), ("sqrt", "abs")], [(0, 0, 1), (0, 1, 0)]),
-    )
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/interval/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/interval/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/methods.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/methods.py
deleted file mode 100644
index 16059155a7a8ff9dca321d458e87adfc67b78216..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/methods.py
+++ /dev/null
@@ -1,703 +0,0 @@
-import inspect
-import operator
-
-import numpy as np
-import pytest
-
-from pandas._typing import Dtype
-
-from pandas.core.dtypes.common import is_bool_dtype
-from pandas.core.dtypes.missing import na_value_for_dtype
-
-import pandas as pd
-import pandas._testing as tm
-from pandas.core.sorting import nargsort
-
-
-class BaseMethodsTests:
-    """Various Series and DataFrame methods."""
-
-    def test_hash_pandas_object(self, data):
-        # _hash_pandas_object should return a uint64 ndarray of the same length
-        # as the data
-        from pandas.core.util.hashing import _default_hash_key
-
-        res = data._hash_pandas_object(
-            encoding="utf-8", hash_key=_default_hash_key, categorize=False
-        )
-        assert res.dtype == np.uint64
-        assert res.shape == data.shape
-
-    def test_value_counts_default_dropna(self, data):
-        # make sure we have consistent default dropna kwarg
-        if not hasattr(data, "value_counts"):
-            pytest.skip(f"value_counts is not implemented for {type(data)}")
-        sig = inspect.signature(data.value_counts)
-        kwarg = sig.parameters["dropna"]
-        assert kwarg.default is True
-
-    @pytest.mark.parametrize("dropna", [True, False])
-    def test_value_counts(self, all_data, dropna):
-        all_data = all_data[:10]
-        if dropna:
-            other = all_data[~all_data.isna()]
-        else:
-            other = all_data
-
-        result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
-        expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
-
-        tm.assert_series_equal(result, expected)
-
-    def test_value_counts_with_normalize(self, data):
-        # GH 33172
-        data = data[:10].unique()
-        values = np.array(data[~data.isna()])
-        ser = pd.Series(data, dtype=data.dtype)
-
-        result = ser.value_counts(normalize=True).sort_index()
-
-        if not isinstance(data, pd.Categorical):
-            expected = pd.Series(
-                [1 / len(values)] * len(values), index=result.index, name="proportion"
-            )
-        else:
-            expected = pd.Series(0.0, index=result.index, name="proportion")
-            expected[result > 0] = 1 / len(values)
-
-        if getattr(data.dtype, "storage", "") == "pyarrow" or isinstance(
-            data.dtype, pd.ArrowDtype
-        ):
-            # TODO: avoid special-casing
-            expected = expected.astype("double[pyarrow]")
-        elif getattr(data.dtype, "storage", "") == "pyarrow_numpy":
-            # TODO: avoid special-casing
-            expected = expected.astype("float64")
-        elif na_value_for_dtype(data.dtype) is pd.NA:
-            # TODO(GH#44692): avoid special-casing
-            expected = expected.astype("Float64")
-
-        tm.assert_series_equal(result, expected)
-
-    def test_count(self, data_missing):
-        df = pd.DataFrame({"A": data_missing})
-        result = df.count(axis="columns")
-        expected = pd.Series([0, 1])
-        tm.assert_series_equal(result, expected)
-
-    def test_series_count(self, data_missing):
-        # GH#26835
-        ser = pd.Series(data_missing)
-        result = ser.count()
-        expected = 1
-        assert result == expected
-
-    def test_apply_simple_series(self, data):
-        result = pd.Series(data).apply(id)
-        assert isinstance(result, pd.Series)
-
-    @pytest.mark.parametrize("na_action", [None, "ignore"])
-    def test_map(self, data_missing, na_action):
-        result = data_missing.map(lambda x: x, na_action=na_action)
-        expected = data_missing.to_numpy()
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_argsort(self, data_for_sorting):
-        result = pd.Series(data_for_sorting).argsort()
-        # argsort result gets passed to take, so should be np.intp
-        expected = pd.Series(np.array([2, 0, 1], dtype=np.intp))
-        tm.assert_series_equal(result, expected)
-
-    def test_argsort_missing_array(self, data_missing_for_sorting):
-        result = data_missing_for_sorting.argsort()
-        # argsort result gets passed to take, so should be np.intp
-        expected = np.array([2, 0, 1], dtype=np.intp)
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_argsort_missing(self, data_missing_for_sorting):
-        msg = "The behavior of Series.argsort in the presence of NA values"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = pd.Series(data_missing_for_sorting).argsort()
-        expected = pd.Series(np.array([1, -1, 0], dtype=np.intp))
-        tm.assert_series_equal(result, expected)
-
-    def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_value):
-        # GH 24382
-        is_bool = data_for_sorting.dtype._is_boolean
-
-        exp_argmax = 1
-        exp_argmax_repeated = 3
-        if is_bool:
-            # See data_for_sorting docstring
-            exp_argmax = 0
-            exp_argmax_repeated = 1
-
-        # data_for_sorting -> [B, C, A] with A < B < C
-        assert data_for_sorting.argmax() == exp_argmax
-        assert data_for_sorting.argmin() == 2
-
-        # with repeated values -> first occurrence
-        data = data_for_sorting.take([2, 0, 0, 1, 1, 2])
-        assert data.argmax() == exp_argmax_repeated
-        assert data.argmin() == 0
-
-        # with missing values
-        # data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
-        assert data_missing_for_sorting.argmax() == 0
-        assert data_missing_for_sorting.argmin() == 2
-
-    @pytest.mark.parametrize("method", ["argmax", "argmin"])
-    def test_argmin_argmax_empty_array(self, method, data):
-        # GH 24382
-        err_msg = "attempt to get"
-        with pytest.raises(ValueError, match=err_msg):
-            getattr(data[:0], method)()
-
-    @pytest.mark.parametrize("method", ["argmax", "argmin"])
-    def test_argmin_argmax_all_na(self, method, data, na_value):
-        # all missing with skipna=True is the same as empty
-        err_msg = "attempt to get"
-        data_na = type(data)._from_sequence([na_value, na_value], dtype=data.dtype)
-        with pytest.raises(ValueError, match=err_msg):
-            getattr(data_na, method)()
-
-    @pytest.mark.parametrize(
-        "op_name, skipna, expected",
-        [
-            ("idxmax", True, 0),
-            ("idxmin", True, 2),
-            ("argmax", True, 0),
-            ("argmin", True, 2),
-            ("idxmax", False, np.nan),
-            ("idxmin", False, np.nan),
-            ("argmax", False, -1),
-            ("argmin", False, -1),
-        ],
-    )
-    def test_argreduce_series(
-        self, data_missing_for_sorting, op_name, skipna, expected
-    ):
-        # data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
-        warn = None
-        msg = "The behavior of Series.argmax/argmin"
-        if op_name.startswith("arg") and expected == -1:
-            warn = FutureWarning
-        if op_name.startswith("idx") and np.isnan(expected):
-            warn = FutureWarning
-            msg = f"The behavior of Series.{op_name}"
-        ser = pd.Series(data_missing_for_sorting)
-        with tm.assert_produces_warning(warn, match=msg):
-            result = getattr(ser, op_name)(skipna=skipna)
-        tm.assert_almost_equal(result, expected)
-
-    def test_argmax_argmin_no_skipna_notimplemented(self, data_missing_for_sorting):
-        # GH#38733
-        data = data_missing_for_sorting
-
-        with pytest.raises(NotImplementedError, match=""):
-            data.argmin(skipna=False)
-
-        with pytest.raises(NotImplementedError, match=""):
-            data.argmax(skipna=False)
-
-    @pytest.mark.parametrize(
-        "na_position, expected",
-        [
-            ("last", np.array([2, 0, 1], dtype=np.dtype("intp"))),
-            ("first", np.array([1, 2, 0], dtype=np.dtype("intp"))),
-        ],
-    )
-    def test_nargsort(self, data_missing_for_sorting, na_position, expected):
-        # GH 25439
-        result = nargsort(data_missing_for_sorting, na_position=na_position)
-        tm.assert_numpy_array_equal(result, expected)
-
-    @pytest.mark.parametrize("ascending", [True, False])
-    def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
-        ser = pd.Series(data_for_sorting)
-        result = ser.sort_values(ascending=ascending, key=sort_by_key)
-        expected = ser.iloc[[2, 0, 1]]
-        if not ascending:
-            # GH 35922. Expect stable sort
-            if ser.nunique() == 2:
-                expected = ser.iloc[[0, 1, 2]]
-            else:
-                expected = ser.iloc[[1, 0, 2]]
-
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize("ascending", [True, False])
-    def test_sort_values_missing(
-        self, data_missing_for_sorting, ascending, sort_by_key
-    ):
-        ser = pd.Series(data_missing_for_sorting)
-        result = ser.sort_values(ascending=ascending, key=sort_by_key)
-        if ascending:
-            expected = ser.iloc[[2, 0, 1]]
-        else:
-            expected = ser.iloc[[0, 2, 1]]
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize("ascending", [True, False])
-    def test_sort_values_frame(self, data_for_sorting, ascending):
-        df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting})
-        result = df.sort_values(["A", "B"])
-        expected = pd.DataFrame(
-            {"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1]
-        )
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize("box", [pd.Series, lambda x: x])
-    @pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
-    def test_unique(self, data, box, method):
-        duplicated = box(data._from_sequence([data[0], data[0]]))
-
-        result = method(duplicated)
-
-        assert len(result) == 1
-        assert isinstance(result, type(data))
-        assert result[0] == duplicated[0]
-
-    def test_factorize(self, data_for_grouping):
-        codes, uniques = pd.factorize(data_for_grouping, use_na_sentinel=True)
-
-        is_bool = data_for_grouping.dtype._is_boolean
-        if is_bool:
-            # only 2 unique values
-            expected_codes = np.array([0, 0, -1, -1, 1, 1, 0, 0], dtype=np.intp)
-            expected_uniques = data_for_grouping.take([0, 4])
-        else:
-            expected_codes = np.array([0, 0, -1, -1, 1, 1, 0, 2], dtype=np.intp)
-            expected_uniques = data_for_grouping.take([0, 4, 7])
-
-        tm.assert_numpy_array_equal(codes, expected_codes)
-        tm.assert_extension_array_equal(uniques, expected_uniques)
-
-    def test_factorize_equivalence(self, data_for_grouping):
-        codes_1, uniques_1 = pd.factorize(data_for_grouping, use_na_sentinel=True)
-        codes_2, uniques_2 = data_for_grouping.factorize(use_na_sentinel=True)
-
-        tm.assert_numpy_array_equal(codes_1, codes_2)
-        tm.assert_extension_array_equal(uniques_1, uniques_2)
-        assert len(uniques_1) == len(pd.unique(uniques_1))
-        assert uniques_1.dtype == data_for_grouping.dtype
-
-    def test_factorize_empty(self, data):
-        codes, uniques = pd.factorize(data[:0])
-        expected_codes = np.array([], dtype=np.intp)
-        expected_uniques = type(data)._from_sequence([], dtype=data[:0].dtype)
-
-        tm.assert_numpy_array_equal(codes, expected_codes)
-        tm.assert_extension_array_equal(uniques, expected_uniques)
-
-    def test_fillna_copy_frame(self, data_missing):
-        arr = data_missing.take([1, 1])
-        df = pd.DataFrame({"A": arr})
-        df_orig = df.copy()
-
-        filled_val = df.iloc[0, 0]
-        result = df.fillna(filled_val)
-
-        result.iloc[0, 0] = filled_val
-
-        tm.assert_frame_equal(df, df_orig)
-
-    def test_fillna_copy_series(self, data_missing):
-        arr = data_missing.take([1, 1])
-        ser = pd.Series(arr, copy=False)
-        ser_orig = ser.copy()
-
-        filled_val = ser[0]
-        result = ser.fillna(filled_val)
-        result.iloc[0] = filled_val
-
-        tm.assert_series_equal(ser, ser_orig)
-
-    def test_fillna_length_mismatch(self, data_missing):
-        msg = "Length of 'value' does not match."
-        with pytest.raises(ValueError, match=msg):
-            data_missing.fillna(data_missing.take([1]))
-
-    # Subclasses can override if we expect e.g Sparse[bool], boolean, pyarrow[bool]
-    _combine_le_expected_dtype: Dtype = np.dtype(bool)
-
-    def test_combine_le(self, data_repeated):
-        # GH 20825
-        # Test that combine works when doing a <= (le) comparison
-        orig_data1, orig_data2 = data_repeated(2)
-        s1 = pd.Series(orig_data1)
-        s2 = pd.Series(orig_data2)
-        result = s1.combine(s2, lambda x1, x2: x1 <= x2)
-        expected = pd.Series(
-            [a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))],
-            dtype=self._combine_le_expected_dtype,
-        )
-        tm.assert_series_equal(result, expected)
-
-        val = s1.iloc[0]
-        result = s1.combine(val, lambda x1, x2: x1 <= x2)
-        expected = pd.Series(
-            [a <= val for a in list(orig_data1)],
-            dtype=self._combine_le_expected_dtype,
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_combine_add(self, data_repeated):
-        # GH 20825
-        orig_data1, orig_data2 = data_repeated(2)
-        s1 = pd.Series(orig_data1)
-        s2 = pd.Series(orig_data2)
-
-        # Check if the operation is supported pointwise for our scalars. If not,
-        #  we will expect Series.combine to raise as well.
-        try:
-            with np.errstate(over="ignore"):
-                expected = pd.Series(
-                    orig_data1._from_sequence(
-                        [a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
-                    )
-                )
-        except TypeError:
-            # If the operation is not supported pointwise for our scalars,
-            #  then Series.combine should also raise
-            with pytest.raises(TypeError):
-                s1.combine(s2, lambda x1, x2: x1 + x2)
-            return
-
-        result = s1.combine(s2, lambda x1, x2: x1 + x2)
-        tm.assert_series_equal(result, expected)
-
-        val = s1.iloc[0]
-        result = s1.combine(val, lambda x1, x2: x1 + x2)
-        expected = pd.Series(
-            orig_data1._from_sequence([a + val for a in list(orig_data1)])
-        )
-        tm.assert_series_equal(result, expected)
-
-    def test_combine_first(self, data):
-        # https://github.com/pandas-dev/pandas/issues/24147
-        a = pd.Series(data[:3])
-        b = pd.Series(data[2:5], index=[2, 3, 4])
-        result = a.combine_first(b)
-        expected = pd.Series(data[:5])
-        tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize("frame", [True, False])
-    @pytest.mark.parametrize(
-        "periods, indices",
-        [(-2, [2, 3, 4, -1, -1]), (0, [0, 1, 2, 3, 4]), (2, [-1, -1, 0, 1, 2])],
-    )
-    def test_container_shift(self, data, frame, periods, indices):
-        # https://github.com/pandas-dev/pandas/issues/22386
-        subset = data[:5]
-        data = pd.Series(subset, name="A")
-        expected = pd.Series(subset.take(indices, allow_fill=True), name="A")
-
-        if frame:
-            result = data.to_frame(name="A").assign(B=1).shift(periods)
-            expected = pd.concat(
-                [expected, pd.Series([1] * 5, name="B").shift(periods)], axis=1
-            )
-            compare = tm.assert_frame_equal
-        else:
-            result = data.shift(periods)
-            compare = tm.assert_series_equal
-
-        compare(result, expected)
-
-    def test_shift_0_periods(self, data):
-        # GH#33856 shifting with periods=0 should return a copy, not same obj
-        result = data.shift(0)
-        assert data[0] != data[1]  # otherwise below is invalid
-        data[0] = data[1]
-        assert result[0] != result[1]  # i.e. not the same object/view
-
-    @pytest.mark.parametrize("periods", [1, -2])
-    def test_diff(self, data, periods):
-        data = data[:5]
-        if is_bool_dtype(data.dtype):
-            op = operator.xor
-        else:
-            op = operator.sub
-        try:
-            # does this array implement ops?
-            op(data, data)
-        except Exception:
-            pytest.skip(f"{type(data)} does not support diff")
-        s = pd.Series(data)
-        result = s.diff(periods)
-        expected = pd.Series(op(data, data.shift(periods)))
-        tm.assert_series_equal(result, expected)
-
-        df = pd.DataFrame({"A": data, "B": [1.0] * 5})
-        result = df.diff(periods)
-        if periods == 1:
-            b = [np.nan, 0, 0, 0, 0]
-        else:
-            b = [0, 0, 0, np.nan, np.nan]
-        expected = pd.DataFrame({"A": expected, "B": b})
-        tm.assert_frame_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "periods, indices",
-        [[-4, [-1, -1]], [-1, [1, -1]], [0, [0, 1]], [1, [-1, 0]], [4, [-1, -1]]],
-    )
-    def test_shift_non_empty_array(self, data, periods, indices):
-        # https://github.com/pandas-dev/pandas/issues/23911
-        subset = data[:2]
-        result = subset.shift(periods)
-        expected = subset.take(indices, allow_fill=True)
-        tm.assert_extension_array_equal(result, expected)
-
-    @pytest.mark.parametrize("periods", [-4, -1, 0, 1, 4])
-    def test_shift_empty_array(self, data, periods):
-        # https://github.com/pandas-dev/pandas/issues/23911
-        empty = data[:0]
-        result = empty.shift(periods)
-        expected = empty
-        tm.assert_extension_array_equal(result, expected)
-
-    def test_shift_zero_copies(self, data):
-        # GH#31502
-        result = data.shift(0)
-        assert result is not data
-
-        result = data[:0].shift(2)
-        assert result is not data
-
-    def test_shift_fill_value(self, data):
-        arr = data[:4]
-        fill_value = data[0]
-        result = arr.shift(1, fill_value=fill_value)
-        expected = data.take([0, 0, 1, 2])
-        tm.assert_extension_array_equal(result, expected)
-
-        result = arr.shift(-2, fill_value=fill_value)
-        expected = data.take([2, 3, 0, 0])
-        tm.assert_extension_array_equal(result, expected)
-
-    def test_not_hashable(self, data):
-        # We are in general mutable, so not hashable
-        with pytest.raises(TypeError, match="unhashable type"):
-            hash(data)
-
-    def test_hash_pandas_object_works(self, data, as_frame):
-        # https://github.com/pandas-dev/pandas/issues/23066
-        data = pd.Series(data)
-        if as_frame:
-            data = data.to_frame()
-        a = pd.util.hash_pandas_object(data)
-        b = pd.util.hash_pandas_object(data)
-        tm.assert_equal(a, b)
-
-    def test_searchsorted(self, data_for_sorting, as_series):
-        if data_for_sorting.dtype._is_boolean:
-            return self._test_searchsorted_bool_dtypes(data_for_sorting, as_series)
-
-        b, c, a = data_for_sorting
-        arr = data_for_sorting.take([2, 0, 1])  # to get [a, b, c]
-
-        if as_series:
-            arr = pd.Series(arr)
-        assert arr.searchsorted(a) == 0
-        assert arr.searchsorted(a, side="right") == 1
-
-        assert arr.searchsorted(b) == 1
-        assert arr.searchsorted(b, side="right") == 2
-
-        assert arr.searchsorted(c) == 2
-        assert arr.searchsorted(c, side="right") == 3
-
-        result = arr.searchsorted(arr.take([0, 2]))
-        expected = np.array([0, 2], dtype=np.intp)
-
-        tm.assert_numpy_array_equal(result, expected)
-
-        # sorter
-        sorter = np.array([1, 2, 0])
-        assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
-
-    def _test_searchsorted_bool_dtypes(self, data_for_sorting, as_series):
-        # We call this from test_searchsorted in cases where we have a
-        #  boolean-like dtype. The non-bool test assumes we have more than 2
-        #  unique values.
-        dtype = data_for_sorting.dtype
-        data_for_sorting = pd.array([True, False], dtype=dtype)
-        b, a = data_for_sorting
-        arr = type(data_for_sorting)._from_sequence([a, b])
-
-        if as_series:
-            arr = pd.Series(arr)
-        assert arr.searchsorted(a) == 0
-        assert arr.searchsorted(a, side="right") == 1
-
-        assert arr.searchsorted(b) == 1
-        assert arr.searchsorted(b, side="right") == 2
-
-        result = arr.searchsorted(arr.take([0, 1]))
-        expected = np.array([0, 1], dtype=np.intp)
-
-        tm.assert_numpy_array_equal(result, expected)
-
-        # sorter
-        sorter = np.array([1, 0])
-        assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
-
-    def test_where_series(self, data, na_value, as_frame):
-        assert data[0] != data[1]
-        cls = type(data)
-        a, b = data[:2]
-
-        orig = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
-        ser = orig.copy()
-        cond = np.array([True, True, False, False])
-
-        if as_frame:
-            ser = ser.to_frame(name="a")
-            cond = cond.reshape(-1, 1)
-
-        result = ser.where(cond)
-        expected = pd.Series(
-            cls._from_sequence([a, a, na_value, na_value], dtype=data.dtype)
-        )
-
-        if as_frame:
-            expected = expected.to_frame(name="a")
-        tm.assert_equal(result, expected)
-
-        ser.mask(~cond, inplace=True)
-        tm.assert_equal(ser, expected)
-
-        # array other
-        ser = orig.copy()
-        if as_frame:
-            ser = ser.to_frame(name="a")
-        cond = np.array([True, False, True, True])
-        other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
-        if as_frame:
-            other = pd.DataFrame({"a": other})
-            cond = pd.DataFrame({"a": cond})
-        result = ser.where(cond, other)
-        expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
-        if as_frame:
-            expected = expected.to_frame(name="a")
-        tm.assert_equal(result, expected)
-
-        ser.mask(~cond, other, inplace=True)
-        tm.assert_equal(ser, expected)
-
-    @pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
-    def test_repeat(self, data, repeats, as_series, use_numpy):
-        arr = type(data)._from_sequence(data[:3], dtype=data.dtype)
-        if as_series:
-            arr = pd.Series(arr)
-
-        result = np.repeat(arr, repeats) if use_numpy else arr.repeat(repeats)
-
-        repeats = [repeats] * 3 if isinstance(repeats, int) else repeats
-        expected = [x for x, n in zip(arr, repeats) for _ in range(n)]
-        expected = type(data)._from_sequence(expected, dtype=data.dtype)
-        if as_series:
-            expected = pd.Series(expected, index=arr.index.repeat(repeats))
-
-        tm.assert_equal(result, expected)
-
-    @pytest.mark.parametrize(
-        "repeats, kwargs, error, msg",
-        [
-            (2, {"axis": 1}, ValueError, "axis"),
-            (-1, {}, ValueError, "negative"),
-            ([1, 2], {}, ValueError, "shape"),
-            (2, {"foo": "bar"}, TypeError, "'foo'"),
-        ],
-    )
-    def test_repeat_raises(self, data, repeats, kwargs, error, msg, use_numpy):
-        with pytest.raises(error, match=msg):
-            if use_numpy:
-                np.repeat(data, repeats, **kwargs)
-            else:
-                data.repeat(repeats, **kwargs)
-
-    def test_delete(self, data):
-        result = data.delete(0)
-        expected = data[1:]
-        tm.assert_extension_array_equal(result, expected)
-
-        result = data.delete([1, 3])
-        expected = data._concat_same_type([data[[0]], data[[2]], data[4:]])
-        tm.assert_extension_array_equal(result, expected)
-
-    def test_insert(self, data):
-        # insert at the beginning
-        result = data[1:].insert(0, data[0])
-        tm.assert_extension_array_equal(result, data)
-
-        result = data[1:].insert(-len(data[1:]), data[0])
-        tm.assert_extension_array_equal(result, data)
-
-        # insert at the middle
-        result = data[:-1].insert(4, data[-1])
-
-        taker = np.arange(len(data))
-        taker[5:] = taker[4:-1]
-        taker[4] = len(data) - 1
-        expected = data.take(taker)
-        tm.assert_extension_array_equal(result, expected)
-
-    def test_insert_invalid(self, data, invalid_scalar):
-        item = invalid_scalar
-
-        with pytest.raises((TypeError, ValueError)):
-            data.insert(0, item)
-
-        with pytest.raises((TypeError, ValueError)):
-            data.insert(4, item)
-
-        with pytest.raises((TypeError, ValueError)):
-            data.insert(len(data) - 1, item)
-
-    def test_insert_invalid_loc(self, data):
-        ub = len(data)
-
-        with pytest.raises(IndexError):
-            data.insert(ub + 1, data[0])
-
-        with pytest.raises(IndexError):
-            data.insert(-ub - 1, data[0])
-
-        with pytest.raises(TypeError):
-            # we expect TypeError here instead of IndexError to match np.insert
-            data.insert(1.5, data[0])
-
-    @pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
-    def test_equals(self, data, na_value, as_series, box):
-        data2 = type(data)._from_sequence([data[0]] * len(data), dtype=data.dtype)
-        data_na = type(data)._from_sequence([na_value] * len(data), dtype=data.dtype)
-
-        data = tm.box_expected(data, box, transpose=False)
-        data2 = tm.box_expected(data2, box, transpose=False)
-        data_na = tm.box_expected(data_na, box, transpose=False)
-
-        # we are asserting with `is True/False` explicitly, to test that the
-        # result is an actual Python bool, and not something "truthy"
-
-        assert data.equals(data) is True
-        assert data.equals(data.copy()) is True
-
-        # unequal other data
-        assert data.equals(data2) is False
-        assert data.equals(data_na) is False
-
-        # different length
-        assert data[:2].equals(data[:3]) is False
-
-        # empty are equal
-        assert data[:0].equals(data[:0]) is True
-
-        # other types
-        assert data.equals(None) is False
-        assert data[[0]].equals(data[0]) is False
-
-    def test_equals_same_data_different_object(self, data):
-        # https://github.com/pandas-dev/pandas/issues/34660
-        assert pd.Series(data).equals(pd.Series(data))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_constructors.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_constructors.py
deleted file mode 100644
index 91ec1b2475cde0af024cf7d0267316cf1a26ef1d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_constructors.py
+++ /dev/null
@@ -1,860 +0,0 @@
-from datetime import (
-    date,
-    datetime,
-)
-import itertools
-
-import numpy as np
-import pytest
-
-from pandas.compat import pa_version_under7p0
-
-from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
-
-import pandas as pd
-from pandas import (
-    Index,
-    MultiIndex,
-    Series,
-    Timestamp,
-    date_range,
-)
-import pandas._testing as tm
-
-
-def test_constructor_single_level():
-    result = MultiIndex(
-        levels=[["foo", "bar", "baz", "qux"]], codes=[[0, 1, 2, 3]], names=["first"]
-    )
-    assert isinstance(result, MultiIndex)
-    expected = Index(["foo", "bar", "baz", "qux"], name="first")
-    tm.assert_index_equal(result.levels[0], expected)
-    assert result.names == ["first"]
-
-
-def test_constructor_no_levels():
-    msg = "non-zero number of levels/codes"
-    with pytest.raises(ValueError, match=msg):
-        MultiIndex(levels=[], codes=[])
-
-    msg = "Must pass both levels and codes"
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex(levels=[])
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex(codes=[])
-
-
-def test_constructor_nonhashable_names():
-    # GH 20527
-    levels = [[1, 2], ["one", "two"]]
-    codes = [[0, 0, 1, 1], [0, 1, 0, 1]]
-    names = (["foo"], ["bar"])
-    msg = r"MultiIndex\.name must be a hashable type"
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex(levels=levels, codes=codes, names=names)
-
-    # With .rename()
-    mi = MultiIndex(
-        levels=[[1, 2], ["one", "two"]],
-        codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
-        names=("foo", "bar"),
-    )
-    renamed = [["fooo"], ["barr"]]
-    with pytest.raises(TypeError, match=msg):
-        mi.rename(names=renamed)
-
-    # With .set_names()
-    with pytest.raises(TypeError, match=msg):
-        mi.set_names(names=renamed)
-
-
-def test_constructor_mismatched_codes_levels(idx):
-    codes = [np.array([1]), np.array([2]), np.array([3])]
-    levels = ["a"]
-
-    msg = "Length of levels and codes must be the same"
-    with pytest.raises(ValueError, match=msg):
-        MultiIndex(levels=levels, codes=codes)
-
-    length_error = (
-        r"On level 0, code max \(3\) >= length of level \(1\)\. "
-        "NOTE: this index is in an inconsistent state"
-    )
-    label_error = r"Unequal code lengths: \[4, 2\]"
-    code_value_error = r"On level 0, code value \(-2\) < -1"
-
-    # important to check that it's looking at the right thing.
-    with pytest.raises(ValueError, match=length_error):
-        MultiIndex(levels=[["a"], ["b"]], codes=[[0, 1, 2, 3], [0, 3, 4, 1]])
-
-    with pytest.raises(ValueError, match=label_error):
-        MultiIndex(levels=[["a"], ["b"]], codes=[[0, 0, 0, 0], [0, 0]])
-
-    # external API
-    with pytest.raises(ValueError, match=length_error):
-        idx.copy().set_levels([["a"], ["b"]])
-
-    with pytest.raises(ValueError, match=label_error):
-        idx.copy().set_codes([[0, 0, 0, 0], [0, 0]])
-
-    # test set_codes with verify_integrity=False
-    # the setting should not raise any value error
-    idx.copy().set_codes(codes=[[0, 0, 0, 0], [0, 0]], verify_integrity=False)
-
-    # code value smaller than -1
-    with pytest.raises(ValueError, match=code_value_error):
-        MultiIndex(levels=[["a"], ["b"]], codes=[[0, -2], [0, 0]])
-
-
-def test_na_levels():
-    # GH26408
-    # test if codes are re-assigned value -1 for levels
-    # with missing values (NaN, NaT, None)
-    result = MultiIndex(
-        levels=[[np.nan, None, pd.NaT, 128, 2]], codes=[[0, -1, 1, 2, 3, 4]]
-    )
-    expected = MultiIndex(
-        levels=[[np.nan, None, pd.NaT, 128, 2]], codes=[[-1, -1, -1, -1, 3, 4]]
-    )
-    tm.assert_index_equal(result, expected)
-
-    result = MultiIndex(
-        levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[0, -1, 1, 2, 3, 4]]
-    )
-    expected = MultiIndex(
-        levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[-1, -1, 1, -1, 3, -1]]
-    )
-    tm.assert_index_equal(result, expected)
-
-    # verify set_levels and set_codes
-    result = MultiIndex(
-        levels=[[1, 2, 3, 4, 5]], codes=[[0, -1, 1, 2, 3, 4]]
-    ).set_levels([[np.nan, "s", pd.NaT, 128, None]])
-    tm.assert_index_equal(result, expected)
-
-    result = MultiIndex(
-        levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[1, 2, 2, 2, 2, 2]]
-    ).set_codes([[0, -1, 1, 2, 3, 4]])
-    tm.assert_index_equal(result, expected)
-
-
-def test_copy_in_constructor():
-    levels = np.array(["a", "b", "c"])
-    codes = np.array([1, 1, 2, 0, 0, 1, 1])
-    val = codes[0]
-    mi = MultiIndex(levels=[levels, levels], codes=[codes, codes], copy=True)
-    assert mi.codes[0][0] == val
-    codes[0] = 15
-    assert mi.codes[0][0] == val
-    val = levels[0]
-    levels[0] = "PANDA"
-    assert mi.levels[0][0] == val
-
-
-# ----------------------------------------------------------------------------
-# from_arrays
-# ----------------------------------------------------------------------------
-def test_from_arrays(idx):
-    arrays = [
-        np.asarray(lev).take(level_codes)
-        for lev, level_codes in zip(idx.levels, idx.codes)
-    ]
-
-    # list of arrays as input
-    result = MultiIndex.from_arrays(arrays, names=idx.names)
-    tm.assert_index_equal(result, idx)
-
-    # infer correctly
-    result = MultiIndex.from_arrays([[pd.NaT, Timestamp("20130101")], ["a", "b"]])
-    assert result.levels[0].equals(Index([Timestamp("20130101")]))
-    assert result.levels[1].equals(Index(["a", "b"]))
-
-
-def test_from_arrays_iterator(idx):
-    # GH 18434
-    arrays = [
-        np.asarray(lev).take(level_codes)
-        for lev, level_codes in zip(idx.levels, idx.codes)
-    ]
-
-    # iterator as input
-    result = MultiIndex.from_arrays(iter(arrays), names=idx.names)
-    tm.assert_index_equal(result, idx)
-
-    # invalid iterator input
-    msg = "Input must be a list / sequence of array-likes."
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex.from_arrays(0)
-
-
-def test_from_arrays_tuples(idx):
-    arrays = tuple(
-        tuple(np.asarray(lev).take(level_codes))
-        for lev, level_codes in zip(idx.levels, idx.codes)
-    )
-
-    # tuple of tuples as input
-    result = MultiIndex.from_arrays(arrays, names=idx.names)
-    tm.assert_index_equal(result, idx)
-
-
-@pytest.mark.parametrize(
-    ("idx1", "idx2"),
-    [
-        (
-            pd.period_range("2011-01-01", freq="D", periods=3),
-            pd.period_range("2015-01-01", freq="H", periods=3),
-        ),
-        (
-            date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"),
-            date_range("2015-01-01 10:00", freq="H", periods=3, tz="Asia/Tokyo"),
-        ),
-        (
-            pd.timedelta_range("1 days", freq="D", periods=3),
-            pd.timedelta_range("2 hours", freq="H", periods=3),
-        ),
-    ],
-)
-def test_from_arrays_index_series_period_datetimetz_and_timedelta(idx1, idx2):
-    result = MultiIndex.from_arrays([idx1, idx2])
-    tm.assert_index_equal(result.get_level_values(0), idx1)
-    tm.assert_index_equal(result.get_level_values(1), idx2)
-
-    result2 = MultiIndex.from_arrays([Series(idx1), Series(idx2)])
-    tm.assert_index_equal(result2.get_level_values(0), idx1)
-    tm.assert_index_equal(result2.get_level_values(1), idx2)
-
-    tm.assert_index_equal(result, result2)
-
-
-def test_from_arrays_index_datetimelike_mixed():
-    idx1 = date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern")
-    idx2 = date_range("2015-01-01 10:00", freq="H", periods=3)
-    idx3 = pd.timedelta_range("1 days", freq="D", periods=3)
-    idx4 = pd.period_range("2011-01-01", freq="D", periods=3)
-
-    result = MultiIndex.from_arrays([idx1, idx2, idx3, idx4])
-    tm.assert_index_equal(result.get_level_values(0), idx1)
-    tm.assert_index_equal(result.get_level_values(1), idx2)
-    tm.assert_index_equal(result.get_level_values(2), idx3)
-    tm.assert_index_equal(result.get_level_values(3), idx4)
-
-    result2 = MultiIndex.from_arrays(
-        [Series(idx1), Series(idx2), Series(idx3), Series(idx4)]
-    )
-    tm.assert_index_equal(result2.get_level_values(0), idx1)
-    tm.assert_index_equal(result2.get_level_values(1), idx2)
-    tm.assert_index_equal(result2.get_level_values(2), idx3)
-    tm.assert_index_equal(result2.get_level_values(3), idx4)
-
-    tm.assert_index_equal(result, result2)
-
-
-def test_from_arrays_index_series_categorical():
-    # GH13743
-    idx1 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=False)
-    idx2 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=True)
-
-    result = MultiIndex.from_arrays([idx1, idx2])
-    tm.assert_index_equal(result.get_level_values(0), idx1)
-    tm.assert_index_equal(result.get_level_values(1), idx2)
-
-    result2 = MultiIndex.from_arrays([Series(idx1), Series(idx2)])
-    tm.assert_index_equal(result2.get_level_values(0), idx1)
-    tm.assert_index_equal(result2.get_level_values(1), idx2)
-
-    result3 = MultiIndex.from_arrays([idx1.values, idx2.values])
-    tm.assert_index_equal(result3.get_level_values(0), idx1)
-    tm.assert_index_equal(result3.get_level_values(1), idx2)
-
-
-def test_from_arrays_empty():
-    # 0 levels
-    msg = "Must pass non-zero number of levels/codes"
-    with pytest.raises(ValueError, match=msg):
-        MultiIndex.from_arrays(arrays=[])
-
-    # 1 level
-    result = MultiIndex.from_arrays(arrays=[[]], names=["A"])
-    assert isinstance(result, MultiIndex)
-    expected = Index([], name="A")
-    tm.assert_index_equal(result.levels[0], expected)
-    assert result.names == ["A"]
-
-    # N levels
-    for N in [2, 3]:
-        arrays = [[]] * N
-        names = list("ABC")[:N]
-        result = MultiIndex.from_arrays(arrays=arrays, names=names)
-        expected = MultiIndex(levels=[[]] * N, codes=[[]] * N, names=names)
-        tm.assert_index_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "invalid_sequence_of_arrays",
-    [
-        1,
-        [1],
-        [1, 2],
-        [[1], 2],
-        [1, [2]],
-        "a",
-        ["a"],
-        ["a", "b"],
-        [["a"], "b"],
-        (1,),
-        (1, 2),
-        ([1], 2),
-        (1, [2]),
-        "a",
-        ("a",),
-        ("a", "b"),
-        (["a"], "b"),
-        [(1,), 2],
-        [1, (2,)],
-        [("a",), "b"],
-        ((1,), 2),
-        (1, (2,)),
-        (("a",), "b"),
-    ],
-)
-def test_from_arrays_invalid_input(invalid_sequence_of_arrays):
-    msg = "Input must be a list / sequence of array-likes"
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex.from_arrays(arrays=invalid_sequence_of_arrays)
-
-
-@pytest.mark.parametrize(
-    "idx1, idx2", [([1, 2, 3], ["a", "b"]), ([], ["a", "b"]), ([1, 2, 3], [])]
-)
-def test_from_arrays_different_lengths(idx1, idx2):
-    # see gh-13599
-    msg = "^all arrays must be same length$"
-    with pytest.raises(ValueError, match=msg):
-        MultiIndex.from_arrays([idx1, idx2])
-
-
-def test_from_arrays_respects_none_names():
-    # GH27292
-    a = Series([1, 2, 3], name="foo")
-    b = Series(["a", "b", "c"], name="bar")
-
-    result = MultiIndex.from_arrays([a, b], names=None)
-    expected = MultiIndex(
-        levels=[[1, 2, 3], ["a", "b", "c"]], codes=[[0, 1, 2], [0, 1, 2]], names=None
-    )
-
-    tm.assert_index_equal(result, expected)
-
-
-# ----------------------------------------------------------------------------
-# from_tuples
-# ----------------------------------------------------------------------------
-def test_from_tuples():
-    msg = "Cannot infer number of levels from empty list"
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex.from_tuples([])
-
-    expected = MultiIndex(
-        levels=[[1, 3], [2, 4]], codes=[[0, 1], [0, 1]], names=["a", "b"]
-    )
-
-    # input tuples
-    result = MultiIndex.from_tuples(((1, 2), (3, 4)), names=["a", "b"])
-    tm.assert_index_equal(result, expected)
-
-
-def test_from_tuples_iterator():
-    # GH 18434
-    # input iterator for tuples
-    expected = MultiIndex(
-        levels=[[1, 3], [2, 4]], codes=[[0, 1], [0, 1]], names=["a", "b"]
-    )
-
-    result = MultiIndex.from_tuples(zip([1, 3], [2, 4]), names=["a", "b"])
-    tm.assert_index_equal(result, expected)
-
-    # input non-iterables
-    msg = "Input must be a list / sequence of tuple-likes."
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex.from_tuples(0)
-
-
-def test_from_tuples_empty():
-    # GH 16777
-    result = MultiIndex.from_tuples([], names=["a", "b"])
-    expected = MultiIndex.from_arrays(arrays=[[], []], names=["a", "b"])
-    tm.assert_index_equal(result, expected)
-
-
-def test_from_tuples_index_values(idx):
-    result = MultiIndex.from_tuples(idx)
-    assert (result.values == idx.values).all()
-
-
-def test_tuples_with_name_string():
-    # GH 15110 and GH 14848
-
-    li = [(0, 0, 1), (0, 1, 0), (1, 0, 0)]
-    msg = "Names should be list-like for a MultiIndex"
-    with pytest.raises(ValueError, match=msg):
-        Index(li, name="abc")
-    with pytest.raises(ValueError, match=msg):
-        Index(li, name="a")
-
-
-def test_from_tuples_with_tuple_label():
-    # GH 15457
-    expected = pd.DataFrame(
-        [[2, 1, 2], [4, (1, 2), 3]], columns=["a", "b", "c"]
-    ).set_index(["a", "b"])
-    idx = MultiIndex.from_tuples([(2, 1), (4, (1, 2))], names=("a", "b"))
-    result = pd.DataFrame([2, 3], columns=["c"], index=idx)
-    tm.assert_frame_equal(expected, result)
-
-
-# ----------------------------------------------------------------------------
-# from_product
-# ----------------------------------------------------------------------------
-def test_from_product_empty_zero_levels():
-    # 0 levels
-    msg = "Must pass non-zero number of levels/codes"
-    with pytest.raises(ValueError, match=msg):
-        MultiIndex.from_product([])
-
-
-def test_from_product_empty_one_level():
-    result = MultiIndex.from_product([[]], names=["A"])
-    expected = Index([], name="A")
-    tm.assert_index_equal(result.levels[0], expected)
-    assert result.names == ["A"]
-
-
-@pytest.mark.parametrize(
-    "first, second", [([], []), (["foo", "bar", "baz"], []), ([], ["a", "b", "c"])]
-)
-def test_from_product_empty_two_levels(first, second):
-    names = ["A", "B"]
-    result = MultiIndex.from_product([first, second], names=names)
-    expected = MultiIndex(levels=[first, second], codes=[[], []], names=names)
-    tm.assert_index_equal(result, expected)
-
-
-@pytest.mark.parametrize("N", list(range(4)))
-def test_from_product_empty_three_levels(N):
-    # GH12258
-    names = ["A", "B", "C"]
-    lvl2 = list(range(N))
-    result = MultiIndex.from_product([[], lvl2, []], names=names)
-    expected = MultiIndex(levels=[[], lvl2, []], codes=[[], [], []], names=names)
-    tm.assert_index_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "invalid_input", [1, [1], [1, 2], [[1], 2], "a", ["a"], ["a", "b"], [["a"], "b"]]
-)
-def test_from_product_invalid_input(invalid_input):
-    msg = r"Input must be a list / sequence of iterables|Input must be list-like"
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex.from_product(iterables=invalid_input)
-
-
-def test_from_product_datetimeindex():
-    dt_index = date_range("2000-01-01", periods=2)
-    mi = MultiIndex.from_product([[1, 2], dt_index])
-    etalon = construct_1d_object_array_from_listlike(
-        [
-            (1, Timestamp("2000-01-01")),
-            (1, Timestamp("2000-01-02")),
-            (2, Timestamp("2000-01-01")),
-            (2, Timestamp("2000-01-02")),
-        ]
-    )
-    tm.assert_numpy_array_equal(mi.values, etalon)
-
-
-def test_from_product_rangeindex():
-    # RangeIndex is preserved by factorize, so preserved in levels
-    rng = Index(range(5))
-    other = ["a", "b"]
-    mi = MultiIndex.from_product([rng, other])
-    tm.assert_index_equal(mi._levels[0], rng, exact=True)
-
-
-@pytest.mark.parametrize("ordered", [False, True])
-@pytest.mark.parametrize("f", [lambda x: x, lambda x: Series(x), lambda x: x.values])
-def test_from_product_index_series_categorical(ordered, f):
-    # GH13743
-    first = ["foo", "bar"]
-
-    idx = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=ordered)
-    expected = pd.CategoricalIndex(
-        list("abcaab") + list("abcaab"), categories=list("bac"), ordered=ordered
-    )
-
-    result = MultiIndex.from_product([first, f(idx)])
-    tm.assert_index_equal(result.get_level_values(1), expected)
-
-
-def test_from_product():
-    first = ["foo", "bar", "buz"]
-    second = ["a", "b", "c"]
-    names = ["first", "second"]
-    result = MultiIndex.from_product([first, second], names=names)
-
-    tuples = [
-        ("foo", "a"),
-        ("foo", "b"),
-        ("foo", "c"),
-        ("bar", "a"),
-        ("bar", "b"),
-        ("bar", "c"),
-        ("buz", "a"),
-        ("buz", "b"),
-        ("buz", "c"),
-    ]
-    expected = MultiIndex.from_tuples(tuples, names=names)
-
-    tm.assert_index_equal(result, expected)
-
-
-def test_from_product_iterator():
-    # GH 18434
-    first = ["foo", "bar", "buz"]
-    second = ["a", "b", "c"]
-    names = ["first", "second"]
-    tuples = [
-        ("foo", "a"),
-        ("foo", "b"),
-        ("foo", "c"),
-        ("bar", "a"),
-        ("bar", "b"),
-        ("bar", "c"),
-        ("buz", "a"),
-        ("buz", "b"),
-        ("buz", "c"),
-    ]
-    expected = MultiIndex.from_tuples(tuples, names=names)
-
-    # iterator as input
-    result = MultiIndex.from_product(iter([first, second]), names=names)
-    tm.assert_index_equal(result, expected)
-
-    # Invalid non-iterable input
-    msg = "Input must be a list / sequence of iterables."
-    with pytest.raises(TypeError, match=msg):
-        MultiIndex.from_product(0)
-
-
-@pytest.mark.parametrize(
-    "a, b, expected_names",
-    [
-        (
-            Series([1, 2, 3], name="foo"),
-            Series(["a", "b"], name="bar"),
-            ["foo", "bar"],
-        ),
-        (Series([1, 2, 3], name="foo"), ["a", "b"], ["foo", None]),
-        ([1, 2, 3], ["a", "b"], None),
-    ],
-)
-def test_from_product_infer_names(a, b, expected_names):
-    # GH27292
-    result = MultiIndex.from_product([a, b])
-    expected = MultiIndex(
-        levels=[[1, 2, 3], ["a", "b"]],
-        codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
-        names=expected_names,
-    )
-    tm.assert_index_equal(result, expected)
-
-
-def test_from_product_respects_none_names():
-    # GH27292
-    a = Series([1, 2, 3], name="foo")
-    b = Series(["a", "b"], name="bar")
-
-    result = MultiIndex.from_product([a, b], names=None)
-    expected = MultiIndex(
-        levels=[[1, 2, 3], ["a", "b"]],
-        codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
-        names=None,
-    )
-    tm.assert_index_equal(result, expected)
-
-
-def test_from_product_readonly():
-    # GH#15286 passing read-only array to from_product
-    a = np.array(range(3))
-    b = ["a", "b"]
-    expected = MultiIndex.from_product([a, b])
-
-    a.setflags(write=False)
-    result = MultiIndex.from_product([a, b])
-    tm.assert_index_equal(result, expected)
-
-
-def test_create_index_existing_name(idx):
-    # GH11193, when an existing index is passed, and a new name is not
-    # specified, the new index should inherit the previous object name
-    index = idx
-    index.names = ["foo", "bar"]
-    result = Index(index)
-    expected = Index(
-        Index(
-            [
-                ("foo", "one"),
-                ("foo", "two"),
-                ("bar", "one"),
-                ("baz", "two"),
-                ("qux", "one"),
-                ("qux", "two"),
-            ],
-            dtype="object",
-        )
-    )
-    tm.assert_index_equal(result, expected)
-
-    result = Index(index, name="A")
-    expected = Index(
-        Index(
-            [
-                ("foo", "one"),
-                ("foo", "two"),
-                ("bar", "one"),
-                ("baz", "two"),
-                ("qux", "one"),
-                ("qux", "two"),
-            ],
-            dtype="object",
-        ),
-        name="A",
-    )
-    tm.assert_index_equal(result, expected)
-
-
-# ----------------------------------------------------------------------------
-# from_frame
-# ----------------------------------------------------------------------------
-def test_from_frame():
-    # GH 22420
-    df = pd.DataFrame(
-        [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]], columns=["L1", "L2"]
-    )
-    expected = MultiIndex.from_tuples(
-        [("a", "a"), ("a", "b"), ("b", "a"), ("b", "b")], names=["L1", "L2"]
-    )
-    result = MultiIndex.from_frame(df)
-    tm.assert_index_equal(expected, result)
-
-
-@pytest.mark.skipif(pa_version_under7p0, reason="minimum pyarrow not installed")
-def test_from_frame_missing_values_multiIndex():
-    # GH 39984
-    import pyarrow as pa
-
-    df = pd.DataFrame(
-        {
-            "a": Series([1, 2, None], dtype="Int64"),
-            "b": pd.Float64Dtype().__from_arrow__(pa.array([0.2, np.nan, None])),
-        }
-    )
-    multi_indexed = MultiIndex.from_frame(df)
-    expected = MultiIndex.from_arrays(
-        [
-            Series([1, 2, None]).astype("Int64"),
-            pd.Float64Dtype().__from_arrow__(pa.array([0.2, np.nan, None])),
-        ],
-        names=["a", "b"],
-    )
-    tm.assert_index_equal(multi_indexed, expected)
-
-
-@pytest.mark.parametrize(
-    "non_frame",
-    [
-        Series([1, 2, 3, 4]),
-        [1, 2, 3, 4],
-        [[1, 2], [3, 4], [5, 6]],
-        Index([1, 2, 3, 4]),
-        np.array([[1, 2], [3, 4], [5, 6]]),
-        27,
-    ],
-)
-def test_from_frame_error(non_frame):
-    # GH 22420
-    with pytest.raises(TypeError, match="Input must be a DataFrame"):
-        MultiIndex.from_frame(non_frame)
-
-
-def test_from_frame_dtype_fidelity():
-    # GH 22420
-    df = pd.DataFrame(
-        {
-            "dates": date_range("19910905", periods=6, tz="US/Eastern"),
-            "a": [1, 1, 1, 2, 2, 2],
-            "b": pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True),
-            "c": ["x", "x", "y", "z", "x", "y"],
-        }
-    )
-    original_dtypes = df.dtypes.to_dict()
-
-    expected_mi = MultiIndex.from_arrays(
-        [
-            date_range("19910905", periods=6, tz="US/Eastern"),
-            [1, 1, 1, 2, 2, 2],
-            pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True),
-            ["x", "x", "y", "z", "x", "y"],
-        ],
-        names=["dates", "a", "b", "c"],
-    )
-    mi = MultiIndex.from_frame(df)
-    mi_dtypes = {name: mi.levels[i].dtype for i, name in enumerate(mi.names)}
-
-    tm.assert_index_equal(expected_mi, mi)
-    assert original_dtypes == mi_dtypes
-
-
-@pytest.mark.parametrize(
-    "names_in,names_out", [(None, [("L1", "x"), ("L2", "y")]), (["x", "y"], ["x", "y"])]
-)
-def test_from_frame_valid_names(names_in, names_out):
-    # GH 22420
-    df = pd.DataFrame(
-        [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]],
-        columns=MultiIndex.from_tuples([("L1", "x"), ("L2", "y")]),
-    )
-    mi = MultiIndex.from_frame(df, names=names_in)
-    assert mi.names == names_out
-
-
-@pytest.mark.parametrize(
-    "names,expected_error_msg",
-    [
-        ("bad_input", "Names should be list-like for a MultiIndex"),
-        (["a", "b", "c"], "Length of names must match number of levels in MultiIndex"),
-    ],
-)
-def test_from_frame_invalid_names(names, expected_error_msg):
-    # GH 22420
-    df = pd.DataFrame(
-        [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]],
-        columns=MultiIndex.from_tuples([("L1", "x"), ("L2", "y")]),
-    )
-    with pytest.raises(ValueError, match=expected_error_msg):
-        MultiIndex.from_frame(df, names=names)
-
-
-def test_index_equal_empty_iterable():
-    # #16844
-    a = MultiIndex(levels=[[], []], codes=[[], []], names=["a", "b"])
-    b = MultiIndex.from_arrays(arrays=[[], []], names=["a", "b"])
-    tm.assert_index_equal(a, b)
-
-
-def test_raise_invalid_sortorder():
-    # Test that the MultiIndex constructor raise when a incorrect sortorder is given
-    # GH#28518
-
-    levels = [[0, 1], [0, 1, 2]]
-
-    # Correct sortorder
-    MultiIndex(
-        levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], sortorder=2
-    )
-
-    with pytest.raises(ValueError, match=r".* sortorder 2 with lexsort_depth 1.*"):
-        MultiIndex(
-            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]], sortorder=2
-        )
-
-    with pytest.raises(ValueError, match=r".* sortorder 1 with lexsort_depth 0.*"):
-        MultiIndex(
-            levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]], sortorder=1
-        )
-
-
-def test_datetimeindex():
-    idx1 = pd.DatetimeIndex(
-        ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"] * 2, tz="Asia/Tokyo"
-    )
-    idx2 = date_range("2010/01/01", periods=6, freq="M", tz="US/Eastern")
-    idx = MultiIndex.from_arrays([idx1, idx2])
-
-    expected1 = pd.DatetimeIndex(
-        ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"], tz="Asia/Tokyo"
-    )
-
-    tm.assert_index_equal(idx.levels[0], expected1)
-    tm.assert_index_equal(idx.levels[1], idx2)
-
-    # from datetime combos
-    # GH 7888
-    date1 = np.datetime64("today")
-    date2 = datetime.today()
-    date3 = Timestamp.today()
-
-    for d1, d2 in itertools.product([date1, date2, date3], [date1, date2, date3]):
-        index = MultiIndex.from_product([[d1], [d2]])
-        assert isinstance(index.levels[0], pd.DatetimeIndex)
-        assert isinstance(index.levels[1], pd.DatetimeIndex)
-
-    # but NOT date objects, matching Index behavior
-    date4 = date.today()
-    index = MultiIndex.from_product([[date4], [date2]])
-    assert not isinstance(index.levels[0], pd.DatetimeIndex)
-    assert isinstance(index.levels[1], pd.DatetimeIndex)
-
-
-def test_constructor_with_tz():
-    index = pd.DatetimeIndex(
-        ["2013/01/01 09:00", "2013/01/02 09:00"], name="dt1", tz="US/Pacific"
-    )
-    columns = pd.DatetimeIndex(
-        ["2014/01/01 09:00", "2014/01/02 09:00"], name="dt2", tz="Asia/Tokyo"
-    )
-
-    result = MultiIndex.from_arrays([index, columns])
-
-    assert result.names == ["dt1", "dt2"]
-    tm.assert_index_equal(result.levels[0], index)
-    tm.assert_index_equal(result.levels[1], columns)
-
-    result = MultiIndex.from_arrays([Series(index), Series(columns)])
-
-    assert result.names == ["dt1", "dt2"]
-    tm.assert_index_equal(result.levels[0], index)
-    tm.assert_index_equal(result.levels[1], columns)
-
-
-def test_multiindex_inference_consistency():
-    # check that inference behavior matches the base class
-
-    v = date.today()
-
-    arr = [v, v]
-
-    idx = Index(arr)
-    assert idx.dtype == object
-
-    mi = MultiIndex.from_arrays([arr])
-    lev = mi.levels[0]
-    assert lev.dtype == object
-
-    mi = MultiIndex.from_product([arr])
-    lev = mi.levels[0]
-    assert lev.dtype == object
-
-    mi = MultiIndex.from_tuples([(x,) for x in arr])
-    lev = mi.levels[0]
-    assert lev.dtype == object
-
-
-def test_dtype_representation():
-    # GH#46900
-    pmidx = MultiIndex.from_arrays([[1], ["a"]], names=[("a", "b"), ("c", "d")])
-    result = pmidx.dtypes
-    expected = Series(
-        ["int64", "object"], index=MultiIndex.from_tuples([("a", "b"), ("c", "d")])
-    )
-    tm.assert_series_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/xml/test_xml.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/xml/test_xml.py
deleted file mode 100644
index 88655483800eea56a822272db9c05afa73b8473c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/xml/test_xml.py
+++ /dev/null
@@ -1,2082 +0,0 @@
-from __future__ import annotations
-
-from io import (
-    BytesIO,
-    StringIO,
-)
-from lzma import LZMAError
-import os
-from tarfile import ReadError
-from urllib.error import HTTPError
-from xml.etree.ElementTree import ParseError
-from zipfile import BadZipFile
-
-import numpy as np
-import pytest
-
-from pandas.compat._optional import import_optional_dependency
-from pandas.errors import (
-    EmptyDataError,
-    ParserError,
-)
-import pandas.util._test_decorators as td
-
-import pandas as pd
-from pandas import (
-    NA,
-    DataFrame,
-    Series,
-)
-import pandas._testing as tm
-from pandas.core.arrays import (
-    ArrowStringArray,
-    StringArray,
-)
-
-from pandas.io.common import get_handle
-from pandas.io.xml import read_xml
-
-# CHECK LIST
-
-# [x] - ValueError: "Values for parser can only be lxml or etree."
-
-# etree
-# [X] - ImportError: "lxml not found, please install or use the etree parser."
-# [X] - TypeError: "expected str, bytes or os.PathLike object, not NoneType"
-# [X] - ValueError: "Either element or attributes can be parsed not both."
-# [X] - ValueError: "xpath does not return any nodes..."
-# [X] - SyntaxError: "You have used an incorrect or unsupported XPath"
-# [X] - ValueError: "names does not match length of child elements in xpath."
-# [X] - TypeError: "...is not a valid type for names"
-# [X] - ValueError: "To use stylesheet, you need lxml installed..."
-# []  - URLError: (GENERAL ERROR WITH HTTPError AS SUBCLASS)
-# [X] - HTTPError: "HTTP Error 404: Not Found"
-# []  - OSError: (GENERAL ERROR WITH FileNotFoundError AS SUBCLASS)
-# [X] - FileNotFoundError: "No such file or directory"
-# []  - ParseError    (FAILSAFE CATCH ALL FOR VERY COMPLEX XML)
-# [X] - UnicodeDecodeError: "'utf-8' codec can't decode byte 0xe9..."
-# [X] - UnicodeError: "UTF-16 stream does not start with BOM"
-# [X] - BadZipFile: "File is not a zip file"
-# [X] - OSError: "Invalid data stream"
-# [X] - LZMAError: "Input format not supported by decoder"
-# [X] - ValueError: "Unrecognized compression type"
-# [X] - PermissionError: "Forbidden"
-
-# lxml
-# [X] - ValueError: "Either element or attributes can be parsed not both."
-# [X] - AttributeError: "__enter__"
-# [X] - XSLTApplyError: "Cannot resolve URI"
-# [X] - XSLTParseError: "document is not a stylesheet"
-# [X] - ValueError: "xpath does not return any nodes."
-# [X] - XPathEvalError: "Invalid expression"
-# []  - XPathSyntaxError: (OLD VERSION IN lxml FOR XPATH ERRORS)
-# [X] - TypeError: "empty namespace prefix is not supported in XPath"
-# [X] - ValueError: "names does not match length of child elements in xpath."
-# [X] - TypeError: "...is not a valid type for names"
-# [X] - LookupError: "unknown encoding"
-# []  - URLError: (USUALLY DUE TO NETWORKING)
-# [X  - HTTPError: "HTTP Error 404: Not Found"
-# [X] - OSError: "failed to load external entity"
-# [X] - XMLSyntaxError: "Start tag expected, '<' not found"
-# []  - ParserError: (FAILSAFE CATCH ALL FOR VERY COMPLEX XML
-# [X] - ValueError: "Values for parser can only be lxml or etree."
-# [X] - UnicodeDecodeError: "'utf-8' codec can't decode byte 0xe9..."
-# [X] - UnicodeError: "UTF-16 stream does not start with BOM"
-# [X] - BadZipFile: "File is not a zip file"
-# [X] - OSError: "Invalid data stream"
-# [X] - LZMAError: "Input format not supported by decoder"
-# [X] - ValueError: "Unrecognized compression type"
-# [X] - PermissionError: "Forbidden"
-
-geom_df = DataFrame(
-    {
-        "shape": ["square", "circle", "triangle"],
-        "degrees": [360, 360, 180],
-        "sides": [4, np.nan, 3],
-    }
-)
-
-xml_default_nmsp = """\
-<?xml version='1.0' encoding='utf-8'?>
-<data xmlns="http://example.com">
-  <row>
-    <shape>square</shape>
-    <degrees>360</degrees>
-    <sides>4</sides>
-  </row>
-  <row>
-    <shape>circle</shape>
-    <degrees>360</degrees>
-    <sides/>
-  </row>
-  <row>
-    <shape>triangle</shape>
-    <degrees>180</degrees>
-    <sides>3</sides>
-  </row>
-</data>"""
-
-xml_prefix_nmsp = """\
-<?xml version='1.0' encoding='utf-8'?>
-<doc:data xmlns:doc="http://example.com">
-  <doc:row>
-    <doc:shape>square</doc:shape>
-    <doc:degrees>360</doc:degrees>
-    <doc:sides>4.0</doc:sides>
-  </doc:row>
-  <doc:row>
-    <doc:shape>circle</doc:shape>
-    <doc:degrees>360</doc:degrees>
-    <doc:sides/>
-  </doc:row>
-  <doc:row>
-    <doc:shape>triangle</doc:shape>
-    <doc:degrees>180</doc:degrees>
-    <doc:sides>3.0</doc:sides>
-  </doc:row>
-</doc:data>"""
-
-
-df_kml = DataFrame(
-    {
-        "id": {
-            0: "ID_00001",
-            1: "ID_00002",
-            2: "ID_00003",
-            3: "ID_00004",
-            4: "ID_00005",
-        },
-        "name": {
-            0: "Blue Line (Forest Park)",
-            1: "Red, Purple Line",
-            2: "Red, Purple Line",
-            3: "Red, Purple Line",
-            4: "Red, Purple Line",
-        },
-        "styleUrl": {
-            0: "#LineStyle01",
-            1: "#LineStyle01",
-            2: "#LineStyle01",
-            3: "#LineStyle01",
-            4: "#LineStyle01",
-        },
-        "extrude": {0: 0, 1: 0, 2: 0, 3: 0, 4: 0},
-        "altitudeMode": {
-            0: "clampedToGround",
-            1: "clampedToGround",
-            2: "clampedToGround",
-            3: "clampedToGround",
-            4: "clampedToGround",
-        },
-        "coordinates": {
-            0: (
-                "-87.77678526964958,41.8708863930319,0 "
-                "-87.77826234150609,41.87097820122218,0 "
-                "-87.78251583439344,41.87130129991005,0 "
-                "-87.78418294588424,41.87145055520308,0 "
-                "-87.7872369165933,41.8717239119163,0 "
-                "-87.79160214925886,41.87210797280065,0"
-            ),
-            1: (
-                "-87.65758750947528,41.96427269188822,0 "
-                "-87.65802133507393,41.96581929055245,0 "
-                "-87.65819033925305,41.96621846093642,0 "
-                "-87.6583189819129,41.96650362897086,0 "
-                "-87.65835858701473,41.96669002089185,0 "
-                "-87.65838428411853,41.96688150295095,0 "
-                "-87.65842208882658,41.96745896091846,0 "
-                "-87.65846556843937,41.9683761425439,0 "
-                "-87.65849296214573,41.96913893870342,0"
-            ),
-            2: (
-                "-87.65492939166126,41.95377494531437,0 "
-                "-87.65557043199591,41.95376544118533,0 "
-                "-87.65606302030132,41.95376391658746,0 "
-                "-87.65623502146268,41.95377379126367,0 "
-                "-87.65634748981634,41.95380103566435,0 "
-                "-87.65646537904269,41.95387703994676,0 "
-                "-87.65656532461145,41.95396622645799,0 "
-                "-87.65664760856414,41.95404201996044,0 "
-                "-87.65671750555913,41.95416647054043,0 "
-                "-87.65673983607117,41.95429949810849,0 "
-                "-87.65673866475777,41.95441024240925,0 "
-                "-87.6567690255541,41.95490657227902,0 "
-                "-87.65683672482363,41.95692259283837,0 "
-                "-87.6568900886376,41.95861070983142,0 "
-                "-87.65699865558875,41.96181418669004,0 "
-                "-87.65756347177603,41.96397045777844,0 "
-                "-87.65758750947528,41.96427269188822,0"
-            ),
-            3: (
-                "-87.65362593118043,41.94742799535678,0 "
-                "-87.65363554415794,41.94819886386848,0 "
-                "-87.6536456393239,41.95059994675451,0 "
-                "-87.65365831235026,41.95108288489359,0 "
-                "-87.6536604873874,41.9519954657554,0 "
-                "-87.65362592053201,41.95245597302328,0 "
-                "-87.65367158496069,41.95311153649393,0 "
-                "-87.65368468595476,41.9533202828916,0 "
-                "-87.65369271253692,41.95343095587119,0 "
-                "-87.65373335834569,41.95351536301472,0 "
-                "-87.65378605844126,41.95358212680591,0 "
-                "-87.65385067928185,41.95364452823767,0 "
-                "-87.6539390793817,41.95370263886964,0 "
-                "-87.6540786298351,41.95373403675265,0 "
-                "-87.65430648647626,41.9537535411832,0 "
-                "-87.65492939166126,41.95377494531437,0"
-            ),
-            4: (
-                "-87.65345391792157,41.94217681262115,0 "
-                "-87.65342448305786,41.94237224420864,0 "
-                "-87.65339745703922,41.94268217746244,0 "
-                "-87.65337753982941,41.94288140770284,0 "
-                "-87.65336256753105,41.94317369618263,0 "
-                "-87.65338799707138,41.94357253961736,0 "
-                "-87.65340240886648,41.94389158188269,0 "
-                "-87.65341837392448,41.94406444407721,0 "
-                "-87.65342275247338,41.94421065714904,0 "
-                "-87.65347469646018,41.94434829382345,0 "
-                "-87.65351486483024,41.94447699917548,0 "
-                "-87.65353483605053,41.9453896864472,0 "
-                "-87.65361975532807,41.94689193720703,0 "
-                "-87.65362593118043,41.94742799535678,0"
-            ),
-        },
-    }
-)
-
-
-def test_literal_xml_deprecation():
-    # GH 53809
-    pytest.importorskip("lxml")
-    msg = (
-        "Passing literal xml to 'read_xml' is deprecated and "
-        "will be removed in a future version. To read from a "
-        "literal string, wrap it in a 'StringIO' object."
-    )
-
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        read_xml(xml_default_nmsp)
-
-
-@pytest.fixture(params=["rb", "r"])
-def mode(request):
-    return request.param
-
-
-@pytest.fixture(params=[pytest.param("lxml", marks=td.skip_if_no("lxml")), "etree"])
-def parser(request):
-    return request.param
-
-
-def read_xml_iterparse(data, **kwargs):
-    with tm.ensure_clean() as path:
-        with open(path, "w", encoding="utf-8") as f:
-            f.write(data)
-        return read_xml(path, **kwargs)
-
-
-def read_xml_iterparse_comp(comp_path, compression_only, **kwargs):
-    with get_handle(comp_path, "r", compression=compression_only) as handles:
-        with tm.ensure_clean() as path:
-            with open(path, "w", encoding="utf-8") as f:
-                f.write(handles.handle.read())
-            return read_xml(path, **kwargs)
-
-
-# FILE / URL
-
-
-def test_parser_consistency_file(xml_books):
-    pytest.importorskip("lxml")
-    df_file_lxml = read_xml(xml_books, parser="lxml")
-    df_file_etree = read_xml(xml_books, parser="etree")
-
-    df_iter_lxml = read_xml(
-        xml_books,
-        parser="lxml",
-        iterparse={"book": ["category", "title", "year", "author", "price"]},
-    )
-    df_iter_etree = read_xml(
-        xml_books,
-        parser="etree",
-        iterparse={"book": ["category", "title", "year", "author", "price"]},
-    )
-
-    tm.assert_frame_equal(df_file_lxml, df_file_etree)
-    tm.assert_frame_equal(df_file_lxml, df_iter_lxml)
-    tm.assert_frame_equal(df_iter_lxml, df_iter_etree)
-
-
-@pytest.mark.network
-@pytest.mark.single_cpu
-def test_parser_consistency_url(parser, httpserver):
-    httpserver.serve_content(content=xml_default_nmsp)
-
-    df_xpath = read_xml(StringIO(xml_default_nmsp), parser=parser)
-    df_iter = read_xml(
-        BytesIO(xml_default_nmsp.encode()),
-        parser=parser,
-        iterparse={"row": ["shape", "degrees", "sides"]},
-    )
-
-    tm.assert_frame_equal(df_xpath, df_iter)
-
-
-def test_file_like(xml_books, parser, mode):
-    with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:
-        df_file = read_xml(f, parser=parser)
-
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_file, df_expected)
-
-
-def test_file_io(xml_books, parser, mode):
-    with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:
-        xml_obj = f.read()
-
-    df_io = read_xml(
-        (BytesIO(xml_obj) if isinstance(xml_obj, bytes) else StringIO(xml_obj)),
-        parser=parser,
-    )
-
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_io, df_expected)
-
-
-def test_file_buffered_reader_string(xml_books, parser, mode):
-    with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:
-        xml_obj = f.read()
-
-    if mode == "rb":
-        xml_obj = StringIO(xml_obj.decode())
-    elif mode == "r":
-        xml_obj = StringIO(xml_obj)
-
-    df_str = read_xml(xml_obj, parser=parser)
-
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_str, df_expected)
-
-
-def test_file_buffered_reader_no_xml_declaration(xml_books, parser, mode):
-    with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:
-        next(f)
-        xml_obj = f.read()
-
-    if mode == "rb":
-        xml_obj = StringIO(xml_obj.decode())
-    elif mode == "r":
-        xml_obj = StringIO(xml_obj)
-
-    df_str = read_xml(xml_obj, parser=parser)
-
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_str, df_expected)
-
-
-def test_string_charset(parser):
-    txt = "<中文標籤><row><c1>1</c1><c2>2</c2></row></中文標籤>"
-
-    df_str = read_xml(StringIO(txt), parser=parser)
-
-    df_expected = DataFrame({"c1": 1, "c2": 2}, index=[0])
-
-    tm.assert_frame_equal(df_str, df_expected)
-
-
-def test_file_charset(xml_doc_ch_utf, parser):
-    df_file = read_xml(xml_doc_ch_utf, parser=parser)
-
-    df_expected = DataFrame(
-        {
-            "問": [
-                "問  若箇是邪而言破邪 何者是正而道(Sorry, this is Big5 only)申正",
-                "問 既破有得申無得 亦應但破性執申假名以不",
-                "問 既破性申假 亦應但破有申無 若有無兩洗 亦應性假雙破耶",
-            ],
-            "答": [
-                "".join(
-                    [
-                        "答  邪既無量 正亦多途  大略為言不出二種 謂",
-                        "有得與無得 有得是邪須破 無得是正須申\n\t\t故",
-                    ]
-                ),
-                None,
-                "答  不例  有無皆是性 所以須雙破 既分性假異 故有破不破",
-            ],
-            "a": [
-                None,
-                "答 性執是有得 假名是無得  今破有得申無得 即是破性執申假名也",
-                None,
-            ],
-        }
-    )
-
-    tm.assert_frame_equal(df_file, df_expected)
-
-
-def test_file_handle_close(xml_books, parser):
-    with open(xml_books, "rb") as f:
-        read_xml(BytesIO(f.read()), parser=parser)
-
-        assert not f.closed
-
-
-@pytest.mark.parametrize("val", ["", b""])
-def test_empty_string_lxml(val):
-    lxml_etree = pytest.importorskip("lxml.etree")
-
-    msg = "|".join(
-        [
-            "Document is empty",
-            # Seen on Mac with lxml 4.91
-            r"None \(line 0\)",
-        ]
-    )
-    with pytest.raises(lxml_etree.XMLSyntaxError, match=msg):
-        if isinstance(val, str):
-            read_xml(StringIO(val), parser="lxml")
-        else:
-            read_xml(BytesIO(val), parser="lxml")
-
-
-@pytest.mark.parametrize("val", ["", b""])
-def test_empty_string_etree(val):
-    with pytest.raises(ParseError, match="no element found"):
-        if isinstance(val, str):
-            read_xml(StringIO(val), parser="etree")
-        else:
-            read_xml(BytesIO(val), parser="etree")
-
-
-def test_wrong_file_path(parser):
-    msg = (
-        "Passing literal xml to 'read_xml' is deprecated and "
-        "will be removed in a future version. To read from a "
-        "literal string, wrap it in a 'StringIO' object."
-    )
-    filename = os.path.join("data", "html", "books.xml")
-
-    with pytest.raises(
-        FutureWarning,
-        match=msg,
-    ):
-        read_xml(filename, parser=parser)
-
-
-@pytest.mark.network
-@pytest.mark.single_cpu
-def test_url(httpserver, xml_file):
-    pytest.importorskip("lxml")
-    with open(xml_file, encoding="utf-8") as f:
-        httpserver.serve_content(content=f.read())
-        df_url = read_xml(httpserver.url, xpath=".//book[count(*)=4]")
-
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_url, df_expected)
-
-
-@pytest.mark.network
-@pytest.mark.single_cpu
-def test_wrong_url(parser, httpserver):
-    httpserver.serve_content("NOT FOUND", code=404)
-    with pytest.raises(HTTPError, match=("HTTP Error 404: NOT FOUND")):
-        read_xml(httpserver.url, xpath=".//book[count(*)=4]", parser=parser)
-
-
-# CONTENT
-
-
-def test_whitespace(parser):
-    xml = """
-      <data>
-        <row sides=" 4 ">
-          <shape>
-              square
-          </shape>
-          <degrees>&#009;360&#009;</degrees>
-        </row>
-        <row sides=" 0 ">
-          <shape>
-              circle
-          </shape>
-          <degrees>&#009;360&#009;</degrees>
-        </row>
-        <row sides=" 3 ">
-          <shape>
-              triangle
-          </shape>
-          <degrees>&#009;180&#009;</degrees>
-        </row>
-      </data>"""
-
-    df_xpath = read_xml(StringIO(xml), parser=parser, dtype="string")
-
-    df_iter = read_xml_iterparse(
-        xml,
-        parser=parser,
-        iterparse={"row": ["sides", "shape", "degrees"]},
-        dtype="string",
-    )
-
-    df_expected = DataFrame(
-        {
-            "sides": [" 4 ", " 0 ", " 3 "],
-            "shape": [
-                "\n              square\n          ",
-                "\n              circle\n          ",
-                "\n              triangle\n          ",
-            ],
-            "degrees": ["\t360\t", "\t360\t", "\t180\t"],
-        },
-        dtype="string",
-    )
-
-    tm.assert_frame_equal(df_xpath, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-# XPATH
-
-
-def test_empty_xpath_lxml(xml_books):
-    pytest.importorskip("lxml")
-    with pytest.raises(ValueError, match=("xpath does not return any nodes")):
-        read_xml(xml_books, xpath=".//python", parser="lxml")
-
-
-def test_bad_xpath_etree(xml_books):
-    with pytest.raises(
-        SyntaxError, match=("You have used an incorrect or unsupported XPath")
-    ):
-        read_xml(xml_books, xpath=".//[book]", parser="etree")
-
-
-def test_bad_xpath_lxml(xml_books):
-    lxml_etree = pytest.importorskip("lxml.etree")
-
-    with pytest.raises(lxml_etree.XPathEvalError, match=("Invalid expression")):
-        read_xml(xml_books, xpath=".//[book]", parser="lxml")
-
-
-# NAMESPACE
-
-
-def test_default_namespace(parser):
-    df_nmsp = read_xml(
-        StringIO(xml_default_nmsp),
-        xpath=".//ns:row",
-        namespaces={"ns": "http://example.com"},
-        parser=parser,
-    )
-
-    df_iter = read_xml_iterparse(
-        xml_default_nmsp,
-        parser=parser,
-        iterparse={"row": ["shape", "degrees", "sides"]},
-    )
-
-    df_expected = DataFrame(
-        {
-            "shape": ["square", "circle", "triangle"],
-            "degrees": [360, 360, 180],
-            "sides": [4.0, float("nan"), 3.0],
-        }
-    )
-
-    tm.assert_frame_equal(df_nmsp, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_prefix_namespace(parser):
-    df_nmsp = read_xml(
-        StringIO(xml_prefix_nmsp),
-        xpath=".//doc:row",
-        namespaces={"doc": "http://example.com"},
-        parser=parser,
-    )
-    df_iter = read_xml_iterparse(
-        xml_prefix_nmsp, parser=parser, iterparse={"row": ["shape", "degrees", "sides"]}
-    )
-
-    df_expected = DataFrame(
-        {
-            "shape": ["square", "circle", "triangle"],
-            "degrees": [360, 360, 180],
-            "sides": [4.0, float("nan"), 3.0],
-        }
-    )
-
-    tm.assert_frame_equal(df_nmsp, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_consistency_default_namespace():
-    pytest.importorskip("lxml")
-    df_lxml = read_xml(
-        StringIO(xml_default_nmsp),
-        xpath=".//ns:row",
-        namespaces={"ns": "http://example.com"},
-        parser="lxml",
-    )
-
-    df_etree = read_xml(
-        StringIO(xml_default_nmsp),
-        xpath=".//doc:row",
-        namespaces={"doc": "http://example.com"},
-        parser="etree",
-    )
-
-    tm.assert_frame_equal(df_lxml, df_etree)
-
-
-def test_consistency_prefix_namespace():
-    pytest.importorskip("lxml")
-    df_lxml = read_xml(
-        StringIO(xml_prefix_nmsp),
-        xpath=".//doc:row",
-        namespaces={"doc": "http://example.com"},
-        parser="lxml",
-    )
-
-    df_etree = read_xml(
-        StringIO(xml_prefix_nmsp),
-        xpath=".//doc:row",
-        namespaces={"doc": "http://example.com"},
-        parser="etree",
-    )
-
-    tm.assert_frame_equal(df_lxml, df_etree)
-
-
-# PREFIX
-
-
-def test_missing_prefix_with_default_namespace(xml_books, parser):
-    with pytest.raises(ValueError, match=("xpath does not return any nodes")):
-        read_xml(xml_books, xpath=".//Placemark", parser=parser)
-
-
-def test_missing_prefix_definition_etree(kml_cta_rail_lines):
-    with pytest.raises(SyntaxError, match=("you used an undeclared namespace prefix")):
-        read_xml(kml_cta_rail_lines, xpath=".//kml:Placemark", parser="etree")
-
-
-def test_missing_prefix_definition_lxml(kml_cta_rail_lines):
-    lxml_etree = pytest.importorskip("lxml.etree")
-
-    with pytest.raises(lxml_etree.XPathEvalError, match=("Undefined namespace prefix")):
-        read_xml(kml_cta_rail_lines, xpath=".//kml:Placemark", parser="lxml")
-
-
-@pytest.mark.parametrize("key", ["", None])
-def test_none_namespace_prefix(key):
-    pytest.importorskip("lxml")
-    with pytest.raises(
-        TypeError, match=("empty namespace prefix is not supported in XPath")
-    ):
-        read_xml(
-            StringIO(xml_default_nmsp),
-            xpath=".//kml:Placemark",
-            namespaces={key: "http://www.opengis.net/kml/2.2"},
-            parser="lxml",
-        )
-
-
-# ELEMS AND ATTRS
-
-
-def test_file_elems_and_attrs(xml_books, parser):
-    df_file = read_xml(xml_books, parser=parser)
-    df_iter = read_xml(
-        xml_books,
-        parser=parser,
-        iterparse={"book": ["category", "title", "author", "year", "price"]},
-    )
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_file, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_file_only_attrs(xml_books, parser):
-    df_file = read_xml(xml_books, attrs_only=True, parser=parser)
-    df_iter = read_xml(xml_books, parser=parser, iterparse={"book": ["category"]})
-    df_expected = DataFrame({"category": ["cooking", "children", "web"]})
-
-    tm.assert_frame_equal(df_file, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_file_only_elems(xml_books, parser):
-    df_file = read_xml(xml_books, elems_only=True, parser=parser)
-    df_iter = read_xml(
-        xml_books,
-        parser=parser,
-        iterparse={"book": ["title", "author", "year", "price"]},
-    )
-    df_expected = DataFrame(
-        {
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_file, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_elem_and_attrs_only(kml_cta_rail_lines, parser):
-    with pytest.raises(
-        ValueError,
-        match=("Either element or attributes can be parsed not both"),
-    ):
-        read_xml(kml_cta_rail_lines, elems_only=True, attrs_only=True, parser=parser)
-
-
-def test_empty_attrs_only(parser):
-    xml = """
-      <data>
-        <row>
-          <shape sides="4">square</shape>
-          <degrees>360</degrees>
-        </row>
-        <row>
-          <shape sides="0">circle</shape>
-          <degrees>360</degrees>
-        </row>
-        <row>
-          <shape sides="3">triangle</shape>
-          <degrees>180</degrees>
-        </row>
-      </data>"""
-
-    with pytest.raises(
-        ValueError,
-        match=("xpath does not return any nodes or attributes"),
-    ):
-        read_xml(StringIO(xml), xpath="./row", attrs_only=True, parser=parser)
-
-
-def test_empty_elems_only(parser):
-    xml = """
-      <data>
-        <row sides="4" shape="square" degrees="360"/>
-        <row sides="0" shape="circle" degrees="360"/>
-        <row sides="3" shape="triangle" degrees="180"/>
-      </data>"""
-
-    with pytest.raises(
-        ValueError,
-        match=("xpath does not return any nodes or attributes"),
-    ):
-        read_xml(StringIO(xml), xpath="./row", elems_only=True, parser=parser)
-
-
-def test_attribute_centric_xml():
-    pytest.importorskip("lxml")
-    xml = """\
-<?xml version="1.0" encoding="UTF-8"?>
-<TrainSchedule>
-      <Stations>
-         <station Name="Manhattan" coords="31,460,195,498"/>
-         <station Name="Laraway Road" coords="63,409,194,455"/>
-         <station Name="179th St (Orland Park)" coords="0,364,110,395"/>
-         <station Name="153rd St (Orland Park)" coords="7,333,113,362"/>
-         <station Name="143rd St (Orland Park)" coords="17,297,115,330"/>
-         <station Name="Palos Park" coords="128,281,239,303"/>
-         <station Name="Palos Heights" coords="148,257,283,279"/>
-         <station Name="Worth" coords="170,230,248,255"/>
-         <station Name="Chicago Ridge" coords="70,187,208,214"/>
-         <station Name="Oak Lawn" coords="166,159,266,185"/>
-         <station Name="Ashburn" coords="197,133,336,157"/>
-         <station Name="Wrightwood" coords="219,106,340,133"/>
-         <station Name="Chicago Union Sta" coords="220,0,360,43"/>
-      </Stations>
-</TrainSchedule>"""
-
-    df_lxml = read_xml(StringIO(xml), xpath=".//station")
-    df_etree = read_xml(StringIO(xml), xpath=".//station", parser="etree")
-
-    df_iter_lx = read_xml_iterparse(xml, iterparse={"station": ["Name", "coords"]})
-    df_iter_et = read_xml_iterparse(
-        xml, parser="etree", iterparse={"station": ["Name", "coords"]}
-    )
-
-    tm.assert_frame_equal(df_lxml, df_etree)
-    tm.assert_frame_equal(df_iter_lx, df_iter_et)
-
-
-# NAMES
-
-
-def test_names_option_output(xml_books, parser):
-    df_file = read_xml(
-        xml_books, names=["Col1", "Col2", "Col3", "Col4", "Col5"], parser=parser
-    )
-    df_iter = read_xml(
-        xml_books,
-        parser=parser,
-        names=["Col1", "Col2", "Col3", "Col4", "Col5"],
-        iterparse={"book": ["category", "title", "author", "year", "price"]},
-    )
-
-    df_expected = DataFrame(
-        {
-            "Col1": ["cooking", "children", "web"],
-            "Col2": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "Col3": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "Col4": [2005, 2005, 2003],
-            "Col5": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_file, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_repeat_names(parser):
-    xml = """\
-<shapes>
-  <shape type="2D">
-    <name>circle</name>
-    <type>curved</type>
-  </shape>
-  <shape type="3D">
-    <name>sphere</name>
-    <type>curved</type>
-  </shape>
-</shapes>"""
-    df_xpath = read_xml(
-        StringIO(xml),
-        xpath=".//shape",
-        parser=parser,
-        names=["type_dim", "shape", "type_edge"],
-    )
-
-    df_iter = read_xml_iterparse(
-        xml,
-        parser=parser,
-        iterparse={"shape": ["type", "name", "type"]},
-        names=["type_dim", "shape", "type_edge"],
-    )
-
-    df_expected = DataFrame(
-        {
-            "type_dim": ["2D", "3D"],
-            "shape": ["circle", "sphere"],
-            "type_edge": ["curved", "curved"],
-        }
-    )
-
-    tm.assert_frame_equal(df_xpath, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_repeat_values_new_names(parser):
-    xml = """\
-<shapes>
-  <shape>
-    <name>rectangle</name>
-    <family>rectangle</family>
-  </shape>
-  <shape>
-    <name>square</name>
-    <family>rectangle</family>
-  </shape>
-  <shape>
-    <name>ellipse</name>
-    <family>ellipse</family>
-  </shape>
-  <shape>
-    <name>circle</name>
-    <family>ellipse</family>
-  </shape>
-</shapes>"""
-    df_xpath = read_xml(
-        StringIO(xml), xpath=".//shape", parser=parser, names=["name", "group"]
-    )
-
-    df_iter = read_xml_iterparse(
-        xml,
-        parser=parser,
-        iterparse={"shape": ["name", "family"]},
-        names=["name", "group"],
-    )
-
-    df_expected = DataFrame(
-        {
-            "name": ["rectangle", "square", "ellipse", "circle"],
-            "group": ["rectangle", "rectangle", "ellipse", "ellipse"],
-        }
-    )
-
-    tm.assert_frame_equal(df_xpath, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_repeat_elements(parser):
-    xml = """\
-<shapes>
-  <shape>
-    <value item="name">circle</value>
-    <value item="family">ellipse</value>
-    <value item="degrees">360</value>
-    <value item="sides">0</value>
-  </shape>
-  <shape>
-    <value item="name">triangle</value>
-    <value item="family">polygon</value>
-    <value item="degrees">180</value>
-    <value item="sides">3</value>
-  </shape>
-  <shape>
-    <value item="name">square</value>
-    <value item="family">polygon</value>
-    <value item="degrees">360</value>
-    <value item="sides">4</value>
-  </shape>
-</shapes>"""
-    df_xpath = read_xml(
-        StringIO(xml),
-        xpath=".//shape",
-        parser=parser,
-        names=["name", "family", "degrees", "sides"],
-    )
-
-    df_iter = read_xml_iterparse(
-        xml,
-        parser=parser,
-        iterparse={"shape": ["value", "value", "value", "value"]},
-        names=["name", "family", "degrees", "sides"],
-    )
-
-    df_expected = DataFrame(
-        {
-            "name": ["circle", "triangle", "square"],
-            "family": ["ellipse", "polygon", "polygon"],
-            "degrees": [360, 180, 360],
-            "sides": [0, 3, 4],
-        }
-    )
-
-    tm.assert_frame_equal(df_xpath, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_names_option_wrong_length(xml_books, parser):
-    with pytest.raises(ValueError, match=("names does not match length")):
-        read_xml(xml_books, names=["Col1", "Col2", "Col3"], parser=parser)
-
-
-def test_names_option_wrong_type(xml_books, parser):
-    with pytest.raises(TypeError, match=("is not a valid type for names")):
-        read_xml(xml_books, names="Col1, Col2, Col3", parser=parser)
-
-
-# ENCODING
-
-
-def test_wrong_encoding(xml_baby_names, parser):
-    with pytest.raises(UnicodeDecodeError, match=("'utf-8' codec can't decode")):
-        read_xml(xml_baby_names, parser=parser)
-
-
-def test_utf16_encoding(xml_baby_names, parser):
-    with pytest.raises(
-        UnicodeError,
-        match=(
-            "UTF-16 stream does not start with BOM|"
-            "'utf-16-le' codec can't decode byte"
-        ),
-    ):
-        read_xml(xml_baby_names, encoding="UTF-16", parser=parser)
-
-
-def test_unknown_encoding(xml_baby_names, parser):
-    with pytest.raises(LookupError, match=("unknown encoding: UFT-8")):
-        read_xml(xml_baby_names, encoding="UFT-8", parser=parser)
-
-
-def test_ascii_encoding(xml_baby_names, parser):
-    with pytest.raises(UnicodeDecodeError, match=("'ascii' codec can't decode byte")):
-        read_xml(xml_baby_names, encoding="ascii", parser=parser)
-
-
-def test_parser_consistency_with_encoding(xml_baby_names):
-    pytest.importorskip("lxml")
-    df_xpath_lxml = read_xml(xml_baby_names, parser="lxml", encoding="ISO-8859-1")
-    df_xpath_etree = read_xml(xml_baby_names, parser="etree", encoding="iso-8859-1")
-
-    df_iter_lxml = read_xml(
-        xml_baby_names,
-        parser="lxml",
-        encoding="ISO-8859-1",
-        iterparse={"row": ["rank", "malename", "femalename"]},
-    )
-    df_iter_etree = read_xml(
-        xml_baby_names,
-        parser="etree",
-        encoding="ISO-8859-1",
-        iterparse={"row": ["rank", "malename", "femalename"]},
-    )
-
-    tm.assert_frame_equal(df_xpath_lxml, df_xpath_etree)
-    tm.assert_frame_equal(df_xpath_etree, df_iter_etree)
-    tm.assert_frame_equal(df_iter_lxml, df_iter_etree)
-
-
-def test_wrong_encoding_for_lxml():
-    pytest.importorskip("lxml")
-    # GH#45133
-    data = """<data>
-  <row>
-    <a>c</a>
-  </row>
-</data>
-"""
-    with pytest.raises(TypeError, match="encoding None"):
-        read_xml(StringIO(data), parser="lxml", encoding=None)
-
-
-def test_none_encoding_etree():
-    # GH#45133
-    data = """<data>
-  <row>
-    <a>c</a>
-  </row>
-</data>
-"""
-    result = read_xml(StringIO(data), parser="etree", encoding=None)
-    expected = DataFrame({"a": ["c"]})
-    tm.assert_frame_equal(result, expected)
-
-
-# PARSER
-
-
-@td.skip_if_installed("lxml")
-def test_default_parser_no_lxml(xml_books):
-    with pytest.raises(
-        ImportError, match=("lxml not found, please install or use the etree parser.")
-    ):
-        read_xml(xml_books)
-
-
-def test_wrong_parser(xml_books):
-    with pytest.raises(
-        ValueError, match=("Values for parser can only be lxml or etree.")
-    ):
-        read_xml(xml_books, parser="bs4")
-
-
-# STYLESHEET
-
-
-def test_stylesheet_file(kml_cta_rail_lines, xsl_flatten_doc):
-    pytest.importorskip("lxml")
-    df_style = read_xml(
-        kml_cta_rail_lines,
-        xpath=".//k:Placemark",
-        namespaces={"k": "http://www.opengis.net/kml/2.2"},
-        stylesheet=xsl_flatten_doc,
-    )
-
-    df_iter = read_xml(
-        kml_cta_rail_lines,
-        iterparse={
-            "Placemark": [
-                "id",
-                "name",
-                "styleUrl",
-                "extrude",
-                "altitudeMode",
-                "coordinates",
-            ]
-        },
-    )
-
-    tm.assert_frame_equal(df_kml, df_style)
-    tm.assert_frame_equal(df_kml, df_iter)
-
-
-def test_stylesheet_file_like(kml_cta_rail_lines, xsl_flatten_doc, mode):
-    pytest.importorskip("lxml")
-    with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:
-        df_style = read_xml(
-            kml_cta_rail_lines,
-            xpath=".//k:Placemark",
-            namespaces={"k": "http://www.opengis.net/kml/2.2"},
-            stylesheet=f,
-        )
-
-    tm.assert_frame_equal(df_kml, df_style)
-
-
-def test_stylesheet_io(kml_cta_rail_lines, xsl_flatten_doc, mode):
-    # note: By default the bodies of untyped functions are not checked,
-    # consider using --check-untyped-defs
-    pytest.importorskip("lxml")
-    xsl_obj: BytesIO | StringIO  # type: ignore[annotation-unchecked]
-
-    with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:
-        if mode == "rb":
-            xsl_obj = BytesIO(f.read())
-        else:
-            xsl_obj = StringIO(f.read())
-
-    df_style = read_xml(
-        kml_cta_rail_lines,
-        xpath=".//k:Placemark",
-        namespaces={"k": "http://www.opengis.net/kml/2.2"},
-        stylesheet=xsl_obj,
-    )
-
-    tm.assert_frame_equal(df_kml, df_style)
-
-
-def test_stylesheet_buffered_reader(kml_cta_rail_lines, xsl_flatten_doc, mode):
-    pytest.importorskip("lxml")
-    with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:
-        xsl_obj = f.read()
-
-    df_style = read_xml(
-        kml_cta_rail_lines,
-        xpath=".//k:Placemark",
-        namespaces={"k": "http://www.opengis.net/kml/2.2"},
-        stylesheet=xsl_obj,
-    )
-
-    tm.assert_frame_equal(df_kml, df_style)
-
-
-def test_style_charset():
-    pytest.importorskip("lxml")
-    xml = "<中文標籤><row><c1>1</c1><c2>2</c2></row></中文標籤>"
-
-    xsl = """\
-<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
- <xsl:output omit-xml-declaration="yes" indent="yes"/>
- <xsl:strip-space elements="*"/>
-
- <xsl:template match="node()|@*">
-     <xsl:copy>
-       <xsl:apply-templates select="node()|@*"/>
-     </xsl:copy>
- </xsl:template>
-
- <xsl:template match="中文標籤">
-     <根>
-       <xsl:apply-templates />
-     </根>
- </xsl:template>
-
-</xsl:stylesheet>"""
-
-    df_orig = read_xml(StringIO(xml))
-    df_style = read_xml(StringIO(xml), stylesheet=xsl)
-
-    tm.assert_frame_equal(df_orig, df_style)
-
-
-def test_not_stylesheet(kml_cta_rail_lines, xml_books):
-    lxml_etree = pytest.importorskip("lxml.etree")
-
-    with pytest.raises(
-        lxml_etree.XSLTParseError, match=("document is not a stylesheet")
-    ):
-        read_xml(kml_cta_rail_lines, stylesheet=xml_books)
-
-
-def test_incorrect_xsl_syntax(kml_cta_rail_lines):
-    lxml_etree = pytest.importorskip("lxml.etree")
-
-    xsl = """\
-<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform"
-                              xmlns:k="http://www.opengis.net/kml/2.2"/>
-    <xsl:output method="xml" omit-xml-declaration="yes"
-                cdata-section-elements="k:description" indent="yes"/>
-    <xsl:strip-space elements="*"/>
-
-    <xsl:template match="node()|@*">
-     <xsl:copy>
-       <xsl:apply-templates select="node()|@*"/>
-     </xsl:copy>
-    </xsl:template>
-
-    <xsl:template match="k:MultiGeometry|k:LineString">
-        <xsl:apply-templates select='*'/>
-    </xsl:template>
-
-    <xsl:template match="k:description|k:Snippet|k:Style"/>
-</xsl:stylesheet>"""
-
-    with pytest.raises(
-        lxml_etree.XMLSyntaxError, match=("Extra content at the end of the document")
-    ):
-        read_xml(kml_cta_rail_lines, stylesheet=xsl)
-
-
-def test_incorrect_xsl_eval(kml_cta_rail_lines):
-    lxml_etree = pytest.importorskip("lxml.etree")
-
-    xsl = """\
-<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform"
-                              xmlns:k="http://www.opengis.net/kml/2.2">
-    <xsl:output method="xml" omit-xml-declaration="yes"
-                cdata-section-elements="k:description" indent="yes"/>
-    <xsl:strip-space elements="*"/>
-
-    <xsl:template match="node(*)|@*">
-     <xsl:copy>
-       <xsl:apply-templates select="node()|@*"/>
-     </xsl:copy>
-    </xsl:template>
-
-    <xsl:template match="k:MultiGeometry|k:LineString">
-        <xsl:apply-templates select='*'/>
-    </xsl:template>
-
-    <xsl:template match="k:description|k:Snippet|k:Style"/>
-</xsl:stylesheet>"""
-
-    with pytest.raises(lxml_etree.XSLTParseError, match=("failed to compile")):
-        read_xml(kml_cta_rail_lines, stylesheet=xsl)
-
-
-def test_incorrect_xsl_apply(kml_cta_rail_lines):
-    lxml_etree = pytest.importorskip("lxml.etree")
-
-    xsl = """\
-<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
-    <xsl:output method="xml" encoding="utf-8" indent="yes" />
-    <xsl:strip-space elements="*"/>
-
-    <xsl:template match="@*|node()">
-        <xsl:copy>
-            <xsl:copy-of select="document('non_existent.xml')/*"/>
-        </xsl:copy>
-    </xsl:template>
-</xsl:stylesheet>"""
-
-    with pytest.raises(lxml_etree.XSLTApplyError, match=("Cannot resolve URI")):
-        read_xml(kml_cta_rail_lines, stylesheet=xsl)
-
-
-def test_wrong_stylesheet(kml_cta_rail_lines, xml_data_path):
-    xml_etree = pytest.importorskip("lxml.etree")
-
-    xsl = xml_data_path / "flatten.xsl"
-
-    with pytest.raises(
-        xml_etree.XMLSyntaxError,
-        match=("Start tag expected, '<' not found"),
-    ):
-        read_xml(kml_cta_rail_lines, stylesheet=xsl)
-
-
-def test_stylesheet_file_close(kml_cta_rail_lines, xsl_flatten_doc, mode):
-    # note: By default the bodies of untyped functions are not checked,
-    # consider using --check-untyped-defs
-    pytest.importorskip("lxml")
-    xsl_obj: BytesIO | StringIO  # type: ignore[annotation-unchecked]
-
-    with open(xsl_flatten_doc, mode, encoding="utf-8" if mode == "r" else None) as f:
-        if mode == "rb":
-            xsl_obj = BytesIO(f.read())
-        else:
-            xsl_obj = StringIO(f.read())
-
-        read_xml(kml_cta_rail_lines, stylesheet=xsl_obj)
-
-        assert not f.closed
-
-
-def test_stylesheet_with_etree(kml_cta_rail_lines, xsl_flatten_doc):
-    pytest.importorskip("lxml")
-    with pytest.raises(
-        ValueError, match=("To use stylesheet, you need lxml installed")
-    ):
-        read_xml(kml_cta_rail_lines, parser="etree", stylesheet=xsl_flatten_doc)
-
-
-@pytest.mark.parametrize("val", ["", b""])
-def test_empty_stylesheet(val):
-    pytest.importorskip("lxml")
-    msg = (
-        "Passing literal xml to 'read_xml' is deprecated and "
-        "will be removed in a future version. To read from a "
-        "literal string, wrap it in a 'StringIO' object."
-    )
-    kml = os.path.join("data", "xml", "cta_rail_lines.kml")
-
-    with pytest.raises(FutureWarning, match=msg):
-        read_xml(kml, stylesheet=val)
-
-
-# ITERPARSE
-def test_file_like_iterparse(xml_books, parser, mode):
-    with open(xml_books, mode, encoding="utf-8" if mode == "r" else None) as f:
-        if mode == "r" and parser == "lxml":
-            with pytest.raises(
-                TypeError, match=("reading file objects must return bytes objects")
-            ):
-                read_xml(
-                    f,
-                    parser=parser,
-                    iterparse={
-                        "book": ["category", "title", "year", "author", "price"]
-                    },
-                )
-            return None
-        else:
-            df_filelike = read_xml(
-                f,
-                parser=parser,
-                iterparse={"book": ["category", "title", "year", "author", "price"]},
-            )
-
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_filelike, df_expected)
-
-
-def test_file_io_iterparse(xml_books, parser, mode):
-    funcIO = StringIO if mode == "r" else BytesIO
-    with open(
-        xml_books,
-        mode,
-        encoding="utf-8" if mode == "r" else None,
-    ) as f:
-        with funcIO(f.read()) as b:
-            if mode == "r" and parser == "lxml":
-                with pytest.raises(
-                    TypeError, match=("reading file objects must return bytes objects")
-                ):
-                    read_xml(
-                        b,
-                        parser=parser,
-                        iterparse={
-                            "book": ["category", "title", "year", "author", "price"]
-                        },
-                    )
-                return None
-            else:
-                df_fileio = read_xml(
-                    b,
-                    parser=parser,
-                    iterparse={
-                        "book": ["category", "title", "year", "author", "price"]
-                    },
-                )
-
-    df_expected = DataFrame(
-        {
-            "category": ["cooking", "children", "web"],
-            "title": ["Everyday Italian", "Harry Potter", "Learning XML"],
-            "author": ["Giada De Laurentiis", "J K. Rowling", "Erik T. Ray"],
-            "year": [2005, 2005, 2003],
-            "price": [30.00, 29.99, 39.95],
-        }
-    )
-
-    tm.assert_frame_equal(df_fileio, df_expected)
-
-
-@pytest.mark.network
-@pytest.mark.single_cpu
-def test_url_path_error(parser, httpserver, xml_file):
-    with open(xml_file, encoding="utf-8") as f:
-        httpserver.serve_content(content=f.read())
-        with pytest.raises(
-            ParserError, match=("iterparse is designed for large XML files")
-        ):
-            read_xml(
-                httpserver.url,
-                parser=parser,
-                iterparse={"row": ["shape", "degrees", "sides", "date"]},
-            )
-
-
-def test_compression_error(parser, compression_only):
-    with tm.ensure_clean(filename="geom_xml.zip") as path:
-        geom_df.to_xml(path, parser=parser, compression=compression_only)
-
-        with pytest.raises(
-            ParserError, match=("iterparse is designed for large XML files")
-        ):
-            read_xml(
-                path,
-                parser=parser,
-                iterparse={"row": ["shape", "degrees", "sides", "date"]},
-                compression=compression_only,
-            )
-
-
-def test_wrong_dict_type(xml_books, parser):
-    with pytest.raises(TypeError, match="list is not a valid type for iterparse"):
-        read_xml(
-            xml_books,
-            parser=parser,
-            iterparse=["category", "title", "year", "author", "price"],
-        )
-
-
-def test_wrong_dict_value(xml_books, parser):
-    with pytest.raises(
-        TypeError, match="<class 'str'> is not a valid type for value in iterparse"
-    ):
-        read_xml(xml_books, parser=parser, iterparse={"book": "category"})
-
-
-def test_bad_xml(parser):
-    bad_xml = """\
-<?xml version='1.0' encoding='utf-8'?>
-  <row>
-    <shape>square</shape>
-    <degrees>00360</degrees>
-    <sides>4.0</sides>
-    <date>2020-01-01</date>
-   </row>
-  <row>
-    <shape>circle</shape>
-    <degrees>00360</degrees>
-    <sides/>
-    <date>2021-01-01</date>
-  </row>
-  <row>
-    <shape>triangle</shape>
-    <degrees>00180</degrees>
-    <sides>3.0</sides>
-    <date>2022-01-01</date>
-  </row>
-"""
-    with tm.ensure_clean(filename="bad.xml") as path:
-        with open(path, "w", encoding="utf-8") as f:
-            f.write(bad_xml)
-
-        with pytest.raises(
-            SyntaxError,
-            match=(
-                "Extra content at the end of the document|"
-                "junk after document element"
-            ),
-        ):
-            read_xml(
-                path,
-                parser=parser,
-                parse_dates=["date"],
-                iterparse={"row": ["shape", "degrees", "sides", "date"]},
-            )
-
-
-def test_comment(parser):
-    xml = """\
-<!-- comment before root -->
-<shapes>
-  <!-- comment within root -->
-  <shape>
-    <name>circle</name>
-    <type>2D</type>
-  </shape>
-  <shape>
-    <name>sphere</name>
-    <type>3D</type>
-    <!-- comment within child -->
-  </shape>
-  <!-- comment within root -->
-</shapes>
-<!-- comment after root -->"""
-
-    df_xpath = read_xml(StringIO(xml), xpath=".//shape", parser=parser)
-
-    df_iter = read_xml_iterparse(
-        xml, parser=parser, iterparse={"shape": ["name", "type"]}
-    )
-
-    df_expected = DataFrame(
-        {
-            "name": ["circle", "sphere"],
-            "type": ["2D", "3D"],
-        }
-    )
-
-    tm.assert_frame_equal(df_xpath, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_dtd(parser):
-    xml = """\
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE non-profits [
-    <!ELEMENT shapes (shape*) >
-    <!ELEMENT shape ( name, type )>
-    <!ELEMENT name (#PCDATA)>
-]>
-<shapes>
-  <shape>
-    <name>circle</name>
-    <type>2D</type>
-  </shape>
-  <shape>
-    <name>sphere</name>
-    <type>3D</type>
-  </shape>
-</shapes>"""
-
-    df_xpath = read_xml(StringIO(xml), xpath=".//shape", parser=parser)
-
-    df_iter = read_xml_iterparse(
-        xml, parser=parser, iterparse={"shape": ["name", "type"]}
-    )
-
-    df_expected = DataFrame(
-        {
-            "name": ["circle", "sphere"],
-            "type": ["2D", "3D"],
-        }
-    )
-
-    tm.assert_frame_equal(df_xpath, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_processing_instruction(parser):
-    xml = """\
-<?xml version="1.0" encoding="UTF-8"?>
-<?xml-stylesheet type="text/xsl" href="style.xsl"?>
-<?display table-view?>
-<?sort alpha-ascending?>
-<?textinfo whitespace is allowed ?>
-<?elementnames <shape>, <name>, <type> ?>
-<shapes>
-  <shape>
-    <name>circle</name>
-    <type>2D</type>
-  </shape>
-  <shape>
-    <name>sphere</name>
-    <type>3D</type>
-  </shape>
-</shapes>"""
-
-    df_xpath = read_xml(StringIO(xml), xpath=".//shape", parser=parser)
-
-    df_iter = read_xml_iterparse(
-        xml, parser=parser, iterparse={"shape": ["name", "type"]}
-    )
-
-    df_expected = DataFrame(
-        {
-            "name": ["circle", "sphere"],
-            "type": ["2D", "3D"],
-        }
-    )
-
-    tm.assert_frame_equal(df_xpath, df_expected)
-    tm.assert_frame_equal(df_iter, df_expected)
-
-
-def test_no_result(xml_books, parser):
-    with pytest.raises(
-        ParserError, match="No result from selected items in iterparse."
-    ):
-        read_xml(
-            xml_books,
-            parser=parser,
-            iterparse={"node": ["attr1", "elem1", "elem2", "elem3"]},
-        )
-
-
-def test_empty_data(xml_books, parser):
-    with pytest.raises(EmptyDataError, match="No columns to parse from file"):
-        read_xml(
-            xml_books,
-            parser=parser,
-            iterparse={"book": ["attr1", "elem1", "elem2", "elem3"]},
-        )
-
-
-def test_online_stylesheet():
-    pytest.importorskip("lxml")
-    xml = """\
-<?xml version="1.0" encoding="UTF-8"?>
-<catalog>
-  <cd>
-    <title>Empire Burlesque</title>
-    <artist>Bob Dylan</artist>
-    <country>USA</country>
-    <company>Columbia</company>
-    <price>10.90</price>
-    <year>1985</year>
-  </cd>
-  <cd>
-    <title>Hide your heart</title>
-    <artist>Bonnie Tyler</artist>
-    <country>UK</country>
-    <company>CBS Records</company>
-    <price>9.90</price>
-    <year>1988</year>
-  </cd>
-  <cd>
-    <title>Greatest Hits</title>
-    <artist>Dolly Parton</artist>
-    <country>USA</country>
-    <company>RCA</company>
-    <price>9.90</price>
-    <year>1982</year>
-  </cd>
-  <cd>
-    <title>Still got the blues</title>
-    <artist>Gary Moore</artist>
-    <country>UK</country>
-    <company>Virgin records</company>
-    <price>10.20</price>
-    <year>1990</year>
-  </cd>
-  <cd>
-    <title>Eros</title>
-    <artist>Eros Ramazzotti</artist>
-    <country>EU</country>
-    <company>BMG</company>
-    <price>9.90</price>
-    <year>1997</year>
-  </cd>
-  <cd>
-    <title>One night only</title>
-    <artist>Bee Gees</artist>
-    <country>UK</country>
-    <company>Polydor</company>
-    <price>10.90</price>
-    <year>1998</year>
-  </cd>
-  <cd>
-    <title>Sylvias Mother</title>
-    <artist>Dr.Hook</artist>
-    <country>UK</country>
-    <company>CBS</company>
-    <price>8.10</price>
-    <year>1973</year>
-  </cd>
-  <cd>
-    <title>Maggie May</title>
-    <artist>Rod Stewart</artist>
-    <country>UK</country>
-    <company>Pickwick</company>
-    <price>8.50</price>
-    <year>1990</year>
-  </cd>
-  <cd>
-    <title>Romanza</title>
-    <artist>Andrea Bocelli</artist>
-    <country>EU</country>
-    <company>Polydor</company>
-    <price>10.80</price>
-    <year>1996</year>
-  </cd>
-  <cd>
-    <title>When a man loves a woman</title>
-    <artist>Percy Sledge</artist>
-    <country>USA</country>
-    <company>Atlantic</company>
-    <price>8.70</price>
-    <year>1987</year>
-  </cd>
-  <cd>
-    <title>Black angel</title>
-    <artist>Savage Rose</artist>
-    <country>EU</country>
-    <company>Mega</company>
-    <price>10.90</price>
-    <year>1995</year>
-  </cd>
-  <cd>
-    <title>1999 Grammy Nominees</title>
-    <artist>Many</artist>
-    <country>USA</country>
-    <company>Grammy</company>
-    <price>10.20</price>
-    <year>1999</year>
-  </cd>
-  <cd>
-    <title>For the good times</title>
-    <artist>Kenny Rogers</artist>
-    <country>UK</country>
-    <company>Mucik Master</company>
-    <price>8.70</price>
-    <year>1995</year>
-  </cd>
-  <cd>
-    <title>Big Willie style</title>
-    <artist>Will Smith</artist>
-    <country>USA</country>
-    <company>Columbia</company>
-    <price>9.90</price>
-    <year>1997</year>
-  </cd>
-  <cd>
-    <title>Tupelo Honey</title>
-    <artist>Van Morrison</artist>
-    <country>UK</country>
-    <company>Polydor</company>
-    <price>8.20</price>
-    <year>1971</year>
-  </cd>
-  <cd>
-    <title>Soulsville</title>
-    <artist>Jorn Hoel</artist>
-    <country>Norway</country>
-    <company>WEA</company>
-    <price>7.90</price>
-    <year>1996</year>
-  </cd>
-  <cd>
-    <title>The very best of</title>
-    <artist>Cat Stevens</artist>
-    <country>UK</country>
-    <company>Island</company>
-    <price>8.90</price>
-    <year>1990</year>
-  </cd>
-  <cd>
-    <title>Stop</title>
-    <artist>Sam Brown</artist>
-    <country>UK</country>
-    <company>A and M</company>
-    <price>8.90</price>
-    <year>1988</year>
-  </cd>
-  <cd>
-    <title>Bridge of Spies</title>
-    <artist>T`Pau</artist>
-    <country>UK</country>
-    <company>Siren</company>
-    <price>7.90</price>
-    <year>1987</year>
-  </cd>
-  <cd>
-    <title>Private Dancer</title>
-    <artist>Tina Turner</artist>
-    <country>UK</country>
-    <company>Capitol</company>
-    <price>8.90</price>
-    <year>1983</year>
-  </cd>
-  <cd>
-    <title>Midt om natten</title>
-    <artist>Kim Larsen</artist>
-    <country>EU</country>
-    <company>Medley</company>
-    <price>7.80</price>
-    <year>1983</year>
-  </cd>
-  <cd>
-    <title>Pavarotti Gala Concert</title>
-    <artist>Luciano Pavarotti</artist>
-    <country>UK</country>
-    <company>DECCA</company>
-    <price>9.90</price>
-    <year>1991</year>
-  </cd>
-  <cd>
-    <title>The dock of the bay</title>
-    <artist>Otis Redding</artist>
-    <country>USA</country>
-    <COMPANY>Stax Records</COMPANY>
-    <PRICE>7.90</PRICE>
-    <YEAR>1968</YEAR>
-  </cd>
-  <cd>
-    <title>Picture book</title>
-    <artist>Simply Red</artist>
-    <country>EU</country>
-    <company>Elektra</company>
-    <price>7.20</price>
-    <year>1985</year>
-  </cd>
-  <cd>
-    <title>Red</title>
-    <artist>The Communards</artist>
-    <country>UK</country>
-    <company>London</company>
-    <price>7.80</price>
-    <year>1987</year>
-  </cd>
-  <cd>
-    <title>Unchain my heart</title>
-    <artist>Joe Cocker</artist>
-    <country>USA</country>
-    <company>EMI</company>
-    <price>8.20</price>
-    <year>1987</year>
-  </cd>
-</catalog>
-"""
-    xsl = """\
-<?xml version="1.0" encoding="UTF-8"?>
-<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
-<xsl:template match="/">
-<html>
-<body>
-  <h2>My CD Collection</h2>
-  <table border="1">
-    <tr bgcolor="#9acd32">
-      <th style="text-align:left">Title</th>
-      <th style="text-align:left">Artist</th>
-    </tr>
-    <xsl:for-each select="catalog/cd">
-    <tr>
-      <td><xsl:value-of select="title"/></td>
-      <td><xsl:value-of select="artist"/></td>
-    </tr>
-    </xsl:for-each>
-  </table>
-</body>
-</html>
-</xsl:template>
-</xsl:stylesheet>
-"""
-
-    df_xsl = read_xml(
-        StringIO(xml),
-        xpath=".//tr[td and position() <= 6]",
-        names=["title", "artist"],
-        stylesheet=xsl,
-    )
-
-    df_expected = DataFrame(
-        {
-            "title": {
-                0: "Empire Burlesque",
-                1: "Hide your heart",
-                2: "Greatest Hits",
-                3: "Still got the blues",
-                4: "Eros",
-            },
-            "artist": {
-                0: "Bob Dylan",
-                1: "Bonnie Tyler",
-                2: "Dolly Parton",
-                3: "Gary Moore",
-                4: "Eros Ramazzotti",
-            },
-        }
-    )
-
-    tm.assert_frame_equal(df_expected, df_xsl)
-
-
-# COMPRESSION
-
-
-def test_compression_read(parser, compression_only):
-    with tm.ensure_clean() as comp_path:
-        geom_df.to_xml(
-            comp_path, index=False, parser=parser, compression=compression_only
-        )
-
-        df_xpath = read_xml(comp_path, parser=parser, compression=compression_only)
-
-        df_iter = read_xml_iterparse_comp(
-            comp_path,
-            compression_only,
-            parser=parser,
-            iterparse={"row": ["shape", "degrees", "sides"]},
-            compression=compression_only,
-        )
-
-    tm.assert_frame_equal(df_xpath, geom_df)
-    tm.assert_frame_equal(df_iter, geom_df)
-
-
-def test_wrong_compression(parser, compression, compression_only):
-    actual_compression = compression
-    attempted_compression = compression_only
-
-    if actual_compression == attempted_compression:
-        pytest.skip(f"{actual_compression} == {attempted_compression}")
-
-    errors = {
-        "bz2": (OSError, "Invalid data stream"),
-        "gzip": (OSError, "Not a gzipped file"),
-        "zip": (BadZipFile, "File is not a zip file"),
-        "tar": (ReadError, "file could not be opened successfully"),
-    }
-    zstd = import_optional_dependency("zstandard", errors="ignore")
-    if zstd is not None:
-        errors["zstd"] = (zstd.ZstdError, "Unknown frame descriptor")
-    lzma = import_optional_dependency("lzma", errors="ignore")
-    if lzma is not None:
-        errors["xz"] = (LZMAError, "Input format not supported by decoder")
-    error_cls, error_str = errors[attempted_compression]
-
-    with tm.ensure_clean() as path:
-        geom_df.to_xml(path, parser=parser, compression=actual_compression)
-
-        with pytest.raises(error_cls, match=error_str):
-            read_xml(path, parser=parser, compression=attempted_compression)
-
-
-def test_unsuported_compression(parser):
-    with pytest.raises(ValueError, match="Unrecognized compression type"):
-        with tm.ensure_clean() as path:
-            read_xml(path, parser=parser, compression="7z")
-
-
-# STORAGE OPTIONS
-
-
-@pytest.mark.network
-@pytest.mark.single_cpu
-def test_s3_parser_consistency(s3_public_bucket_with_data, s3so):
-    pytest.importorskip("s3fs")
-    pytest.importorskip("lxml")
-    s3 = f"s3://{s3_public_bucket_with_data.name}/books.xml"
-
-    df_lxml = read_xml(s3, parser="lxml", storage_options=s3so)
-
-    df_etree = read_xml(s3, parser="etree", storage_options=s3so)
-
-    tm.assert_frame_equal(df_lxml, df_etree)
-
-
-def test_read_xml_nullable_dtypes(parser, string_storage, dtype_backend):
-    # GH#50500
-    data = """<?xml version='1.0' encoding='utf-8'?>
-<data xmlns="http://example.com">
-<row>
-  <a>x</a>
-  <b>1</b>
-  <c>4.0</c>
-  <d>x</d>
-  <e>2</e>
-  <f>4.0</f>
-  <g></g>
-  <h>True</h>
-  <i>False</i>
-</row>
-<row>
-  <a>y</a>
-  <b>2</b>
-  <c>5.0</c>
-  <d></d>
-  <e></e>
-  <f></f>
-  <g></g>
-  <h>False</h>
-  <i></i>
-</row>
-</data>"""
-
-    if string_storage == "python":
-        string_array = StringArray(np.array(["x", "y"], dtype=np.object_))
-        string_array_na = StringArray(np.array(["x", NA], dtype=np.object_))
-
-    else:
-        pa = pytest.importorskip("pyarrow")
-        string_array = ArrowStringArray(pa.array(["x", "y"]))
-        string_array_na = ArrowStringArray(pa.array(["x", None]))
-
-    with pd.option_context("mode.string_storage", string_storage):
-        result = read_xml(StringIO(data), parser=parser, dtype_backend=dtype_backend)
-
-    expected = DataFrame(
-        {
-            "a": string_array,
-            "b": Series([1, 2], dtype="Int64"),
-            "c": Series([4.0, 5.0], dtype="Float64"),
-            "d": string_array_na,
-            "e": Series([2, NA], dtype="Int64"),
-            "f": Series([4.0, NA], dtype="Float64"),
-            "g": Series([NA, NA], dtype="Int64"),
-            "h": Series([True, False], dtype="boolean"),
-            "i": Series([False, NA], dtype="boolean"),
-        }
-    )
-
-    if dtype_backend == "pyarrow":
-        pa = pytest.importorskip("pyarrow")
-        from pandas.arrays import ArrowExtensionArray
-
-        expected = DataFrame(
-            {
-                col: ArrowExtensionArray(pa.array(expected[col], from_pandas=True))
-                for col in expected.columns
-            }
-        )
-        expected["g"] = ArrowExtensionArray(pa.array([None, None]))
-
-    tm.assert_frame_equal(result, expected)
-
-
-def test_invalid_dtype_backend():
-    msg = (
-        "dtype_backend numpy is invalid, only 'numpy_nullable' and "
-        "'pyarrow' are allowed."
-    )
-    with pytest.raises(ValueError, match=msg):
-        read_xml("test", dtype_backend="numpy")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tzdata/zoneinfo/Canada/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tzdata/zoneinfo/Canada/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pyInter/Liyuu_sovits4/README.md b/spaces/pyInter/Liyuu_sovits4/README.md
deleted file mode 100644
index e4faa4d2655bfa6240313ea31079b4f81a82cfee..0000000000000000000000000000000000000000
--- a/spaces/pyInter/Liyuu_sovits4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Sovits4
-emoji: 🐨
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: innnky/sovits4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/slicer2.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/slicer2.py
deleted file mode 100644
index 5b29ee262aa54045e807be2cffeb41687499ba58..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/slicer2.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import numpy as np
-
-
-# This function is obtained from librosa.
-def get_rms(
-    y,
-    frame_length=2048,
-    hop_length=512,
-    pad_mode="constant",
-):
-    padding = (int(frame_length // 2), int(frame_length // 2))
-    y = np.pad(y, padding, mode=pad_mode)
-
-    axis = -1
-    # put our new within-frame axis at the end for now
-    out_strides = y.strides + tuple([y.strides[axis]])
-    # Reduce the shape on the framing axis
-    x_shape_trimmed = list(y.shape)
-    x_shape_trimmed[axis] -= frame_length - 1
-    out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
-    xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
-    if axis < 0:
-        target_axis = axis - 1
-    else:
-        target_axis = axis + 1
-    xw = np.moveaxis(xw, -1, target_axis)
-    # Downsample along the target axis
-    slices = [slice(None)] * xw.ndim
-    slices[axis] = slice(0, None, hop_length)
-    x = xw[tuple(slices)]
-
-    # Calculate power
-    power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
-
-    return np.sqrt(power)
-
-
-class Slicer:
-    def __init__(
-        self,
-        sr: int,
-        threshold: float = -40.0,
-        min_length: int = 5000,
-        min_interval: int = 300,
-        hop_size: int = 20,
-        max_sil_kept: int = 5000,
-    ):
-        if not min_length >= min_interval >= hop_size:
-            raise ValueError(
-                "The following condition must be satisfied: min_length >= min_interval >= hop_size"
-            )
-        if not max_sil_kept >= hop_size:
-            raise ValueError(
-                "The following condition must be satisfied: max_sil_kept >= hop_size"
-            )
-        min_interval = sr * min_interval / 1000
-        self.threshold = 10 ** (threshold / 20.0)
-        self.hop_size = round(sr * hop_size / 1000)
-        self.win_size = min(round(min_interval), 4 * self.hop_size)
-        self.min_length = round(sr * min_length / 1000 / self.hop_size)
-        self.min_interval = round(min_interval / self.hop_size)
-        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
-
-    def _apply_slice(self, waveform, begin, end):
-        if len(waveform.shape) > 1:
-            return waveform[
-                :, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)
-            ]
-        else:
-            return waveform[
-                begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)
-            ]
-
-    # @timeit
-    def slice(self, waveform):
-        if len(waveform.shape) > 1:
-            samples = waveform.mean(axis=0)
-        else:
-            samples = waveform
-        if samples.shape[0] <= self.min_length:
-            return [waveform]
-        rms_list = get_rms(
-            y=samples, frame_length=self.win_size, hop_length=self.hop_size
-        ).squeeze(0)
-        sil_tags = []
-        silence_start = None
-        clip_start = 0
-        for i, rms in enumerate(rms_list):
-            # Keep looping while frame is silent.
-            if rms < self.threshold:
-                # Record start of silent frames.
-                if silence_start is None:
-                    silence_start = i
-                continue
-            # Keep looping while frame is not silent and silence start has not been recorded.
-            if silence_start is None:
-                continue
-            # Clear recorded silence start if interval is not enough or clip is too short
-            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
-            need_slice_middle = (
-                i - silence_start >= self.min_interval
-                and i - clip_start >= self.min_length
-            )
-            if not is_leading_silence and not need_slice_middle:
-                silence_start = None
-                continue
-            # Need slicing. Record the range of silent frames to be removed.
-            if i - silence_start <= self.max_sil_kept:
-                pos = rms_list[silence_start : i + 1].argmin() + silence_start
-                if silence_start == 0:
-                    sil_tags.append((0, pos))
-                else:
-                    sil_tags.append((pos, pos))
-                clip_start = pos
-            elif i - silence_start <= self.max_sil_kept * 2:
-                pos = rms_list[
-                    i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
-                ].argmin()
-                pos += i - self.max_sil_kept
-                pos_l = (
-                    rms_list[
-                        silence_start : silence_start + self.max_sil_kept + 1
-                    ].argmin()
-                    + silence_start
-                )
-                pos_r = (
-                    rms_list[i - self.max_sil_kept : i + 1].argmin()
-                    + i
-                    - self.max_sil_kept
-                )
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                    clip_start = pos_r
-                else:
-                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
-                    clip_start = max(pos_r, pos)
-            else:
-                pos_l = (
-                    rms_list[
-                        silence_start : silence_start + self.max_sil_kept + 1
-                    ].argmin()
-                    + silence_start
-                )
-                pos_r = (
-                    rms_list[i - self.max_sil_kept : i + 1].argmin()
-                    + i
-                    - self.max_sil_kept
-                )
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                else:
-                    sil_tags.append((pos_l, pos_r))
-                clip_start = pos_r
-            silence_start = None
-        # Deal with trailing silence.
-        total_frames = rms_list.shape[0]
-        if (
-            silence_start is not None
-            and total_frames - silence_start >= self.min_interval
-        ):
-            silence_end = min(total_frames, silence_start + self.max_sil_kept)
-            pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
-            sil_tags.append((pos, total_frames + 1))
-        # Apply and return slices.
-        if len(sil_tags) == 0:
-            return [waveform]
-        else:
-            chunks = []
-            if sil_tags[0][0] > 0:
-                chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0]))
-            for i in range(len(sil_tags) - 1):
-                chunks.append(
-                    self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0])
-                )
-            if sil_tags[-1][1] < total_frames:
-                chunks.append(
-                    self._apply_slice(waveform, sil_tags[-1][1], total_frames)
-                )
-            return chunks
-
-
-def main():
-    import os.path
-    from argparse import ArgumentParser
-
-    import librosa
-    import soundfile
-
-    parser = ArgumentParser()
-    parser.add_argument("audio", type=str, help="The audio to be sliced")
-    parser.add_argument(
-        "--out", type=str, help="Output directory of the sliced audio clips"
-    )
-    parser.add_argument(
-        "--db_thresh",
-        type=float,
-        required=False,
-        default=-40,
-        help="The dB threshold for silence detection",
-    )
-    parser.add_argument(
-        "--min_length",
-        type=int,
-        required=False,
-        default=5000,
-        help="The minimum milliseconds required for each sliced audio clip",
-    )
-    parser.add_argument(
-        "--min_interval",
-        type=int,
-        required=False,
-        default=300,
-        help="The minimum milliseconds for a silence part to be sliced",
-    )
-    parser.add_argument(
-        "--hop_size",
-        type=int,
-        required=False,
-        default=10,
-        help="Frame length in milliseconds",
-    )
-    parser.add_argument(
-        "--max_sil_kept",
-        type=int,
-        required=False,
-        default=500,
-        help="The maximum silence length kept around the sliced clip, presented in milliseconds",
-    )
-    args = parser.parse_args()
-    out = args.out
-    if out is None:
-        out = os.path.dirname(os.path.abspath(args.audio))
-    audio, sr = librosa.load(args.audio, sr=None, mono=False)
-    slicer = Slicer(
-        sr=sr,
-        threshold=args.db_thresh,
-        min_length=args.min_length,
-        min_interval=args.min_interval,
-        hop_size=args.hop_size,
-        max_sil_kept=args.max_sil_kept,
-    )
-    chunks = slicer.slice(audio)
-    if not os.path.exists(out):
-        os.makedirs(out)
-    for i, chunk in enumerate(chunks):
-        if len(chunk.shape) > 1:
-            chunk = chunk.T
-        soundfile.write(
-            os.path.join(
-                out,
-                f"%s_%d.wav"
-                % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
-            ),
-            chunk,
-            sr,
-        )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers.py b/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers.py
deleted file mode 100644
index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/demucs/utils.py b/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/demucs/utils.py
deleted file mode 100644
index 94bd323dcd4b170b4d71ff27bade1bb6ad28738f..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Ultimate-Vocal-Remover-WebUI/demucs/utils.py
+++ /dev/null
@@ -1,502 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import defaultdict
-from contextlib import contextmanager
-import math
-import os
-import tempfile
-import typing as tp
-
-import errno
-import functools
-import hashlib
-import inspect
-import io
-import os
-import random
-import socket
-import tempfile
-import warnings
-import zlib
-import tkinter as tk
-
-from diffq import UniformQuantizer, DiffQuantizer
-import torch as th
-import tqdm
-from torch import distributed
-from torch.nn import functional as F
-
-import torch
-
-def unfold(a, kernel_size, stride):
-    """Given input of size [*OT, T], output Tensor of size [*OT, F, K]
-    with K the kernel size, by extracting frames with the given stride.
-
-    This will pad the input so that `F = ceil(T / K)`.
-
-    see https://github.com/pytorch/pytorch/issues/60466
-    """
-    *shape, length = a.shape
-    n_frames = math.ceil(length / stride)
-    tgt_length = (n_frames - 1) * stride + kernel_size
-    a = F.pad(a, (0, tgt_length - length))
-    strides = list(a.stride())
-    assert strides[-1] == 1, 'data should be contiguous'
-    strides = strides[:-1] + [stride, 1]
-    return a.as_strided([*shape, n_frames, kernel_size], strides)
-
-
-def center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]):
-    """
-    Center trim `tensor` with respect to `reference`, along the last dimension.
-    `reference` can also be a number, representing the length to trim to.
-    If the size difference != 0 mod 2, the extra sample is removed on the right side.
-    """
-    ref_size: int
-    if isinstance(reference, torch.Tensor):
-        ref_size = reference.size(-1)
-    else:
-        ref_size = reference
-    delta = tensor.size(-1) - ref_size
-    if delta < 0:
-        raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.")
-    if delta:
-        tensor = tensor[..., delta // 2:-(delta - delta // 2)]
-    return tensor
-
-
-def pull_metric(history: tp.List[dict], name: str):
-    out = []
-    for metrics in history:
-        metric = metrics
-        for part in name.split("."):
-            metric = metric[part]
-        out.append(metric)
-    return out
-
-
-def EMA(beta: float = 1):
-    """
-    Exponential Moving Average callback.
-    Returns a single function that can be called to repeatidly update the EMA
-    with a dict of metrics. The callback will return
-    the new averaged dict of metrics.
-
-    Note that for `beta=1`, this is just plain averaging.
-    """
-    fix: tp.Dict[str, float] = defaultdict(float)
-    total: tp.Dict[str, float] = defaultdict(float)
-
-    def _update(metrics: dict, weight: float = 1) -> dict:
-        nonlocal total, fix
-        for key, value in metrics.items():
-            total[key] = total[key] * beta + weight * float(value)
-            fix[key] = fix[key] * beta + weight
-        return {key: tot / fix[key] for key, tot in total.items()}
-    return _update
-
-
-def sizeof_fmt(num: float, suffix: str = 'B'):
-    """
-    Given `num` bytes, return human readable size.
-    Taken from https://stackoverflow.com/a/1094933
-    """
-    for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
-        if abs(num) < 1024.0:
-            return "%3.1f%s%s" % (num, unit, suffix)
-        num /= 1024.0
-    return "%.1f%s%s" % (num, 'Yi', suffix)
-
-
-@contextmanager
-def temp_filenames(count: int, delete=True):
-    names = []
-    try:
-        for _ in range(count):
-            names.append(tempfile.NamedTemporaryFile(delete=False).name)
-        yield names
-    finally:
-        if delete:
-            for name in names:
-                os.unlink(name)
-
-def average_metric(metric, count=1.):
-    """
-    Average `metric` which should be a float across all hosts. `count` should be
-    the weight for this particular host (i.e. number of examples).
-    """
-    metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda')
-    distributed.all_reduce(metric, op=distributed.ReduceOp.SUM)
-    return metric[1].item() / metric[0].item()
-
-
-def free_port(host='', low=20000, high=40000):
-    """
-    Return a port number that is most likely free.
-    This could suffer from a race condition although
-    it should be quite rare.
-    """
-    sock = socket.socket()
-    while True:
-        port = random.randint(low, high)
-        try:
-            sock.bind((host, port))
-        except OSError as error:
-            if error.errno == errno.EADDRINUSE:
-                continue
-            raise
-        return port
-
-
-def sizeof_fmt(num, suffix='B'):
-    """
-    Given `num` bytes, return human readable size.
-    Taken from https://stackoverflow.com/a/1094933
-    """
-    for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
-        if abs(num) < 1024.0:
-            return "%3.1f%s%s" % (num, unit, suffix)
-        num /= 1024.0
-    return "%.1f%s%s" % (num, 'Yi', suffix)
-
-
-def human_seconds(seconds, display='.2f'):
-    """
-    Given `seconds` seconds, return human readable duration.
-    """
-    value = seconds * 1e6
-    ratios = [1e3, 1e3, 60, 60, 24]
-    names = ['us', 'ms', 's', 'min', 'hrs', 'days']
-    last = names.pop(0)
-    for name, ratio in zip(names, ratios):
-        if value / ratio < 0.3:
-            break
-        value /= ratio
-        last = name
-    return f"{format(value, display)} {last}"
-
-
-class TensorChunk:
-    def __init__(self, tensor, offset=0, length=None):
-        total_length = tensor.shape[-1]
-        assert offset >= 0
-        assert offset < total_length
-
-        if length is None:
-            length = total_length - offset
-        else:
-            length = min(total_length - offset, length)
-
-        self.tensor = tensor
-        self.offset = offset
-        self.length = length
-        self.device = tensor.device
-
-    @property
-    def shape(self):
-        shape = list(self.tensor.shape)
-        shape[-1] = self.length
-        return shape
-
-    def padded(self, target_length):
-        delta = target_length - self.length
-        total_length = self.tensor.shape[-1]
-        assert delta >= 0
-
-        start = self.offset - delta // 2
-        end = start + target_length
-
-        correct_start = max(0, start)
-        correct_end = min(total_length, end)
-
-        pad_left = correct_start - start
-        pad_right = end - correct_end
-
-        out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right))
-        assert out.shape[-1] == target_length
-        return out
-
-
-def tensor_chunk(tensor_or_chunk):
-    if isinstance(tensor_or_chunk, TensorChunk):
-        return tensor_or_chunk
-    else:
-        assert isinstance(tensor_or_chunk, th.Tensor)
-        return TensorChunk(tensor_or_chunk)
-
-
-def apply_model_v1(model, mix, shifts=None, split=False, progress=False, set_progress_bar=None):
-    """
-    Apply model to a given mixture.
-
-    Args:
-        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
-            and apply the oppositve shift to the output. This is repeated `shifts` time and
-            all predictions are averaged. This effectively makes the model time equivariant
-            and improves SDR by up to 0.2 points.
-        split (bool): if True, the input will be broken down in 8 seconds extracts
-            and predictions will be performed individually on each and concatenated.
-            Useful for model with large memory footprint like Tasnet.
-        progress (bool): if True, show a progress bar (requires split=True)
-    """
-
-    channels, length = mix.size()
-    device = mix.device
-    progress_value = 0
-    
-    if split:
-        out = th.zeros(4, channels, length, device=device)
-        shift = model.samplerate * 10
-        offsets = range(0, length, shift)
-        scale = 10
-        if progress:
-            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
-        for offset in offsets:
-            chunk = mix[..., offset:offset + shift]
-            if set_progress_bar:
-                progress_value += 1
-                set_progress_bar(0.1, (0.8/len(offsets)*progress_value))
-                chunk_out = apply_model_v1(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)
-            else:
-                chunk_out = apply_model_v1(model, chunk, shifts=shifts)
-            out[..., offset:offset + shift] = chunk_out
-            offset += shift
-        return out
-    elif shifts:
-        max_shift = int(model.samplerate / 2)
-        mix = F.pad(mix, (max_shift, max_shift))
-        offsets = list(range(max_shift))
-        random.shuffle(offsets)
-        out = 0
-        for offset in offsets[:shifts]:
-            shifted = mix[..., offset:offset + length + max_shift]
-            if set_progress_bar:
-                shifted_out = apply_model_v1(model, shifted, set_progress_bar=set_progress_bar)
-            else:
-                shifted_out = apply_model_v1(model, shifted)
-            out += shifted_out[..., max_shift - offset:max_shift - offset + length]
-        out /= shifts
-        return out
-    else:
-        valid_length = model.valid_length(length)
-        delta = valid_length - length
-        padded = F.pad(mix, (delta // 2, delta - delta // 2))
-        with th.no_grad():
-            out = model(padded.unsqueeze(0))[0]
-        return center_trim(out, mix)
-
-def apply_model_v2(model, mix, shifts=None, split=False,
-                overlap=0.25, transition_power=1., progress=False, set_progress_bar=None): 
-    """
-    Apply model to a given mixture.
-
-    Args:
-        shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec
-            and apply the oppositve shift to the output. This is repeated `shifts` time and
-            all predictions are averaged. This effectively makes the model time equivariant
-            and improves SDR by up to 0.2 points.
-        split (bool): if True, the input will be broken down in 8 seconds extracts
-            and predictions will be performed individually on each and concatenated.
-            Useful for model with large memory footprint like Tasnet.
-        progress (bool): if True, show a progress bar (requires split=True)
-    """
-    
-    assert transition_power >= 1, "transition_power < 1 leads to weird behavior."
-    device = mix.device
-    channels, length = mix.shape
-    progress_value = 0
-    
-    if split:
-        out = th.zeros(len(model.sources), channels, length, device=device)
-        sum_weight = th.zeros(length, device=device)
-        segment = model.segment_length
-        stride = int((1 - overlap) * segment)
-        offsets = range(0, length, stride)
-        scale = stride / model.samplerate
-        if progress:
-            offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds')
-        # We start from a triangle shaped weight, with maximal weight in the middle
-        # of the segment. Then we normalize and take to the power `transition_power`.
-        # Large values of transition power will lead to sharper transitions.
-        weight = th.cat([th.arange(1, segment // 2 + 1),
-                         th.arange(segment - segment // 2, 0, -1)]).to(device)
-        assert len(weight) == segment
-        # If the overlap < 50%, this will translate to linear transition when
-        # transition_power is 1.
-        weight = (weight / weight.max())**transition_power
-        for offset in offsets:
-            chunk = TensorChunk(mix, offset, segment)
-            if set_progress_bar:
-                progress_value += 1
-                set_progress_bar(0.1, (0.8/len(offsets)*progress_value))
-                chunk_out = apply_model_v2(model, chunk, shifts=shifts, set_progress_bar=set_progress_bar)
-            else:
-                chunk_out = apply_model_v2(model, chunk, shifts=shifts)
-            chunk_length = chunk_out.shape[-1]
-            out[..., offset:offset + segment] += weight[:chunk_length] * chunk_out
-            sum_weight[offset:offset + segment] += weight[:chunk_length]
-            offset += segment
-        assert sum_weight.min() > 0
-        out /= sum_weight
-        return out
-    elif shifts:
-        max_shift = int(0.5 * model.samplerate)
-        mix = tensor_chunk(mix)
-        padded_mix = mix.padded(length + 2 * max_shift)
-        out = 0
-        for _ in range(shifts):
-            offset = random.randint(0, max_shift)
-            shifted = TensorChunk(padded_mix, offset, length + max_shift - offset)
-            
-            if set_progress_bar:
-                progress_value += 1
-                shifted_out = apply_model_v2(model, shifted, set_progress_bar=set_progress_bar)
-            else:
-                shifted_out = apply_model_v2(model, shifted)
-            out += shifted_out[..., max_shift - offset:]
-        out /= shifts
-        return out
-    else:
-        valid_length = model.valid_length(length)
-        mix = tensor_chunk(mix)
-        padded_mix = mix.padded(valid_length)
-        with th.no_grad():
-            out = model(padded_mix.unsqueeze(0))[0]
-        return center_trim(out, length)
-
-
-@contextmanager
-def temp_filenames(count, delete=True):
-    names = []
-    try:
-        for _ in range(count):
-            names.append(tempfile.NamedTemporaryFile(delete=False).name)
-        yield names
-    finally:
-        if delete:
-            for name in names:
-                os.unlink(name)
-
-
-def get_quantizer(model, args, optimizer=None):
-    quantizer = None
-    if args.diffq:
-        quantizer = DiffQuantizer(
-            model, min_size=args.q_min_size, group_size=8)
-        if optimizer is not None:
-            quantizer.setup_optimizer(optimizer)
-    elif args.qat:
-        quantizer = UniformQuantizer(
-                model, bits=args.qat, min_size=args.q_min_size)
-    return quantizer
-
-
-def load_model(path, strict=False):
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        load_from = path
-        package = th.load(load_from, 'cpu')
-
-    klass = package["klass"]
-    args = package["args"]
-    kwargs = package["kwargs"]
-
-    if strict:
-        model = klass(*args, **kwargs)
-    else:
-        sig = inspect.signature(klass)
-        for key in list(kwargs):
-            if key not in sig.parameters:
-                warnings.warn("Dropping inexistant parameter " + key)
-                del kwargs[key]
-        model = klass(*args, **kwargs)
-
-    state = package["state"]
-    training_args = package["training_args"]
-    quantizer = get_quantizer(model, training_args)
-
-    set_state(model, quantizer, state)
-    return model
-
-
-def get_state(model, quantizer):
-    if quantizer is None:
-        state = {k: p.data.to('cpu') for k, p in model.state_dict().items()}
-    else:
-        state = quantizer.get_quantized_state()
-        buf = io.BytesIO()
-        th.save(state, buf)
-        state = {'compressed': zlib.compress(buf.getvalue())}
-    return state
-
-
-def set_state(model, quantizer, state):
-    if quantizer is None:
-        model.load_state_dict(state)
-    else:
-        buf = io.BytesIO(zlib.decompress(state["compressed"]))
-        state = th.load(buf, "cpu")
-        quantizer.restore_quantized_state(state)
-
-    return state
-
-
-def save_state(state, path):
-    buf = io.BytesIO()
-    th.save(state, buf)
-    sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
-
-    path = path.parent / (path.stem + "-" + sig + path.suffix)
-    path.write_bytes(buf.getvalue())
-
-
-def save_model(model, quantizer, training_args, path):
-    args, kwargs = model._init_args_kwargs
-    klass = model.__class__
-
-    state = get_state(model, quantizer)
-
-    save_to = path
-    package = {
-        'klass': klass,
-        'args': args,
-        'kwargs': kwargs,
-        'state': state,
-        'training_args': training_args,
-    }
-    th.save(package, save_to)
-
-
-def capture_init(init):
-    @functools.wraps(init)
-    def __init__(self, *args, **kwargs):
-        self._init_args_kwargs = (args, kwargs)
-        init(self, *args, **kwargs)
-
-    return __init__
-
-class DummyPoolExecutor:
-    class DummyResult:
-        def __init__(self, func, *args, **kwargs):
-            self.func = func
-            self.args = args
-            self.kwargs = kwargs
-
-        def result(self):
-            return self.func(*self.args, **self.kwargs)
-
-    def __init__(self, workers=0):
-        pass
-
-    def submit(self, func, *args, **kwargs):
-        return DummyPoolExecutor.DummyResult(func, *args, **kwargs)
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_tb):
-        return
diff --git a/spaces/radames/Candle-Phi-1.5-Wasm/index.html b/spaces/radames/Candle-Phi-1.5-Wasm/index.html
deleted file mode 100644
index 68a23c87c932825a8000082a606060fc2fe35169..0000000000000000000000000000000000000000
--- a/spaces/radames/Candle-Phi-1.5-Wasm/index.html
+++ /dev/null
@@ -1,530 +0,0 @@
-<html>
-  <head>
-    <meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
-    <title>Candle Phi 1.5 Rust/WASM</title>
-  </head>
-  <body></body>
-</html>
-
-<!DOCTYPE html>
-<html>
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <link
-      rel="stylesheet"
-      href="https://cdn.jsdelivr.net/gh/highlightjs/cdn-release@11.8.0/build/styles/default.min.css"
-    />
-    <style>
-      @import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
-      html,
-      body {
-        font-family: "Source Sans 3", sans-serif;
-      }
-      code,
-      output,
-      select,
-      pre {
-        font-family: "Source Code Pro", monospace;
-      }
-    </style>
-    <style type="text/tailwindcss">
-      .link {
-        @apply underline hover:text-blue-500 hover:no-underline;
-      }
-    </style>
-    <script src="https://cdn.tailwindcss.com"></script>
-    <script type="module">
-      import snarkdown from "https://cdn.skypack.dev/snarkdown";
-      import hljs from "https://cdn.skypack.dev/highlight.js";
-      // models base url
-      const MODELS = {
-        phi_1_5_quantized: {
-          base_url:
-            "https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
-          model: "model-q4k.gguf",
-          tokenizer: "tokenizer.json",
-          config: "phi-1_5.json",
-          quantized: true,
-          seq_len: 2048,
-          size: "800 MB",
-        },
-        phi_1_5_quantized_2: {
-          base_url:
-            "https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
-          model: "model-q80.gguf",
-          tokenizer: "tokenizer.json",
-          config: "phi-1_5.json",
-          quantized: true,
-          seq_len: 2048,
-          size: "1.51 GB",
-        },
-        puffin_phi_v2_quantized: {
-          base_url:
-            "https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
-          model: "model-puffin-phi-v2-q4k.gguf",
-          tokenizer: "tokenizer-puffin-phi-v2.json",
-          config: "puffin-phi-v2.json",
-          quantized: true,
-          seq_len: 2048,
-          size: "798 MB",
-        },
-        puffin_phi_v2_quantized_2: {
-          base_url:
-            "https://huggingface.co/lmz/candle-quantized-phi/resolve/main/",
-          model: "model-puffin-phi-v2-q80.gguf",
-          tokenizer: "tokenizer-puffin-phi-v2.json",
-          config: "puffin-phi-v2.json",
-          quantized: true,
-          seq_len: 2048,
-          size: "1.50 GB",
-        },
-      };
-
-      const TEMPLATES = [
-        {
-          title: "Simple prompt",
-          prompt: `Sebastien is in London today, it’s the middle of July yet it’s raining, so Sebastien is feeling gloomy. He`,
-        },
-        {
-          title: "Think step by step",
-          prompt: `Suppose Alice originally had 3 apples, then Bob gave Alice 7 apples, then Alice gave Cook 5 apples, and then Tim gave Alice 3x the amount of apples Alice had. How many apples does Alice have now?  
-Let’s think step by step.`,
-        },
-        {
-          title: "Explaing a code snippet",
-          prompt: `What does this script do?  
-\`\`\`python
-s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-s.bind(('', 0))
-s.listen(1)
-conn, addr = s.accept()
-print('Connected by', addr)
-return conn.getsockname()[1]
-\`\`\`
-Let’s think step by step.`,
-        },
-        {
-          title: "Question answering",
-          prompt: `What is the capital of France?  
-Answer:`,
-        },
-        {
-          title: "Chat mode",
-          prompt: `Alice: Can you tell me how to create a python application to go through all the files
-in one directory where the file’s name DOES NOT end with '.json'?  
-Bob:`,
-        },
-        {
-          title: "Python code completion",
-          prompt: `"""write a python function called batch(function, list) which call function(x) for x in
-list in parallel"""  
-Solution:`,
-        },
-        {
-          title: "Python Sample",
-          prompt: `"""Can you make sure those histograms appear side by side on the same plot:  
-\`\`\`python
-plt.hist(intreps_retrained[0][1].view(64,-1).norm(dim=1).detach().cpu().numpy(), bins = 20)
-plt.hist(intreps_pretrained[0][1].view(64,-1).norm(dim=1).detach().cpu().numpy(), bins = 20)
-\`\`\`  
-"""`,
-        },
-        {
-          title: "Write a Twitter post",
-          prompt: `Write a twitter post for the discovery of gravitational wave.  
-Twitter Post:`,
-        },
-        {
-          title: "Write a review",
-          prompt: `Write a polite review complaining that the video game 'Random Game' was too badly optimized and it burned my laptop.  
-Very polite review:`,
-        },
-      ];
-      const phiWorker = new Worker("./phiWorker.js", {
-        type: "module",
-      });
-      async function generateSequence(controller) {
-        const getValue = (id) => document.querySelector(`#${id}`).value;
-        const modelID = getValue("model");
-        const model = MODELS[modelID];
-        const weightsURL = model.base_url + model.model;
-        const tokenizerURL = model.base_url + model.tokenizer;
-        const configURL = model.base_url + model.config;
-
-        const prompt = getValue("prompt").trim();
-        const temperature = getValue("temperature");
-        const topP = getValue("top-p");
-        const repeatPenalty = getValue("repeat_penalty");
-        const seed = getValue("seed");
-        const maxSeqLen = getValue("max-seq");
-
-        function updateStatus(data) {
-          const outStatus = document.querySelector("#output-status");
-          const outGen = document.querySelector("#output-generation");
-          const outCounter = document.querySelector("#output-counter");
-
-          switch (data.status) {
-            case "loading":
-              outStatus.hidden = false;
-              outStatus.textContent = data.message;
-              outGen.hidden = true;
-              outCounter.hidden = true;
-              break;
-            case "generating":
-              const { message, prompt, sentence, tokensSec, totalTime } = data;
-              outStatus.hidden = true;
-              outCounter.hidden = false;
-              outGen.hidden = false;
-              outGen.innerHTML = snarkdown(prompt + sentence);
-              outCounter.innerHTML = `${(totalTime / 1000).toFixed(
-                2
-              )}s (${tokensSec.toFixed(2)} tok/s)`;
-              hljs.highlightAll();
-              break;
-            case "complete":
-              outStatus.hidden = true;
-              outGen.hidden = false;
-              break;
-          }
-        }
-
-        return new Promise((resolve, reject) => {
-          phiWorker.postMessage({
-            weightsURL,
-            modelID,
-            tokenizerURL,
-            configURL,
-            quantized: model.quantized,
-            prompt,
-            temp: temperature,
-            top_p: topP,
-            repeatPenalty,
-            seed: seed,
-            maxSeqLen,
-            command: "start",
-          });
-
-          const handleAbort = () => {
-            phiWorker.postMessage({ command: "abort" });
-          };
-          const handleMessage = (event) => {
-            const { status, error, message, prompt, sentence } = event.data;
-            if (status) updateStatus(event.data);
-            if (error) {
-              phiWorker.removeEventListener("message", handleMessage);
-              reject(new Error(error));
-            }
-            if (status === "aborted") {
-              phiWorker.removeEventListener("message", handleMessage);
-              resolve(event.data);
-            }
-            if (status === "complete") {
-              phiWorker.removeEventListener("message", handleMessage);
-              resolve(event.data);
-            }
-          };
-
-          controller.signal.addEventListener("abort", handleAbort);
-          phiWorker.addEventListener("message", handleMessage);
-        });
-      }
-
-      const form = document.querySelector("#form");
-      const prompt = document.querySelector("#prompt");
-      const clearBtn = document.querySelector("#clear-btn");
-      const runBtn = document.querySelector("#run");
-      const modelSelect = document.querySelector("#model");
-      const promptTemplates = document.querySelector("#prompt-templates");
-      let runController = new AbortController();
-      let isRunning = false;
-
-      document.addEventListener("DOMContentLoaded", () => {
-        for (const [id, model] of Object.entries(MODELS)) {
-          const option = document.createElement("option");
-          option.value = id;
-          option.innerText = `${id} (${model.size})`;
-          modelSelect.appendChild(option);
-        }
-
-        for (const [i, { title, prompt }] of TEMPLATES.entries()) {
-          const div = document.createElement("div");
-          const input = document.createElement("input");
-          input.type = "radio";
-          input.name = "task";
-          input.id = `templates-${i}`;
-          input.classList.add("font-light", "cursor-pointer");
-          input.value = prompt;
-          const label = document.createElement("label");
-          label.htmlFor = `templates-${i}`;
-          label.classList.add("cursor-pointer", "px-1");
-          label.innerText = title;
-          div.appendChild(input);
-          div.appendChild(label);
-          promptTemplates.appendChild(div);
-        }
-      });
-
-      promptTemplates.addEventListener("change", (e) => {
-        const template = e.target.value;
-        prompt.value = template;
-        prompt.style.height = "auto";
-        prompt.style.height = prompt.scrollHeight + "px";
-      });
-      modelSelect.addEventListener("change", (e) => {
-        const model = MODELS[e.target.value];
-        document.querySelector("#max-seq").max = model.seq_len;
-        document.querySelector("#max-seq").nextElementSibling.value = 200;
-      });
-
-      form.addEventListener("submit", async (e) => {
-        e.preventDefault();
-        if (isRunning) {
-          stopRunning();
-        } else {
-          startRunning();
-          await generateSequence(runController);
-          stopRunning();
-        }
-      });
-
-      function startRunning() {
-        isRunning = true;
-        runBtn.textContent = "Stop";
-      }
-
-      function stopRunning() {
-        runController.abort();
-        runController = new AbortController();
-        runBtn.textContent = "Run";
-        isRunning = false;
-      }
-      clearBtn.addEventListener("click", (e) => {
-        e.preventDefault();
-        prompt.value = "";
-        clearBtn.classList.add("invisible");
-        runBtn.disabled = true;
-        stopRunning();
-      });
-      prompt.addEventListener("input", (e) => {
-        runBtn.disabled = false;
-        if (e.target.value.length > 0) {
-          clearBtn.classList.remove("invisible");
-        } else {
-          clearBtn.classList.add("invisible");
-        }
-      });
-    </script>
-  </head>
-  <body class="container max-w-4xl mx-auto p-4 text-gray-800">
-    <main class="grid grid-cols-1 gap-8 relative">
-      <span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
-      <div>
-        <h1 class="text-5xl font-bold">Candle Phi 1.5</h1>
-        <h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
-        <p class="max-w-lg">
-          The
-          <a
-            href="https://huggingface.co/microsoft/phi-1_5"
-            class="link"
-            target="_blank"
-            >Phi-1.5</a
-          >
-          model achieves state-of-the-art performance with only 1.3 billion
-          parameters, compared to models with up to 10 billion. You can try the
-          quantized version of the model here. Additional prompt examples are
-          available in the
-          <a
-            href="https://arxiv.org/pdf/2309.05463.pdf#page=8"
-            class="link"
-            target="_blank"
-          >
-            technical report </a
-          >.
-        </p>
-        <p class="max-w-lg">
-          You can also try
-          <a
-            href="https://huggingface.co/teknium/Puffin-Phi-v2"
-            class="link"
-            target="_blank"
-            >Puffin-Phi V2
-          </a>
-          quantized version model, a fine-tuned version of Phi-1.5 on the
-          <a
-            href="https://huggingface.co/datasets/LDJnr/Puffin"
-            class="link"
-            target="_blank"
-            >Puffin dataset
-          </a>
-        </p>
-      </div>
-      <div>
-        <p class="text-xs italic max-w-lg">
-          <b>Note:</b>
-          When first run, the app will download and cache the model, which could
-          take a few minutes. The models are <b>~800MB</b> or <b>~1.51GB</b> in
-          size.
-        </p>
-      </div>
-      <div>
-        <label for="model" class="font-medium">Models Options: </label>
-        <select
-          id="model"
-          class="border-2 border-gray-500 rounded-md font-light"
-        ></select>
-      </div>
-      <div>
-        <h3 class="font-medium">Prompt Templates</h3>
-        <form id="prompt-templates" class="flex flex-col gap-1 my-2"></form>
-      </div>
-      <form
-        id="form"
-        class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center"
-      >
-        <input type="submit" hidden />
-        <textarea
-          type="text"
-          id="prompt"
-          class="font-light w-full px-3 py-2 mx-1 resize-none outline-none"
-          oninput="this.style.height = 0;this.style.height = this.scrollHeight + 'px'"
-          placeholder="Add your prompt here..."
-        >
-Write a detailed analogy between mathematics and a lighthouse.  
-Answer:</textarea
-        >
-        <button id="clear-btn">
-          <svg
-            fill="none"
-            xmlns="http://www.w3.org/2000/svg"
-            width="40"
-            viewBox="0 0 70 40"
-          >
-            <path opacity=".5" d="M39 .2v40.2" stroke="#1F2937" />
-            <path
-              d="M1.5 11.5 19 29.1m0-17.6L1.5 29.1"
-              opacity=".5"
-              stroke="#1F2937"
-              stroke-width="2"
-            />
-          </svg>
-        </button>
-        <button
-          id="run"
-          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed"
-        >
-          Run
-        </button>
-      </form>
-      <details>
-        <summary class="font-medium cursor-pointer">Advanced Options</summary>
-
-        <div class="grid grid-cols-3 max-w-md items-center gap-3 py-3">
-          <label class="text-sm font-medium" for="max-seq"
-            >Maximum length
-          </label>
-          <input
-            type="range"
-            id="max-seq"
-            name="max-seq"
-            min="1"
-            max="2048"
-            step="1"
-            value="200"
-            oninput="this.nextElementSibling.value = Number(this.value)"
-          />
-          <output
-            class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
-          >
-            200</output
-          >
-          <label class="text-sm font-medium" for="temperature"
-            >Temperature</label
-          >
-          <input
-            type="range"
-            id="temperature"
-            name="temperature"
-            min="0"
-            max="2"
-            step="0.01"
-            value="0.00"
-            oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
-          />
-          <output
-            class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
-          >
-            0.00</output
-          >
-          <label class="text-sm font-medium" for="top-p">Top-p</label>
-          <input
-            type="range"
-            id="top-p"
-            name="top-p"
-            min="0"
-            max="1"
-            step="0.01"
-            value="1.00"
-            oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
-          />
-          <output
-            class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
-          >
-            1.00</output
-          >
-
-          <label class="text-sm font-medium" for="repeat_penalty"
-            >Repeat Penalty</label
-          >
-
-          <input
-            type="range"
-            id="repeat_penalty"
-            name="repeat_penalty"
-            min="1"
-            max="2"
-            step="0.01"
-            value="1.10"
-            oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
-          />
-          <output
-            class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
-            >1.10</output
-          >
-          <label class="text-sm font-medium" for="seed">Seed</label>
-          <input
-            type="number"
-            id="seed"
-            name="seed"
-            value="299792458"
-            class="font-light border border-gray-700 text-right rounded-md p-2"
-          />
-          <button
-            id="run"
-            onclick="document.querySelector('#seed').value = Math.floor(Math.random() * Number.MAX_SAFE_INTEGER)"
-            class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-1 w-[50px] rounded disabled:bg-gray-300 disabled:cursor-not-allowed text-sm"
-          >
-            Rand
-          </button>
-        </div>
-      </details>
-
-      <div>
-        <h3 class="font-medium">Generation:</h3>
-        <div
-          class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md flex flex-col gap-2"
-        >
-          <div
-            id="output-counter"
-            hidden
-            class="ml-auto font-semibold grid-rows-1 text-sm"
-          ></div>
-          <p hidden id="output-generation" class="grid-rows-2"></p>
-          <span id="output-status" class="m-auto font-light"
-            >No output yet</span
-          >
-        </div>
-      </div>
-    </main>
-  </body>
-</html>
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/EPLAN Electric P8 24 X64 Keygen A Comparison with Other Electrical CAD Software.md b/spaces/raedeXanto/academic-chatgpt-beta/EPLAN Electric P8 24 X64 Keygen A Comparison with Other Electrical CAD Software.md
deleted file mode 100644
index 896f20980b563a8c1338305122ac308cf10043e2..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/EPLAN Electric P8 24 X64 Keygen A Comparison with Other Electrical CAD Software.md	
+++ /dev/null
@@ -1,105 +0,0 @@
-<br />
-<h1>EPLAN Electric P8 24 X64 Keygen: A Comprehensive Guide</h1>
-<p>If you are looking for a consistent, integrated and fast engineering system for planning and designing the electrical engineering for machines and plant systems, you might want to check out <strong>EPLAN Electric P8</strong>. This software supports a wide variety of engineering methods: from manual creation to standardised and template-based approaches. It also allows you to generate and export documentation and reports automatically from your project data.</p>
-<p>However, to enjoy the full benefits of EPLAN Electric P8, you need a valid license key that can activate the software. This is where <strong>EPLAN Electric P8 24 X64 Keygen</strong> comes in handy. This is a tool that can generate a unique and working license key for your EPLAN Electric P8 software. With this keygen, you can unlock all the features and functions of EPLAN Electric P8 without paying a dime.</p>
-<h2>EPLAN Electric P8 24 X64 Keygen</h2><br /><p><b><b>Download Zip</b> &#10042; <a href="https://tinourl.com/2uL4d8">https://tinourl.com/2uL4d8</a></b></p><br /><br />
-<p>In this article, we will show you how to install and use EPLAN Electric P8 24 X64 Keygen, as well as some of its features and benefits. We will also answer some of the most frequently asked questions about this keygen. So, let's get started!</p>
- <h2>How to Install EPLAN Electric P8 24 X64 Keygen</h2>
-<p>Before you can use EPLAN Electric P8 24 X64 Keygen, you need to download and install it on your computer. Here are the steps you need to follow:</p>
-<ol>
-<li>Download EPLAN Electric P8 24 X64 Keygen from a reliable source. You can find many websites that offer this keygen for free, but be careful of malware and viruses. Make sure you scan the file before opening it.</li>
-<li>Extract the zip file to a folder on your computer. You will see two files: <code>EplanP8KeyGen.exe</code> and <code>Readme.txt</code>. The first one is the keygen itself, and the second one contains some instructions and information about the keygen.</li>
-<li>Run <code>EplanP8KeyGen.exe</code> as administrator. You will see a window like this:</li>
-</ol>
-<pre><code>&lt;img src="EplanP8KeyGen.png" alt="EplanP8KeyGen window"&gt; </code></pre>
-<ol start="4">
-<li>Click on <code>Generate</code> button. The keygen will generate a random license key for your EPLAN Electric P8 software. You can copy the key by clicking on <code>Copy</code> button.</li>
-<li>Open your EPLAN Electric P8 software. If you have not installed it yet, you can download it from the official website of EPLAN Software & Service GmbH & Co. KG. You will need to register an account and provide some details before downloading.</li>
-<li>When you open your EPLAN Electric P8 software for the first time, you will see a window like this:</li>
-</ol>
-<pre><code>&lt;img src="EplanP8License.png" alt="EplanP8License window"&gt; </code></pre>
-<ol start="7">
-<li>Paste the license key that you copied from the keygen into the <code>License number</code> field. Then click on <code>OK</code> button.</li>
-<li>Your EPLAN Electric P8 software is now activated with the license key from the keygen. You can start using it without any limitations.</li>
-<li>If you encounter any problems with the keygen or the activation process, you can refer to the <code>Readme.txt</code> file that came with the keygen. It contains some troubleshooting tips and contact information for support.</li>
-</ol>
- <h2>How to Use EPLAN Electric P8 24 X64 Keygen</h2>
-<p>Now that you have installed and activated your EPLAN Electric P8 software with the keygen, you can start using it for your electrical engineering projects. Here are some of the things you can do with this software:</p>
-<p>EPLAN Electric P8 24 X64 Crack Download<br />
-How to Install EPLAN Electric P8 24 X64 with Keygen<br />
-EPLAN Electric P8 24 X64 Serial Number Generator<br />
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-EPLAN Electric P8 24 X64 Full Version Free Download<br />
-EPLAN Electric P8 24 X64 Patch Update<br />
-EPLAN Electric P8 24 X64 Software Review<br />
-EPLAN Electric P8 24 X64 Tutorial for Beginners<br />
-EPLAN Electric P8 24 X64 Features and Benefits<br />
-EPLAN Electric P8 24 X64 System Requirements<br />
-EPLAN Electric P8 24 X64 vs AutoCAD Electrical<br />
-EPLAN Electric P8 24 X64 Customer Support<br />
-EPLAN Electric P8 24 X64 Price and Discount<br />
-EPLAN Electric P8 24 X64 Trial Version Download<br />
-EPLAN Electric P8 24 X64 User Manual PDF<br />
-EPLAN Electric P8 24 X64 Online Training Course<br />
-EPLAN Electric P8 24 X64 Forum and Community<br />
-EPLAN Electric P8 24 X64 Tips and Tricks<br />
-EPLAN Electric P8 24 X64 Best Practices and Standards<br />
-EPLAN Electric P8 24 X64 Error and Solution<br />
-EPLAN Electric P8 24 X64 Comparison with Other Software<br />
-EPLAN Electric P8 24 X64 Testimonials and Reviews<br />
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-How to Uninstall EPLAN Electric P8 24 X64 with Keygen<br />
-How to Fix EPLAN Electric P8 24 X64 Keygen Not Working Issue<br />
-How to Get EPLAN Electric P8 24 X64 Keygen for Free Legally<br />
-How to Use EPLAN Electric P8 24 X64 Keygen Safely and Securely<br />
-How to Update EPLAN Electric P8 24 X64 Keygen to Latest Version <br />
-How to Contact EPLAN Electric P8 24 X64 Keygen Developer or Seller <br />
-How to Verify EPLAN Electric P8 24 X64 Keygen Authenticity and Validity <br />
-How to Recover Lost or Forgotten EPLAN Electric P8 24 X64 Keygen <br />
-How to Transfer or Share EPLAN Electric P8 24 X64 Keygen with Others</p>
-<ul>
-<li>Create and edit electrical schematics and circuit diagrams with ease. You can use various tools and functions such as drag-and-drop, copy-and-paste, automatic connection lines, cross-references, symbols library, etc.</li>
-<li>Use macros, templates and standardised approaches for efficient engineering. You can save time and effort by reusing existing data and applying predefined settings and rules.</li>
-<li>Generate and export documentation and reports automatically from your project data. You can create bills of materials, terminal diagrams, cable lists, wiring diagrams, etc. with just a few clicks.</li>
-<li>Integrate your data with other disciplines and industries such as automation technology, building technology, service and maintenance, etc. You can exchange data with other software systems such as PLC programming tools, CAD tools, ERP systems, etc.</li>
-<li>Customize your software according to your preferences and needs. You can change the user interface language, units of measurement, display options, etc.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>EPLAN Electric P8 is a powerful software for electrical engineering that can help you plan and design machines and plant systems efficiently and effectively. However, to use this software without any restrictions, you need a valid license key that can activate it.</p>
-<p>EPLAN Electric P8 24 X64 Keygen is a tool that can generate a unique and working license key for your EPLAN Electric P8 software. With this keygen, you can unlock all the features and functions of EPLAN Electric P8 without paying anything.</p>
-<p>In this article, we showed you how to install and use EPLAN Electric P8 24 X64 Keygen, as well as some of its features and benefits. We hope you found this article helpful and informative.</p>
-<p>If you want to try out EPLAN Electric P8 24 X64 Keygen for yourself, you can download it from here (link). We guarantee that it is safe and virus-free. However, if you like this software and want to support its developers, we recommend that you buy a genuine license key from their official website (link).</p>
- <h2>FAQs</h2>
-<p>Here are some of the most frequently asked questions and answers about EPLAN Electric P8 24 X64 Keygen:</p>
- <h3>Q: Is EPLAN Electric P8 24 X64 Keygen legal?</h3>
-<p>A: No, it is not legal. Using a keygen to activate a software without paying for it is considered piracy and theft. It violates the intellectual property rights of the software developers. It may also expose your computer to malware and viruses.</p>
- <h3>Q: Will EPLAN Electric P8 24 X64 Keygen work on other versions of EPLAN Electric P8?</h3>
-<p>A: No, it will not work on other versions of EPLAN Electric P8. This keygen is designed specifically for version 24 X64 of EPLAN Electric P8. If you try to use it on other versions, it may not work or cause errors.</p>
- <h3>Q: How long will EPLAN Electric P8 24 X64 Keygen last?</h3>
-<p>A: The license key generated by EPLAN Electric P8 24 X64 Keygen will last indefinitely unless it is revoked or blocked by the software developers. However, there is no guarantee that this will not happen in the future.</p>
- <h3>Q: Can I update my EPLAN Electric P8 software after using EPLAN Electric P8 24 X64 Keygen?</h3>
-<p>after using EPLAN Electric P8 24 X64 Keygen, but you may need to generate a new license key with the keygen if the old one is no longer valid. You can also check for updates from the software itself or from the official website of EPLAN Software & Service GmbH & Co. KG.</p>
- <h3>Q: Is EPLAN Electric P8 24 X64 Keygen safe and virus-free?</h3>
-<p>A: Yes, EPLAN Electric P8 24 X64 Keygen is safe and virus-free. We have tested it with various antivirus programs and found no threats or malware. However, you should always download it from a trusted source and scan it before using it.</p>
- <h3>Q: Where can I get support for EPLAN Electric P8 24 X64 Keygen?</h3>
-<p>A: If you need any help or support for EPLAN Electric P8 24 X64 Keygen, you can contact us at (email address) or (phone number). We are always happy to assist you with any issues or questions you may have. You can also refer to the <code>Readme.txt</code> file that came with the keygen for more information and tips.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Firmware Nokia X2-01 Rm-709 V8.75 Bi.md b/spaces/raedeXanto/academic-chatgpt-beta/Firmware Nokia X2-01 Rm-709 V8.75 Bi.md
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--- a/spaces/raedeXanto/academic-chatgpt-beta/Firmware Nokia X2-01 Rm-709 V8.75 Bi.md	
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-<br />
-<h1>Firmware Nokia X2-01 RM-709 V8.75 BI: Everything You Need to Know</h1>
-<p>Are you looking for a way to improve the performance and functionality of your Nokia X2-01 RM-709 phone? Do you want to get the latest features and security updates for your device? If yes, then you might be interested in updating your firmware to the latest version available.</p>
-<p>Firmware is a software program that controls the hardware components of your phone. It is responsible for making your phone work properly and efficiently. Firmware also determines how your phone interacts with other devices and networks.</p>
-<h2>firmware nokia x2-01 rm-709 v8.75 bi</h2><br /><p><b><b>DOWNLOAD</b> &#128504;&#128504;&#128504; <a href="https://tinourl.com/2uL19E">https://tinourl.com/2uL19E</a></b></p><br /><br />
-<p>In this article, we will tell you everything you need to know about firmware Nokia X2-01 RM-709 V8.75 BI, which is the latest version released by Nokia for this model. We will explain what firmware is, what Nokia X2-01 RM-709 is, what firmware Nokia X2-01 RM-709 V8.75 BI is, how to download it, how to install it, and how to troubleshoot it. We will also answer some frequently asked questions about this topic.</p>
-<p>So, if you are ready to learn more about firmware Nokia X2-01 RM-709 V8.75 BI, then keep reading!</p>
-<p>How to flash Nokia X2-01 RM-709 stock firmware ROM<br />
-Nokia X2-01 RM-709 official firmware download link<br />
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-Nokia X2-01 RM-709 USB driver and how-to flash guide<br />
-Nokia X2-01 RM-709 firmware update, downgrade or reinstall<br />
-Nokia X2-01 RM-709 software issue, boot issue, IMEI issue or dead issue fix<br />
-Nokia X2-01 RM-709 ROM installation tutorial and tool<br />
-Nokia X2-01 RM-709 battery charge and backup recommendation<br />
-Nokia X2-01 RM-709 firmware oficial download link part 1 and 2<br />
-Nokia X2-01 RM-709 ROM file, tool, driver and manual inside zip archive<br />
-Nokia X2-01 RM-709 firmware repair damaged device<br />
-Nokia X2-01 RM-709 firmware scanned against viruses/malware<br />
-Nokia X2-01 RM-709 easy firmware download folder ID<br />
-Nokia X2-01 RM-709 Trello board link and description<br />
-Nokia X2-01 RM-709 flash file English, Hindi and other local Indian languages<br />
-Nokia X2-01 RM-709 latest firmware file release date and version<br />
-Nokia X2-01 RM-709 stock firmware package extract and install steps<br />
-Nokia X2-01 RM-709 firmware compatibility with different models and variants<br />
-Nokia X2-01 RM-709 firmware features and benefits</p>
- <h2>What is Firmware?</h2>
-<p>Firmware is a type of software that is embedded in a device's memory chip or ROM (Read Only Memory). Firmware provides instructions for how the device's hardware should operate and communicate with other devices and networks.</p>
-<p>Firmware is different from other types of software because it is not usually changed or deleted by the user. Firmware can only be updated or modified by the manufacturer or authorized service provider.</p>
-<p>Firmware updates are important because they can fix bugs, improve performance, add new features, enhance security, and resolve compatibility issues with other devices and networks.</p>
- <h2>What is Nokia X2-01 RM-709?</h2>
-<h3>Features and Specifications</h3>
-<p>Nokia X2-01 RM-709 is a mobile phone that was released by Nokia in 2010. It belongs to the Nokia X series of phones that are designed for music lovers and social media users.</p>
-<p>Nokia X2-01 RM-709 has a QWERTY keyboard that makes typing easier and faster. It also has a 2.4-inch QVGA display that supports 262K colors. It has a 0.3-megapixel VGA camera that can record videos at 15 frames per second.</p>
-<p>Nokia X2-01 RM-709 has a built-in FM radio that can play music without headphones. It also has a 3.5mm audio jack that can connect to any standard headphones or speakers. It has a microSD card slot that can support up to 16GB of external memory.</p>
-<p>Nokia X2-01 RM-709 runs on Symbian Series 40 operating system that offers various applications and games. It also supports Bluetooth 2.1, GPRS/EDGE, USB 2.0, and Ovi Mail services.</p>
-<p>Nokia X2-01 RM-709 has a removable Li-Ion 1020 mAh battery that can provide up to 4 hours of talk time or up to 480 hours of standby time.</p>
- <h3>Advantages and Disadvantages</h3>
-<p>Nokia X2-01 RM-709 has some advantages and disadvantages that you should consider before buying or updating it.</p>
-<p>Some of the advantages are:</p>
-<ul>
-<li>It has a QWERTY keyboard that makes typing easier and faster.</li>
-<li>It has a built-in FM radio that can play music without headphones.</li>
-<li>It has a microSD card slot that can support up to 16GB of external memory.</li>
-<li>It runs on Symbian Series 40 operating system that offers various applications and games.</li>
-<li>It supports Bluetooth 2.1, GPRS/EDGE, USB 2.0, and Ovi Mail services.</li>
-</ul>
-<p>Some of the disadvantages are:</p>
-<ul>
-<li>It has a low-resolution camera that cannot take good quality photos or videos.</li>
-<li>It does not support Wi-Fi or 3G connectivity.</li>
-<li>It does not have GPS or navigation features.</li>
-<li>It does not have a touchscreen or accelerometer sensor.</li>
-<li>It does not support multitasking or video streaming.</li>
-</ul>
- <h2>What is Firmware Nokia X2-01 RM-709 V8.75 BI?</h2>
-<h3>What's New in This Version?</h3>
-<p>Firmware Nokia X2-01 RM-709 V8.75 BI is the latest version of firmware released by Nokia for this model in 2017. It is also known as firmware Nokia X2-01 RM-709 Bahasa Indonesia because it supports Indonesian language along with other languages such as English, Arabic, French, German, Spanish, etc.</p>
-<p>Firmware Nokia X2-01 RM-709 V8.75 BI has some new features and improvements compared to the previous versions such as:</p>
-<ul>
-<li>It fixes some bugs and errors that caused the phone to freeze or restart unexpectedly.</li>
-<li>It improves the stability and performance of the phone's software and hardware.</li>
-<li>It enhances the security and privacy of the phone's data and settings.</li>
-<li>It adds some new applications and games such as Facebook Chat, Twitter Mobile, Opera Mini Browser, etc.</li>
-<li>It updates some existing applications and games such as Ovi Store, Ovi Mail, Ovi Music Player, etc.</li>
-</ul>
- <h3>Why You Should Update Your Firmware?</h3>
-<p>You should update your firmware to firmware Nokia X2-01 RM-709 V8.75 BI because it can offer you some benefits such as:</p>
-<ul>
-<li>It can fix some bugs and errors that caused your phone to freeze or restart unexpectedly.</li>
-<li>It can improve the stability and performance of your phone's software and hardware.</li>
-<li>It can enhance the security and privacy of your phone's data and settings.</li>
-<li>It can add some new features and functions that can make your phone more useful and enjoyable.</li>
-<li>It can update some existing features and functions that can make your phone more compatible and efficient.</li>
-</ul>
- ```html <h2>How to Download Firmware Nokia X2-01 RM-709 V8.75 BI?</h2>
-<h3>Official Link</h3>
-<p>The official link to download firmware Nokia X2-01 RM-709 V8.75 BI is from the Nokia website. You can access the link by following these steps:</p>
-<ol>
-<li>Go to the Nokia website at <a href="https://www.nokia.com/phones/en_int/support">https://www.nokia.com/phones/en_int/support</a>.</li>
-<li>Select your phone model from the list or type it in the search box.</li>
-<li>Click on the Software Update option on the left side menu.</li>
-<li>Click on the Download button under the Firmware Nokia X2-01 RM-709 V8.75 BI section.</li>
-<li>Save the file to your computer or a USB drive.</li>
-</ol>
- <h3>Alternative Links</h3>
-<p>If you cannot access the official link or if you want to download firmware Nokia X2-01 RM-709 V8.75 BI from other sources, you can use some alternative links such as:</p>
-<ul>
-<li><a href="https://firmwarefile.com/nokia-x2-01-rm-709">https://firmwarefile.com/nokia-x2-01-rm-709</a>: This is a website that provides various firmware files for different devices. You can download firmware Nokia X2-01 RM-709 V8.75 BI from this link by clicking on the Get Link button and following the instructions.</li>
-<li><a href="https://forum.gsmhosting.com/vbb/f641/nokia-x2-01-rm-709-v8-75-latest-flash-file-2267385/">https://forum.gsmhosting.com/vbb/f641/nokia-x2-01-rm-709-v8-75-latest-flash-file-2267385/</a>: This is a forum where users share and discuss various topics related to mobile phones. You can download firmware Nokia X2-01 RM-709 V8.75 BI from this link by clicking on the X2-01RM709 link in the second post and following the instructions.</li>
-<li><a href="https://firmwareoficial.com/english/nokia/nokia-x2-01-rm-709/">https://firmwareoficial.com/english/nokia/nokia-x2-01-rm-709/</a>: This is another website that provides official firmware files for different devices. You can download firmware Nokia X2-01 RM-709 V8.75 BI from this link by clicking on the Download button and following the instructions.</li>
-</ul>
- <h2>How to Install Firmware Nokia X2-01 RM-709 V8.75 BI?</h2>
-<h3>Requirements and Precautions</h3>
-<p>Before you install firmware Nokia X2-01 RM-709 V8.75 BI on your phone, you need to meet some requirements and take some precautions such as:</p>
-<ul>
-<li>You need to have a compatible USB cable that can connect your phone to your computer.</li>
-<li>You need to have a backup of your phone's data and settings in case something goes wrong during the installation process.</li>
-<li>You need to have enough battery power on your phone and your computer to avoid interruption during the installation process.</li>
-<li>You need to have enough free space on your phone's memory or a microSD card to store the firmware file.</li>
-<li>You need to turn off your phone and remove the SIM card and the battery before installing the firmware.</li>
-<li>You need to follow the installation instructions carefully and do not disconnect or turn off your phone or your computer during the installation process.</li>
-</ul>
- <h3>Step by Step Guide</h3>
-<p>To install firmware Nokia X2-01 RM-709 V8.75 BI on your phone, you can follow this step by step guide:</p>
-<ol>
-<li>Download firmware Nokia X2-01 RM-709 V8.75 BI from one of the links mentioned above and save it to your computer or a USB drive.</li>
-<li>Extract the zip file using a software such as WinRAR or 7-Zip and you will get a folder containing several files such as MCU, PPM, CNT, etc.</li>
-<li>Download and install a flash tool such as Miracle Box or Phoenix Service Software on your computer.</li>
-<li>Launch the flash tool and select your phone model from the list or type it in the search box.</li>
-<li>Select Firmware Update option from the menu and browse for the folder containing the firmware files that you extracted earlier.</li>
-<li>Click on Start or Flash button and wait for the flash tool to detect your phone and start installing the firmware.</li>
-<li>Follow the on-screen instructions and do not disconnect or turn off your phone or your computer until the installation is completed.</li>
-<li>When the installation is completed, you will see a message such as Success or Done on the flash tool screen.</li>
-<li>Disconnect your phone from your computer and insert the battery and SIM card back into your phone.</li>
-<li>Turn on your phone and check if everything is working properly.</li>
-</ol>
- <h2>How to Troubleshoot Firmware Nokia X2-01 RM-709 V8.75 BI?</h2>
-<h3>Common Issues and Solutions</h3>
-<p>Sometimes, you may encounter some issues or problems after installing firmware Nokia X2-01 RM-709 V8.75 BI on your phone such as:</p>
-<ul>
-<li>Your phone does not turn on or gets stuck on a logo screen.</li>
-<li>Your phone does not recognize your SIM card or network signal.</li>
-```html <li>Your phone does not run some applications or games properly.</li>
-<li>Your phone has low battery life or overheats frequently.</li>
-</ul>
-<p>To troubleshoot these issues or problems, you can try some solutions such as:</p>
-<ul>
-<li>Restart your phone and check if the issue is resolved.</li>
-<li>Reset your phone to factory settings and check if the issue is resolved.</li>
-<li>Reinstall the firmware using the same or a different flash tool and check if the issue is resolved.</li>
-<li>Download and install a different version of firmware that is compatible with your phone and check if the issue is resolved.</li>
-<li>Contact Nokia customer support or visit a Nokia service center for further assistance.</li>
-</ul>
- <h3>Tips and Tricks</h3>
-<p>To make the most out of your firmware Nokia X2-01 RM-709 V8.75 BI, you can follow some tips and tricks such as:</p>
-<ul>
-<li>Explore the new features and functions that are added or updated by the firmware.</li>
-<li>Customize your phone's settings and preferences according to your needs and preferences.</li>
-<li>Delete or uninstall any unwanted or unused applications or games to free up some space and memory on your phone.</li>
-<li>Update your applications and games regularly to get the latest versions and features.</li>
-<li>Use a good antivirus software to protect your phone from viruses and malware.</li>
-<li>Use a good battery saver app to optimize your phone's battery performance and life.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, firmware Nokia X2-01 RM-709 V8.75 BI is the latest version of firmware released by Nokia for this model in 2017. It has some new features and improvements that can make your phone more useful and enjoyable. It also fixes some bugs and errors that can make your phone more stable and secure.</p>
-<p>To download firmware Nokia X2-01 RM-709 V8.75 BI, you can use the official link from the Nokia website or some alternative links from other sources. To install firmware Nokia X2-01 RM-709 V8.75 BI, you need to have a compatible USB cable, a backup of your phone's data and settings, enough battery power and free space, and a flash tool such as Miracle Box or Phoenix Service Software. To troubleshoot firmware Nokia X2-01 RM-709 V8.75 BI, you can try some solutions such as restarting your phone, resetting your phone, reinstalling the firmware, or contacting Nokia customer support.</p>
-<p>We hope this article has helped you learn everything you need to know about firmware Nokia X2-01 RM-709 V8.75 BI. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about firmware Nokia X2-01 RM-709 V8.75 BI:</p>
-<ol>
-<li><b>What is the difference between firmware and software?</b><br>Firmware is a type of software that is embedded in a device's memory chip or ROM (Read Only Memory). Firmware provides instructions for how the device's hardware should operate and communicate with other devices and networks. Firmware is different from other types of software because it is not usually changed or deleted by the user. Firmware can only be updated or modified by the manufacturer or authorized service provider.</li>
-<li><b>How do I check my firmware version?</b><br>You can check your firmware version by following these steps:<br>- Turn on your phone and go to Menu > Settings > Phone > Device updates.<br>- Select Options > Check for updates.<br>- You will see your current firmware version on the screen.<br>- You can also compare it with the latest firmware version available on the Nokia website or other sources.</li>
-<li><b>How do I backup my phone's data and settings?</b><br>You can backup your phone's data and settings by following these steps:<br>- Connect your phone to your computer using a USB cable.<br>- Launch Nokia Suite or Ovi Suite on your computer.<br>- Select Backup option from the menu.<br>- Select what data and settings you want to backup such as contacts, messages, photos, videos, etc.<br>- Click on Start backup button and wait for the process to complete.<br>- You can also use a microSD card or a cloud service to backup your phone's data and settings.</li>
-<li><b>How do I reset my phone to factory settings?</b><br>You can reset your phone to factory settings by following these steps:<br>- Turn off your phone and remove the SIM card and battery.<br>- Press and hold * + 3 + Call + Power buttons simultaneously until you see the Nokia logo on the screen.<br>- Release the buttons and wait for the phone to restart.<br>- You will see a message asking you to confirm the reset.<br>- Select Yes option and wait for the process to complete.<br>- You can also go to Menu > Settings > Restore factory settings option on your phone.</li>
-<li><b>How do I contact Nokia customer support?</b><br>You can contact Nokia customer support by following these steps:<br>- Go to the Nokia website at <a href="https://www.nokia.com/phones/en_int/support">https://www.nokia.com/phones/en_int/support</a>.<br>- Select Contact us option from the menu.<br>- Choose your country or region from the list.<br>- You will see various options to contact Nokia customer support such as phone number, email address, live chat, etc.<br>- Choose the option that suits you best and follow the instructions.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Fondamenti Di Chimica Organica Mcmurry Pdf PORTABLE Download.md b/spaces/raedeXanto/academic-chatgpt-beta/Fondamenti Di Chimica Organica Mcmurry Pdf PORTABLE Download.md
deleted file mode 100644
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--- a/spaces/raedeXanto/academic-chatgpt-beta/Fondamenti Di Chimica Organica Mcmurry Pdf PORTABLE Download.md	
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-<br />
-<h1>Fondamenti di Chimica Organica McMurry PDF Download</h1>
-<p>If you are looking for a reliable and comprehensive textbook on organic chemistry, you might want to consider <strong>Fondamenti di Chimica Organica McMurry</strong>. This book, written by John McMurry, is one of the most popular and widely used books on the subject. In this article, we will tell you what this book is about, why you should download it in PDF format, and how you can do that.</p>
- <h2>What is Fondamenti di Chimica Organica McMurry?</h2>
-<p><strong>Fondamenti di Chimica Organica McMurry</strong> is the Italian edition of <em>Organic Chemistry</em>, a textbook written by John McMurry, a professor emeritus of chemistry at Cornell University. The book covers all the essential topics of organic chemistry, from the structure and bonding of organic molecules to the reactions and mechanisms of organic synthesis. The book also includes chapters on biochemistry, natural products, polymers, and spectroscopy.</p>
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- <h3>A comprehensive textbook on organic chemistry</h3>
-<p>One of the main features of <strong>Fondamenti di Chimica Organica McMurry</strong> is its comprehensiveness. The book consists of 26 chapters and over 1300 pages, covering all the major areas of organic chemistry. The book is divided into six parts:</p>
-<ul>
-<li>Part I: Structure and Bonding</li>
-<li>Part II: Functional Groups and Reactions</li>
-<li>Part III: Organic Synthesis</li>
-<li>Part IV: Biomolecules and Bioorganic Chemistry</li>
-<li>Part V: Special Topics in Organic Chemistry</li>
-<li>Part VI: Spectroscopy</li>
-</ul>
-<p>The book also provides appendices on nomenclature, physical constants, answers to selected problems, and a glossary.</p>
- <h3>A useful resource for students and teachers</h3>
-<p><strong>Fondamenti di Chimica Organica McMurry</strong> is not only a comprehensive textbook but also a useful resource for students and teachers of organic chemistry. The book offers several features that enhance the learning experience, such as:</p>
-<ul>
-<li>Clear explanations and examples that illustrate the concepts and principles of organic chemistry.</li>
-<li>Learning objectives and summaries that highlight the key points of each chapter.</li>
-<li>Concept maps that show the connections between different topics.</li>
-<li>Problem-solving strategies that guide the students through the steps of solving organic chemistry problems.</li>
-<li>Exercises and problems that test the students' understanding and application of organic chemistry.</li>
-<li>Solutions manual that provides detailed answers to selected problems.</li>
-<li>Online resources that include animations, videos, quizzes, flashcards, and more.</li>
-</ul>
- <h2>Why should you download Fondamenti di Chimica Organica McMurry PDF?</h2>
-<p>If you are interested in learning or teaching organic chemistry, you might want to download <strong>Fondamenti di Chimica Organica McMurry</strong> in PDF format. There are several reasons why downloading this book in PDF format can be beneficial for you, such as:</p>
- <h3>To learn the fundamentals of organic chemistry</h3>
-<p><strong>Fondamenti di Chimica Organica McMurry</strong> is a book that covers all the basics of organic chemistry. By downloading this book in PDF format, you can access it anytime and anywhere on your computer or mobile device. You can also print it out if you prefer to read it on paper. This way, you can learn the fundamentals of organic chemistry at your own pace and convenience.</p>
- <h3>To access clear explanations and examples</h3>
-<p><strong>Fondamenti di Chimica Organica McMurry</strong> is a book that provides clear explanations and examples for each topic of organic chemistry. By downloading this book in PDF format, you can easily follow along with the text and diagrams on your screen. You can also zoom in or out to adjust the size of the text and images according to your preference. This way, you can access clear explanations and examples that will help you understand organic chemistry better.</p>
- <h3>To practice with exercises and solutions</h3>
-<p><strong>Fondamenti di Chimica Organica McMurry</strong> is a book that offers a lot of exercises and problems for each chapter of organic chemistry. By downloading this book in PDF format, you can practice with these exercises and problems on your device or on paper. You can also check your answers with the solutions manual that is included in the PDF file. This way, you can practice with exercises and solutions that will help you improve your skills and knowledge in organic chemistry.</p>
- <h2>How can you download Fondamenti di Chimica Organica McMurry PDF?</h2>
-<p>If you want to download <strong>Fondamenti di Chimica Organica McMurry</strong> in PDF format, there are several ways you can do that. Here are some of the possible sources where you can find this book in PDF format:</p>
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- <h3>The official website of the publisher</h3>
-<p>The publisher of <strong>Fondamenti di Chimica Organica McMurry</strong> is Zanichelli Editore S.p.A., an Italian publishing company that specializes in scientific books. You can visit their official website at <a href="https://www.zanichelli.it/">https://www.zanichelli.it/</a>. On their website, you can search for this book by its title or ISBN (9788808622159). You can also browse their catalog by subject or author. Once you find this book on their website, you can either buy it online or request a free trial access to their digital platform ZTE (Zanichelli Test Editor). If you choose to buy it online, you will receive an email with a link to download the PDF file after completing your payment. If you choose to request a free trial access to ZTE, you will receive an email with a link to register on their platform and access their digital resources for this book.</p>
- <h3>The online platforms of the libraries</h3>
-<p>If you have access to a library that has a subscription to an online platform that offers <strong>Fondamenti di Chimica Organica McMurry</strong>, you can also download this book in PDF format from there. Some of the possible online platforms that might have this book are:</p>
-<ul>
-<li>Ebscohost (https://www.ebsco.com/)</li>
-<li>JSTOR (https://www.jstor.org/)</li>
-<li>SpringerLink (https://link.springer.com/)</li>
-<li>ScienceDirect (https://www.sciencedirect.com/)</li>
-<li>Oxford Academic (https://academic.oup.com/)</li>
-<li>Taylor & Francis Online (https://www.tandfonline.com/)</li>
-<li>Cambridge Core (https://www.cambridge.org/core)</li>
-<li>SAGE Journals (https://journals.sagepub.com/)</li>
-<li>MUSE (https://muse.jhu.edu/)</li>
-<li>Gale (https://www.gale.com/)</li>
-</ul>
-<p>To download this book from these online platforms, you need to visit their websites and log in with your library credentials. Then, you need to search for this book by its title or ISBN (9788808622159). Once you find this book on their websites, you need to click on the download button or link to get the PDF file.</p>
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- <table border="1">
-<tr><th>Name of University</th><th>Website Address</th></tr>
-<tr><td>Università degli Studi di Bologna</td><td><a href="https://www.unibo.it/">https://www.unibo.it/</a></td></tr>
-<tr><td>Università degli Studi di Milano</td><td><a href="https://www.unimi.it/">https://www.unimi.it/</a></td></tr>
-<tr><td>Università degli Studi di Torino</td><td><a href="https://www.unito.it/">https://www.unito.it/</a></td></tr>
-<tr><td>Università degli Studi di Roma La Sapienza</td><td><a href="https://www.uniroma1.it/">https://www.uniroma1.it/</a></td></tr>
-<tr><td>Università degli Studi di Napoli Federico II</td><td><a href="https://www.unina.it/">https://www.unina.it/</a></td></tr>
-</table>
- <p>To download this book from these academic websites, you need to visit their websites and log in with your university credentials. Then, you need to look for this book in their online catalog or repository. Once you find this book on their websites, you need to click on the download button or link to get the PDF file.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, <strong>Fondamenti di Chimica Organica McMurry</strong> is a great textbook on organic chemistry that covers all the essential topics and provides clear explanations and examples. If you want to download this book in PDF format, you can do so from various sources, such as the official website of the publisher, the online platforms of the libraries, or the academic websites of the universities. By downloading this book in PDF format, you can learn the fundamentals of organic chemistry, access clear explanations and examples, and practice with exercises and solutions.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about <strong>Fondamenti di Chimica Organica McMurry</strong>:</p>
- <h3>Who is the author of Fondamenti di Chimica Organica McMurry?</h3>
-<p>The author of <strong>Fondamenti di Chimica Organica McMurry</strong> is John McMurry, a professor emeritus of chemistry at Cornell University. He has written several books on organic chemistry, such as <em>Fundamentals of Organic Chemistry</em>, <em>Organic Chemistry: A Biological Approach</em>, and <em>The Organic Chemistry of Biological Pathways</em>.</p>
- <h3>What is the ISBN of Fondamenti di Chimica Organica McMurry?</h3>
-<p>The ISBN of <strong>Fondamenti di Chimica Organica McMurry</strong> is 9788808622159. This is the ISBN for the third Italian edition of the book, published by Zanichelli Editore S.p.A. in 2019.</p>
- <h3>How many pages does Fondamenti di Chimica Organica McMurry have?</h3>
-<p><strong>Fondamenti di Chimica Organica McMurry</strong> has 1328 pages. This includes 26 chapters, appendices, a glossary, and an index.</p>
- <h3>How much does Fondamenti di Chimica Organica McMurry cost?</h3>
-<p>The price of <strong>Fondamenti di Chimica Organica McMurry</strong> varies depending on the source and format. On the official website of the publisher, the book costs 65 euros for the printed version and 55 euros for the digital version. On other online platforms or academic websites, the price may differ depending on their policies and availability.</p>
- <h3>Is Fondamenti di Chimica Organica McMurry available in other languages?</h3>
-<p>Yes, <strong>Fondamenti di Chimica Organica McMurry</strong> is available in other languages besides Italian. The original language of the book is English, and it has been translated into Spanish, French, German, Portuguese, Chinese, Japanese, Korean, and Arabic.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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diff --git a/spaces/rayan-saleh/whisper2notion/app-network.py b/spaces/rayan-saleh/whisper2notion/app-network.py
deleted file mode 100644
index 7605c4b126dfc7dac188dce38551ca8ae84d67db..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/app-network.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Run the app with no audio file restrictions, and make it available on the network
-from app import create_ui
-create_ui(-1, server_name="0.0.0.0")
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Contoh Pengisian Format Bos K7a.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Contoh Pengisian Format Bos K7a.md
deleted file mode 100644
index 992218e5fa7ce68f1f28336870383a6f38dfe2d1..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Contoh Pengisian Format Bos K7a.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>contoh pengisian format bos k7a</h2><br /><p><b><b>Download</b> &#10027; <a href="https://urlgoal.com/2uCMzG">https://urlgoal.com/2uCMzG</a></b></p><br /><br />
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diff --git a/spaces/robin0307/MMOCR/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py b/spaces/robin0307/MMOCR/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py
deleted file mode 100644
index 46ca259b3abb8863348f8eef71b0126f77e269eb..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py
+++ /dev/null
@@ -1,58 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py',
-    '../../_base_/schedules/schedule_adam_step_5e.py',
-    '../../_base_/recog_pipelines/sar_pipeline.py',
-    '../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
-    '../../_base_/recog_datasets/academic_test.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-label_convertor = dict(
-    type='AttnConvertor', dict_type='DICT90', with_unknown=True)
-
-model = dict(
-    type='SARNet',
-    backbone=dict(type='ResNet31OCR'),
-    encoder=dict(
-        type='SAREncoder',
-        enc_bi_rnn=False,
-        enc_do_rnn=0.1,
-        enc_gru=False,
-    ),
-    decoder=dict(
-        type='SequentialSARDecoder',
-        enc_bi_rnn=False,
-        dec_bi_rnn=False,
-        dec_do_rnn=0,
-        dec_gru=False,
-        pred_dropout=0.1,
-        d_k=512,
-        pred_concat=True),
-    loss=dict(type='SARLoss'),
-    label_convertor=label_convertor,
-    max_seq_len=30)
-
-data = dict(
-    samples_per_gpu=64,
-    workers_per_gpu=2,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
diff --git a/spaces/rorallitri/biomedical-language-models/logs/3096 Days Torrent.md b/spaces/rorallitri/biomedical-language-models/logs/3096 Days Torrent.md
deleted file mode 100644
index 0ce37f7ab8a0c0c314b6a41c306f4c1b471687f3..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/3096 Days Torrent.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<br />
-<p>With this, your client will be able to download the remaining information (first the .torrent file, then the actual data) using DHT. You can also specify a list of trackers as tr= parameters, for example:</p>
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-<p>How does one approach the towering figure of Adolf Hitler, a figure so out-sized in his cruelty, so colossal in his infamy, that it staggers the imagination to conceive that he actually lived and breathed? Yet he was as human as you and I and gifted with some of the virtues and most of the flaws that flesh is heir to. One could examine him from a historical, psychological, or sociological perspective. Taking a position on Hitler's moral character is not difficult; he was the embodiment of evil. But did he have greatness, as well? His nemesis, Winston Churchill offered this assessment, "If evil can be great, we cannot deny him greatness." He certainly had genius. Not as a painter as he probably wished. His genius lay elsewhere. He was probably the greatest actor of the twentieth-century who used first Germany and then the World as his stage, mesmerizing it with performances of such messianic passion that he transported his audiences away with him to some unattainable inner Valhalla of their own making. Watching one of his performances-I call his speeches performances-because it was not what he said, but how he said it that was extraordinary, one is immediately struck by how well thought out and structured they were, with every gesture and movement of the body orchestrated to give meaning to the the torrent of words pouring forth.<br /><br />Hitler has been well served in cinema. As an enthusiastic cineaste and certifiable egomaniac, he probably would not have been pleased by the portrayals. He likely would have viewed himself as a tragic hero, or worse a misunderstood martyr. He would never have understood that his crimes were too heinous and numerous for cinema to depict him in that light. Some notable interpretations of Der Fuhrer have been, Charles Chaplin, "The Great Dictator" (1940) Ludwig Donath, "The Strange Death of Adolf Hitler" (1943) Luther Adler, "The Desert Fox" (1951) Albin Skoda, "The Last Ten Days" (1955) Richard Basehart, "Hitler" (1962) Billy Frick, "Is Paris Burning?" (1966) Frank Finlay, "The Death of Adolf Hitler" (1973) Alec Guinness, "Hitler: The Last Ten Days" (1973) Ian Bannen, "The Gathering Storm" (1974) Anthony Hopkins, "The Bunker" (1981) Derek Jacobi, "Inside The Third Reich" (1982) Norman Rodway, "The Empty Mirror" (1996) Noah Taylor as a young Hitler in "Max" (2002) Robert Carlyle, "Hitler: The Rise of Evil" (2003) Bruno Ganz, "Der Untergang" (2004). All of these actor have illuminated aspects of the man's complex personality, so in many ways the performer we "like best" as Hitler is the one who best captures those qualities we associate with the man. Alec Guinness best captured my view of Hitler as performing artist. The bunker was his stage and his entourage, his last captive audience for his final, most terrible performance, the initiator of Gotterdammerung. It is an impressive acting turn by Guinness because the script does not allow him to indulge in any great histrionic oration. Instead, he brings all the vanity and egocentricity of a frustrated actor desperately needing the approbation of his audience-however small. Whether launching into a monologue about the great old days of his rise to power, or haranguing his generals to "will" their way to victory, that need for approval, to be the center of attention is like a spoiled child who is never satisfied with the love and attention he receives, can never fill the gaping void that is the core of his existence.<br /><br />Guinness, who made a career both on stage and film portraying "little men" illuminates beautifully the inner life of the man, capturing Hitler's pettiness of spirit, and essential provincialism. A wonderful example of this is the scene where Hitler having decided to kill himself and have his body burned, smugly tells Goebbels who has decided to do the same, that there may not be enough petrol because he is hoarding all of it for himself, all the while spreading gobs of cream on his chocolate cake like a little boy satisfying his sweet tooth. This is for the most part a quieter Hitler than previous shown, and with one notable exception, mercifully free from the usual hysterics and ravings that have long since become cliché. At the same time Guinness manages to capture the drug-ridden, bleary-eyed, broken, ex-dictator shuffling his way to the grave. One could almost feel pity for this creature, if only he himself were not so pitiless. Even at the end he is the eternal actor, never comfortable with being himself, always assuming a new role in order to define not only himself but his relationship to others. Whether playing the charismatic father figure to a still believing Captain Hoffman, (Simon Ward) the sly old matchmaker between General Von Greim, (Eric Porter) and Hanna Reitsch, (Diane Cilento) the betrayed friend and mentor when he hears of Himmler's attempts to negotiated a truce with the allies, or justifying his barbarity ("Nature is cruel, so I too must be cruel!") to his final, penultimate audience, Eva Baum just before they commit suicide, Guinness keeps this Hitler even with his monstrous ego, intensely and uncomfortably human. And with his uncanny chameleon like ability he easily incorporates the man's gestures and mannerisms without ever slipping into caricature. Guinnes always considered his portrayal of Hitler to be among his five greatest film performances and it wouldn't be until he played William Dorrit in "Little Dorrit" (1988) would he have another film role that would challenge his abilities as an actor.</p>
-<p>This starts with a recap of the Nazi rise to power. The opening credits roll as German forces roll through a map and then they retreat back towards Berlin. It's April 20, 1945 and the Russians are closing in on his Berlin bunker. It's the 56th birthday of Adolf Hitler (Alec Guinness) and ten days before he commits suicide. It's a delusional place where Hitler directs non-existent and depleted forces. There is an air of unreality. After Hitler's death, the remaining personnel light up their cigarettes which was forbidden during his life. It's purported to be based on eye-witness accounts of the true events. Guinness brings his legendary acting prowess to the character. It's not the most thrilling story especially since the ending is inevitable. It's a methodical telling. It is the performance that holds it all together.</p>
-<p>Be careful around Adolph Hitler. This man with the mustache will charm you with his charisma, compassion, artistic talents and passion for opera. That's if you are 100% Aryian, don't smoke around him, or have any generational defects. He will also turn on you on a dime, accusing the Germans of inbred disloyalty (reminding us that he was actually Austrian), express his earlier desire to have dropped the European Jews off on Madagascar before realizing that it was easier to simply eradicate them. He even goes as far to praise his British enemies for their obvious loyalty while badmouthing the nationality of the men he commands. In a humorous moment, he laughs about characteristics of both Roosevelt and Churchill. In short, this is a view of a very complex man that you surely do not want to be fooled by.<br /><br />While dozens of biographies have been told (through books and on screen) in regards to the German fuhrer who changed the course of 20th Century history, some are better than others. On screen, Hitler has been portrayed by Bobby Watson ("The Hitler Gang"), Richard Basehart ("Hitler") and TV's "The Bunker" (Anthony Hopkins), among others, and here, it is the extraordinary Sir Alec Guennis who portrays him. Guennis adds a new twist to Hitler, focusing on the man's various personality quirks, both positive and negative, and for that reason, it is easy to see who he was outside of the world's greatest enemy. You have to remind yourself that this is not a man to like in any circumstance.<br /><br />Guennis takes the role seriously, as far from Charlie Chaplin's "The Great Dictator" as could possibly be. However, Guennis's voice, having brilliantly upgraded many comedies from charming con-men in "The Lavender Hill Mob" and "The Lady Killers" to a chuckling Jacob Marley in "Scrooge" and the blind butler "James Sir Bensonmum" in Neil Simon's "Murder By Death". Even with an Oscar Winning dramatic role ("The Bridge on the River Kwai"), it will be his comedy that he will be remembered for. So to see Guennis as Hitler is difficult to accept in a totally dramatic sense, and you have to keep reminding yourself of whom he is playing. It is almost like watching "Airplane's" Leslie Nielsen act serious in dramas and try not laughing.<br /><br />But Hitler was a serious character in modern history, so the laughs disappear as this man's characteristics are revealed. His anger at the apparent betrayal of the S.S., his own generals and eventually Eva Braun, his long-time mistress, is something to be taken seriously. His command that the bridges be guarded by "Hitler's Youth" and the brief visit by a representative of that youth, a casual conversation with other bunker residents of how they will commit suicide, and finally, the desperation he has in having to face his fate will engross you throughout. To think that this man lived less than 70 years ago and all the villains who have come since is really scary. Then, there is his interest in how he will be remembered, wanting photos to remain behind so he won't face the same fate as Jesus in supposedly being misrepresented by artists. So many individual moments of this film stand out. Actual newsreel footage, stills and newly created black and white footage (done in beautiful sepia tone) make this an attractive film to look at about an unattractive time in our history.<br /><br />All of these qualities make it easy to overlook my misgivings in looking at Guennis's performance in view of his comic genius. In retrospect, he is excellent. The final scene between Hitler and Eva Braun (Doris Kunstmann) is truly haunting (as the other bunker residents in the other room face their last days quite differently than Hitler) and a nice pay-off ends the film with some delicious irony.</p>
-<p></p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Action Replay DS Download Tout ce que vous devez savoir sur ce systme de triche pour Nintendo DS.md b/spaces/rorallitri/biomedical-language-models/logs/Action Replay DS Download Tout ce que vous devez savoir sur ce systme de triche pour Nintendo DS.md
deleted file mode 100644
index 00c8e56d08257839dce3c4ad5c49e1f8ebe60418..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Action Replay DS Download Tout ce que vous devez savoir sur ce systme de triche pour Nintendo DS.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-
-<p>The v1.71 firmware update which, is downloadable from within this article is designed for use with all different releases of the Action Replay DS excluding the Media Edition. The v1.71 firmware update includes support for a number of games including Pokemon Black, Pokemon White and Professor Layton and the Lost Future.</p>
-<h2>action replay ds download</h2><br /><p><b><b>Download Zip</b> &#9989; <a href="https://tinurll.com/2uzmdz">https://tinurll.com/2uzmdz</a></b></p><br /><br />
-<p>Action Replay DS is a powerful cheat code system for Nintendo DS. This program, Action Replay Code Manager, allows you to connect your PC to your Action Replay DS cartridge via USB to add new codes which can be downloaded from various Internet sources using<br/>this software.</p>
-<p>Get the cheat codes for Nintendo DS games from the Web. Add new or remove old cheat codes listed on the Action Replay DS cartridge, while it's connected to your computer via USB slot. Keep track of the favorite code sources and download stuff you need straightforwardly to the cartridge.</p>
-<p>Download <b>Action Replay Code Manager</b> 1.25 from our software library for free. The file size of the latest downloadable setup file is 8.5 MB. Commonly, this program's installer has the following filenames: ActionReplayCodeManager.exe and rundll32.exe etc.</p>
-<p>Our antivirus check shows that this download is malware free. Action Replay Code Manager is categorized as Games. The following versions: 1.2 and 1.0 are the most frequently downloaded ones by the program users. This PC software can be installed on 32-bit versions of Windows XP/Vista/7/8/10/11. This free software is an intellectual property of Datel Design & Development.</p>
-<p>Action Replay DS is a powerful cheat code system for Nintendo DS. This program, Action Replay Code Manager , allows you to connect your PC to your Action Replay DS cartridge via USB to add new codes which can be downloaded from various Internet sources using<br />this software.</p>
-<p>can someone please tell mehow to or where to download or burn the action replay DS not ps123 or xbox DSDSDS :alien: pleassssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssseee</p>
-<p></p>
-<p>Once you have downloaded your new driver, you&apos;ll need to install it. In Windows, use a built-in utility called Device Manager, which allows you to see all of the devices recognized by your system, and the drivers associated with them.</p>
-<p>To start a race, there must be at least 2 "registered" racers. If there are 3 or more DS's connected, but not all of them register, then the unregistered DS's will be CPU's. All the HUD elements will be displayed, and map screen can be toggled, but a replay-like camera will be used, instead of a camera behind the kart. The CPU DS('s) will briefly display a message saying "Unable to enter race. The CPU is runnig this race. Please wait for the next race.", with "running" misspelled as "runnig". There is only 1 race before the game resets to the title screen.</p>
-<p>FOR IMMEDIATE RELEASE:<br><br><br><br><br><br><strong>June 7th, 2006</strong> - With MAX Media Dock for PSP, you can connect any Compact Flash card and even micro hard drives to your Sony handheld. Just snap the Media Dock unit into place, and you can attach a Compact Flash card of up to 4GB in size! MAX Media Dock provides a seamless storage solution too. The memory cards appear as Memory Sticks in your PSP browser.<br><br> The MAX Media Dock pack also comes supplied with our MAX Media Manager PC application, which puts the PSP at the centre of your digital lifestyle. You can use it to convert video files into a format your PSP can understand, then save them onto your Compact Flash card or even a PSP a memory stick for viewing on the move. Accepted formats include AVI, MPG/MPEG, VOB (DVD), WMV, MOV, MPE, 3GP and MP4. You can also automatically put your MP3 music files on your Compact Flash or PSP memory card to play back when you're on the move.<br><br> And that's not all. With MAX Media Manager, you can download saves from the CodeJunkies server quickly and easily, transferring them to your PSP with the USB cable supplied. Bored with a beat-'em-up that you only still play in multiplayer mode? Download a save to open every character. Want to challenge your mates in a new driving title, but always lose because he's been playing the game longer and has unlocked faster vehicles than you? Even the odds with a downloaded save. The possibilities are endless...<br><br> MAX Media Dock for PSP is in the shops this week, and can also be bought direct at www.codejunkies.com or by calling 08456 010 015. It's available in the following packs:<br><br> Base Unit (no CF card supplied: £29.99 UK, $39.99 USA<br> With 1GB Compact Flash card: £54.99 UK, $79.99 USA<br> With 2GB Compact Flash card: £84.99 UK, $119.99 USA<br><strong><br> Note for Editors:</strong><br> For more information on all exciting products from Datel, please contact Ian Osborne on 01785 810816, or email ian.osborne@datel.co.uk.<br><strong><br> About Datel:</strong><br> Datel is the world's leading developer of videogame enhancement products for games consoles. For more than 20 years Datel has stayed at the forefront of the videogames industry by developing products that are innovative, inspirational, technologically advanced and above all, fun to use. It is this philosophy that has seen Datel's products consistently outperform all competitors. The company's famous flagship brand Action Replay is a global best seller on the PSP, PlayStation 2, GameCube, Game Boy Advance/SP, Nintendo DS and Xbox.</p>
-<p>Three ... two ... one! Happy Half Year! Well, 2010 is just abouthalf way behind us now (or, if you're an optimist, half way aheadof us). Either way, why not make a half-resolution to try out somenew downloadable games? This week Jett Rocket zooms onto theWiiWareTM service to thwart the plans of the awesomely named PowerPlant Posse. The Nintendo 64TM classic Mario TennisTM on the Virtual ConsoleTM service reminds tennis fans that most matches last less than11 hours. And Maestro! Green Groove on the Nintendo DSiWareTM service lets you become a virtuoso, as everything you do inthe game becomes music.</p>
-<p>We've also got new arrivals in the "Big Name Games" section ofthe WarioWareTM: D.I.Y. series, which features downloadable microgames fromsome of the industry's most talented figures. This week we havesome new additions from the Treehouse, Nintendo's game localizationteam. New "Big Name Games" will be added every Monday through July26, all available to download for zero Nintendo DSi PointsTM to anyone with broadband Internet access and a copy ofeither WarioWare: D.I.Y. for the Nintendo DSTM family of systems or WarioWare: D.I.Y. Showcase software forthe WiiWare service. Visit the Nintendo Channel on your WiiTM system today to learn more about these games.</p>
-<p><strong>Description:</strong> Jump and run and fly. Let Jett Rocketexplore the beautiful planet of Yoroppa and find out why it hasbeen attacked by the evil Power Plant Posse. Visit tropicalislands, poisonous swamps and freezingly cold glaciers. Ride theocean on your Jet Boat, parachute from giant cliffs, speed througharctic regions on your Jet Snowboard or skyrocket with your JetTurbine. You will need everything you've got to fight back and saveYoroppa. Just be Jett Rocket in this epic futuristic full 3D actionadventure.</p>
-<p>Nintendo adds new titles to the Nintendo DSi Shop and the WiiShop Channel at 9 a.m. Pacific time on Mondays. Users withbroadband Internet access can redeem Wii Points or Nintendo DSiPoints to download the games. Wii Points can be purchased in theWii Shop Channel. Nintendo DSi Points can be purchased in theNintendo DSi Shop. A Nintendo Points CardTM can be purchased at retail locations. All points from oneNintendo Points Card must be redeemed in either the Nintendo DSiShop or the Wii Shop Channel. They are not transferable and cannotbe divided between the two systems.</p>
-<p>This paper compares eight reinforcement learning frameworks:<i>adaptive heuristic critic</i> (<i>AHC</i>)learning due to Sutton,<i>Q-learning</i>due to Watkins, and three extensions to both basic methods for speeding up learning. The three extensions are experience replay, learning action models for planning, and teaching. The frameworks were investigated using connectionism as an approach to generalization. To evaluate the performance of different frameworks, a dynamic environment was used as a testbed. The environment is moderately complex and nondeterministic. This paper describes these frameworks and algorithms in detail and presents empirical evaluation of the frameworks.</p>
-<p>A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space (Schrittwieser et al., 2020). For instance, unlike in chess, where each move results in a predictable board layout, Atari games (as well as real-world scenarios) involve complex environments with unknown action consequences. MuZero can perform impressively in these environments using only three functions. These functions first encode the environment, then compute potential actions and rewards, and then finally predict an estimation of the future environment. These three functions are both general and specific enough to allow learning across a variety of tasks without overgeneralization across different contexts.</p>
-<p>MuZero mirrors hippocampus processing. <strong>(Left)</strong> Functions of muZero. <strong>(Right)</strong> Analogous/similar hippocampal functions. <strong>(Left top)</strong> In muZero, the representation functions maps the initial state, such as the position on a checker board, to an internal hidden state that emphasizes important features of the environment. <strong>(Right top)</strong> A similar processing in the extrahippocampal system (EHPCs) represents the environment in a cognitive map as a serious of cell positions encoded by grid and place cells. Place cells tend to have fields clustered around the location of a reward, such as the destination of a candy shop. <strong>(Left center)</strong> From the hidden state, a prediction function computes potential actions (policies) and rewards (values) of the location in the current state. <strong>(Right center)</strong> In the EHPCs, place cells encode features of the environment in relation to their predictive relationship with other features, such as the potential presence of reward. Additionally, state prediction in HPC (via theta sequences) can be coupled through theta coordination coherence to other areas of the brain that process action and reward. <strong>(Bottom left)</strong> In muZero, the dynamics function calculates reward (such as a captured checkers piece) and computes a new hidden state. <strong>(Bottom right)</strong> In the EHPCs, offline replay, which occurs during periods of sleep, emphasizes locations of previous reward. During replay, events can be selectively sampled from visited event space, with priority given to representations or locations that correspond with reward. Reward-predictive representations can then be compressed into a lower dimensional space. Replay can be comprised of novel state configurations that have not been experienced, such as a novel route to the reward location.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ryansilk/quantycs/StreamLit/quantycs/6_Privacy Policy.py b/spaces/ryansilk/quantycs/StreamLit/quantycs/6_Privacy Policy.py
deleted file mode 100644
index 34ea32f55693819689d0c9f62310ad9cadc8d202..0000000000000000000000000000000000000000
--- a/spaces/ryansilk/quantycs/StreamLit/quantycs/6_Privacy Policy.py	
+++ /dev/null
@@ -1,6 +0,0 @@
-import streamlit as st
-from streamlit_option_menu import option_menu
-
-st.title('Contact Us')
-
-st.sidebar.success("Select Stock Data Below")
diff --git a/spaces/sagarkarn/text2image/README.md b/spaces/sagarkarn/text2image/README.md
deleted file mode 100644
index 46b936241c9e70194ff6f9bfa521896565b80b44..0000000000000000000000000000000000000000
--- a/spaces/sagarkarn/text2image/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stabilityai Stable Diffusion 2
-emoji: 💻
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-duplicated_from: xnetba/text2image
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sasha/Image_Upscaling_Restoration_Colorization/app.py b/spaces/sasha/Image_Upscaling_Restoration_Colorization/app.py
deleted file mode 100644
index 5d011a75bb7c05b2710954ce49bb98976270eb60..0000000000000000000000000000000000000000
--- a/spaces/sasha/Image_Upscaling_Restoration_Colorization/app.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os, pathlib
-import cv2
-import gradio as gr
-import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from realesrgan.utils import RealESRGANer
-os.system("hub install deoldify==1.0.1")
-import paddlehub as hub
-os.system("pip freeze")
-
-
-# download weights
-if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
-
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-
-os.makedirs('output', exist_ok=True)
-
-colorizer = hub.Module(name='deoldify')
-render_factor=5
-
-def colorize_image(image):
-    color_image = colorizer.predict(image)
-    return color_image
-
-
-def inference(img, scale):
-    print(img, scale)
-    try:
-        extension = os.path.splitext(os.path.basename(str(img)))[1]
-        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
-        if len(img.shape) == 3 and img.shape[2] == 4:
-            img_mode = 'RGBA'
-        elif len(img.shape) == 2:  # for gray inputs
-            img_mode = None
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-        else:
-            img_mode = None
-
-        h, w = img.shape[0:2]
-        if h < 300:
-            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
-        face_enhancer = GFPGANer(model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
-            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-        except RuntimeError as error:
-            print('Error', error)
-
-        try:
-            if scale != 2:
-                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
-                h, w = img.shape[0:2]
-                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
-        except Exception as error:
-            print('wrong scale input.', error)
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'png'
-        else:
-            extension = 'jpg'
-        save_path = f'output/out.{extension}'
-        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-        cv2.imwrite(save_path, output)
-        print('upsampled image!')
-        output_img = colorize_image(save_path)
-        outputim = output_img[1]
-        print(type(outputim))
-        print('colorized image!')
-        return pathlib.Path(outputim)
-    except Exception as error:
-        print('global exception', error)
-        return None, None
-
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Image Upscaling, Restoration and Colorization")
-    gr.Markdown("## Choose an upscaling factor below and colorize your image!")
-    with gr.Row():
-        with gr.Column():
-            in_image = gr.inputs.Image(type="filepath", label="Input")
-            factor = gr.Slider(2, 10, value=2, step = 1, label="Rescaling factor")
-            #gr.inputs.Number(, default=2)
-            btn = gr.Button("Upscale and Colorize!")
-        with gr.Column():
-            gallery = gr.Image()
-            #out_image = gr.outputs.Image(type="numpy", label="Output (The whole image)")
-            #dld = gr.outputs.File(label="Download the output image")
-    btn.click(fn=inference, inputs=[in_image,factor], outputs=gallery)
-
-    
-    gr.Markdown("Try different upsampling values to see which gives the best colorized output!")
-    gr.Markdown("If the connection errors out, try a smaller upsampling value.")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/The Order 1886 Pc Torrent 26.md b/spaces/scedlatioru/img-to-music/example/The Order 1886 Pc Torrent 26.md
deleted file mode 100644
index 7d2d14fd7aae2f54223ebb35cd09fa890e994f82..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/The Order 1886 Pc Torrent 26.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-<h2>The Order 1886 Pc Torrent 26</h2><br /><p><b><b>Download File</b> &#10004;&#10004;&#10004; <a href="https://gohhs.com/2uEzeI">https://gohhs.com/2uEzeI</a></b></p><br /><br />
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diff --git a/spaces/sdhsdhk/bingo111/src/components/chat-list.tsx b/spaces/sdhsdhk/bingo111/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
-  messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
-  if (!messages.length) {
-    return null
-  }
-
-  return (
-    <div className="chat-container relative flex flex-col">
-      {messages.map((message, index) => (
-        <React.Fragment key={index}>
-          <ChatMessage message={message} />
-          {index < messages.length - 1 && (
-            <Separator className="my-2" />
-          )}
-        </React.Fragment>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/seyia92coding/video-games-recommender/README.md b/spaces/seyia92coding/video-games-recommender/README.md
deleted file mode 100644
index b8d6a75aa80cf79d21c2ef59a9428fad1535f1fe..0000000000000000000000000000000000000000
--- a/spaces/seyia92coding/video-games-recommender/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Video Games Recommender
-emoji: 🏃
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/utils/monotonic_align/core.c b/spaces/shivammehta25/Diff-TTSG/diff_ttsg/utils/monotonic_align/core.c
deleted file mode 100644
index ab8584e6fd958c4b7682e4f6530d347b0421dacb..0000000000000000000000000000000000000000
--- a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/utils/monotonic_align/core.c
+++ /dev/null
@@ -1,23568 +0,0 @@
-/* Generated by Cython 0.29.35 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "depends": [],
-        "name": "diff_ttsg.utils.monotonic_align.core",
-        "sources": [
-            "diff_ttsg/utils/monotonic_align/core.pyx"
-        ]
-    },
-    "module_name": "diff_ttsg.utils.monotonic_align.core"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
-    #error Cython requires Python 2.6+ or Python 3.3+.
-#else
-#define CYTHON_ABI "0_29_35"
-#define CYTHON_HEX_VERSION 0x001D23F0
-#define CYTHON_FUTURE_DIVISION 1
-#include <stddef.h>
-#ifndef offsetof
-  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
-  #if PY_VERSION_HEX >= 0x02070000
-    #define HAVE_LONG_LONG
-  #endif
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#ifdef PYPY_VERSION
-  #define CYTHON_COMPILING_IN_PYPY 1
-  #define CYTHON_COMPILING_IN_PYSTON 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #if PY_VERSION_HEX < 0x03090000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PYSTON_VERSION)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_PYSTON 1
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PY_NOGIL)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_PYSTON 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_NOGIL 1
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #ifndef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_PYSTON 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #if PY_VERSION_HEX < 0x02070000
-    #undef CYTHON_USE_PYTYPE_LOOKUP
-    #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
-    #define CYTHON_USE_PYTYPE_LOOKUP 1
-  #endif
-  #if PY_MAJOR_VERSION < 3
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #if PY_VERSION_HEX < 0x02070000
-    #undef CYTHON_USE_PYLONG_INTERNALS
-    #define CYTHON_USE_PYLONG_INTERNALS 0
-  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
-    #define CYTHON_USE_PYLONG_INTERNALS (PY_VERSION_HEX < 0x030C00A5)
-  #endif
-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
-    #undef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #elif !defined(CYTHON_USE_UNICODE_WRITER)
-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #if PY_VERSION_HEX >= 0x030B00A4
-    #undef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 0
-  #elif !defined(CYTHON_FAST_THREAD_STATE)
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL (PY_VERSION_HEX < 0x030A0000)
-  #endif
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
-  #endif
-  #ifndef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS ((PY_VERSION_HEX >= 0x030600B1) && (PY_VERSION_HEX < 0x030C00A5))
-  #endif
-  #if PY_VERSION_HEX >= 0x030B00A4
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-           typedef unsigned char     uint8_t;
-           typedef unsigned int      uint32_t;
-        #else
-           typedef unsigned __int8   uint8_t;
-           typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-#else
-   #include <stdint.h>
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus) && __cplusplus >= 201103L
-    #if __has_cpp_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH [[fallthrough]]
-    #elif __has_cpp_attribute(clang::fallthrough)
-      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-    #elif __has_cpp_attribute(gnu::fallthrough)
-      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__ ) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
-  #define Py_OptimizeFlag 0
-#endif
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-  #define __Pyx_DefaultClassType PyClass_Type
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject* co=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(0))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto cleanup_code_too;
-        if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here
-        if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too;
-        Py_XDECREF((PyObject*)co);
-        co = (PyCodeObject*)call_result;
-        call_result = NULL;
-        if (0) {
-            cleanup_code_too:
-            Py_XDECREF((PyObject*)co);
-            co = NULL;
-        }
-        end:
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(call_result);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return co;
-    }
-#else
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-  #define __Pyx_DefaultClassType PyType_Type
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_FAST_PYCCALL
-#define __Pyx_PyFastCFunction_Check(func)\
-    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))
-#else
-#define __Pyx_PyFastCFunction_Check(func) 0
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1
-  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)
-  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)
-  #define PyMem_RawFree(p)             PyMem_Free(p)
-#endif
-#if CYTHON_COMPILING_IN_PYSTON
-  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
-#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
-  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
-  #define __Pyx_PyThreadState_Current _PyThreadState_Current
-#endif
-#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
-#include "pythread.h"
-#define Py_tss_NEEDS_INIT 0
-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
-  *key = PyThread_create_key();
-  return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
-  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
-  *key = Py_tss_NEEDS_INIT;
-  return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
-  PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
-  return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
-  PyThread_delete_key(*key);
-  *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
-  return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
-  return PyThread_get_key_value(*key);
-}
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-#else
-#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-  #define CYTHON_PEP393_ENABLED 1
-  #if PY_VERSION_HEX >= 0x030C0000
-    #define __Pyx_PyUnicode_READY(op)       (0)
-  #else
-    #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
-                                                0 : _PyUnicode_Ready((PyObject *)(op)))
-  #endif
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
-  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
-  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
-  #if PY_VERSION_HEX >= 0x030C0000
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
-  #else
-    #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length))
-    #else
-    #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
-    #endif
-  #endif
-#else
-  #define CYTHON_PEP393_ENABLED 0
-  #define PyUnicode_1BYTE_KIND  1
-  #define PyUnicode_2BYTE_KIND  2
-  #define PyUnicode_4BYTE_KIND  4
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
-  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
-#else
-  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
-      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
-  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
-  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
-  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
-#endif
-#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
-#else
-  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
-  #define PyObject_ASCII(o)            PyObject_Repr(o)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBaseString_Type            PyUnicode_Type
-  #define PyStringObject               PyUnicodeObject
-  #define PyString_Type                PyUnicode_Type
-  #define PyString_Check               PyUnicode_Check
-  #define PyString_CheckExact          PyUnicode_CheckExact
-#ifndef PyObject_Unicode
-  #define PyObject_Unicode             PyObject_Str
-#endif
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
-  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
-#else
-  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
-  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
-#endif
-#ifndef PySet_CheckExact
-  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
-#endif
-#if PY_VERSION_HEX >= 0x030900A4
-  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
-  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
-#else
-  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
-  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
-#endif
-#if CYTHON_ASSUME_SAFE_MACROS
-  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
-#else
-  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyIntObject                  PyLongObject
-  #define PyInt_Type                   PyLong_Type
-  #define PyInt_Check(op)              PyLong_Check(op)
-  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
-  #define PyInt_FromString             PyLong_FromString
-  #define PyInt_FromUnicode            PyLong_FromUnicode
-  #define PyInt_FromLong               PyLong_FromLong
-  #define PyInt_FromSize_t             PyLong_FromSize_t
-  #define PyInt_FromSsize_t            PyLong_FromSsize_t
-  #define PyInt_AsLong                 PyLong_AsLong
-  #define PyInt_AS_LONG                PyLong_AS_LONG
-  #define PyInt_AsSsize_t              PyLong_AsSsize_t
-  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
-  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
-  #define PyNumber_Int                 PyNumber_Long
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBoolObject                 PyLongObject
-#endif
-#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
-  #ifndef PyUnicode_InternFromString
-    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
-  #endif
-#endif
-#if PY_VERSION_HEX < 0x030200A4
-  typedef long Py_hash_t;
-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsHash_t
-#else
-  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
-  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsSsize_t
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
-#else
-  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
-#endif
-#if CYTHON_USE_ASYNC_SLOTS
-  #if PY_VERSION_HEX >= 0x030500B1
-    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
-    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
-  #else
-    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
-  #endif
-#else
-  #define __Pyx_PyType_AsAsync(obj) NULL
-#endif
-#ifndef __Pyx_PyAsyncMethodsStruct
-    typedef struct {
-        unaryfunc am_await;
-        unaryfunc am_aiter;
-        unaryfunc am_anext;
-    } __Pyx_PyAsyncMethodsStruct;
-#endif
-
-#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS)
-  #if !defined(_USE_MATH_DEFINES)
-    #define _USE_MATH_DEFINES
-  #endif
-#endif
-#include <math.h>
-#ifdef NAN
-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
-  float value;
-  memset(&value, 0xFF, sizeof(value));
-  return value;
-}
-#endif
-#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
-#define __Pyx_truncl trunc
-#else
-#define __Pyx_truncl truncl
-#endif
-
-#define __PYX_MARK_ERR_POS(f_index, lineno) \
-    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
-#define __PYX_ERR(f_index, lineno, Ln_error) \
-    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
-
-#ifndef __PYX_EXTERN_C
-  #ifdef __cplusplus
-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
-#endif
-
-#define __PYX_HAVE__diff_ttsg__utils__monotonic_align__core
-#define __PYX_HAVE_API__diff_ttsg__utils__monotonic_align__core
-/* Early includes */
-#include <string.h>
-#include <stdio.h>
-#include "numpy/arrayobject.h"
-#include "numpy/ndarrayobject.h"
-#include "numpy/ndarraytypes.h"
-#include "numpy/arrayscalars.h"
-#include "numpy/ufuncobject.h"
-
-    /* NumPy API declarations from "numpy/__init__.pxd" */
-    
-#include "pythread.h"
-#include <stdlib.h>
-#include "pystate.h"
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
-    (sizeof(type) < sizeof(Py_ssize_t))  ||\
-    (sizeof(type) > sizeof(Py_ssize_t) &&\
-          likely(v < (type)PY_SSIZE_T_MAX ||\
-                 v == (type)PY_SSIZE_T_MAX)  &&\
-          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
-                                v == (type)PY_SSIZE_T_MIN)))  ||\
-    (sizeof(type) == sizeof(Py_ssize_t) &&\
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
-    return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
-    #include <cstdlib>
-    #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
-    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
-    #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
-    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
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-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-static PyObject *__pyx_m = NULL;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_cython_runtime = NULL;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static PyObject *__pyx_empty_unicode;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-/* Header.proto */
-#if !defined(CYTHON_CCOMPLEX)
-  #if defined(__cplusplus)
-    #define CYTHON_CCOMPLEX 1
-  #elif defined(_Complex_I)
-    #define CYTHON_CCOMPLEX 1
-  #else
-    #define CYTHON_CCOMPLEX 0
-  #endif
-#endif
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #include <complex>
-  #else
-    #include <complex.h>
-  #endif
-#endif
-#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
-  #undef _Complex_I
-  #define _Complex_I 1.0fj
-#endif
-
-
-static const char *__pyx_f[] = {
-  "diff_ttsg/utils/monotonic_align/core.pyx",
-  "__init__.pxd",
-  "stringsource",
-  "type.pxd",
-};
-/* NoFastGil.proto */
-#define __Pyx_PyGILState_Ensure PyGILState_Ensure
-#define __Pyx_PyGILState_Release PyGILState_Release
-#define __Pyx_FastGIL_Remember()
-#define __Pyx_FastGIL_Forget()
-#define __Pyx_FastGilFuncInit()
-
-/* MemviewSliceStruct.proto */
-struct __pyx_memoryview_obj;
-typedef struct {
-  struct __pyx_memoryview_obj *memview;
-  char *data;
-  Py_ssize_t shape[8];
-  Py_ssize_t strides[8];
-  Py_ssize_t suboffsets[8];
-} __Pyx_memviewslice;
-#define __Pyx_MemoryView_Len(m)  (m.shape[0])
-
-/* Atomics.proto */
-#include <pythread.h>
-#ifndef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 1
-#endif
-#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS
-#define __pyx_atomic_int_type int
-#if CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\
-                    (__GNUC_MINOR__ > 1 ||\
-                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2))))
-    #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1)
-    #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using GNU atomics"
-    #endif
-#elif CYTHON_ATOMICS && defined(_MSC_VER) && CYTHON_COMPILING_IN_NOGIL
-    #include <intrin.h>
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type long
-    #pragma intrinsic (_InterlockedExchangeAdd)
-    #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1)
-    #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #pragma message ("Using MSVC atomics")
-    #endif
-#else
-    #undef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 0
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Not using atomics"
-    #endif
-#endif
-typedef volatile __pyx_atomic_int_type __pyx_atomic_int;
-#if CYTHON_ATOMICS
-    #define __pyx_add_acquisition_count(memview)\
-             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview))
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview))
-#else
-    #define __pyx_add_acquisition_count(memview)\
-            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-#endif
-
-/* ForceInitThreads.proto */
-#ifndef __PYX_FORCE_INIT_THREADS
-  #define __PYX_FORCE_INIT_THREADS 0
-#endif
-
-/* BufferFormatStructs.proto */
-#define IS_UNSIGNED(type) (((type) -1) > 0)
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
-  const char* name;
-  struct __Pyx_StructField_* fields;
-  size_t size;
-  size_t arraysize[8];
-  int ndim;
-  char typegroup;
-  char is_unsigned;
-  int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
-  size_t offset;
-} __Pyx_StructField;
-typedef struct {
-  __Pyx_StructField* field;
-  size_t parent_offset;
-} __Pyx_BufFmt_StackElem;
-typedef struct {
-  __Pyx_StructField root;
-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
-  size_t new_count, enc_count;
-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":689
- * # in Cython to enable them only on the right systems.
- * 
- * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t
- */
-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":690
- * 
- * ctypedef npy_int8       int8_t
- * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int32      int32_t
- * ctypedef npy_int64      int64_t
- */
-typedef npy_int16 __pyx_t_5numpy_int16_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":691
- * ctypedef npy_int8       int8_t
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int64      int64_t
- * #ctypedef npy_int96      int96_t
- */
-typedef npy_int32 __pyx_t_5numpy_int32_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":692
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t
- * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_int96      int96_t
- * #ctypedef npy_int128     int128_t
- */
-typedef npy_int64 __pyx_t_5numpy_int64_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":696
- * #ctypedef npy_int128     int128_t
- * 
- * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t
- */
-typedef npy_uint8 __pyx_t_5numpy_uint8_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":697
- * 
- * ctypedef npy_uint8      uint8_t
- * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint32     uint32_t
- * ctypedef npy_uint64     uint64_t
- */
-typedef npy_uint16 __pyx_t_5numpy_uint16_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":698
- * ctypedef npy_uint8      uint8_t
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint64     uint64_t
- * #ctypedef npy_uint96     uint96_t
- */
-typedef npy_uint32 __pyx_t_5numpy_uint32_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":699
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t
- * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_uint96     uint96_t
- * #ctypedef npy_uint128    uint128_t
- */
-typedef npy_uint64 __pyx_t_5numpy_uint64_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":703
- * #ctypedef npy_uint128    uint128_t
- * 
- * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_float64    float64_t
- * #ctypedef npy_float80    float80_t
- */
-typedef npy_float32 __pyx_t_5numpy_float32_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":704
- * 
- * ctypedef npy_float32    float32_t
- * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_float80    float80_t
- * #ctypedef npy_float128   float128_t
- */
-typedef npy_float64 __pyx_t_5numpy_float64_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":713
- * # The int types are mapped a bit surprising --
- * # numpy.int corresponds to 'l' and numpy.long to 'q'
- * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longlong   long_t
- * ctypedef npy_longlong   longlong_t
- */
-typedef npy_long __pyx_t_5numpy_int_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":714
- * # numpy.int corresponds to 'l' and numpy.long to 'q'
- * ctypedef npy_long       int_t
- * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longlong   longlong_t
- * 
- */
-typedef npy_longlong __pyx_t_5numpy_long_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":715
- * ctypedef npy_long       int_t
- * ctypedef npy_longlong   long_t
- * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_ulong      uint_t
- */
-typedef npy_longlong __pyx_t_5numpy_longlong_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":717
- * ctypedef npy_longlong   longlong_t
- * 
- * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
- * ctypedef npy_ulonglong  ulong_t
- * ctypedef npy_ulonglong  ulonglong_t
- */
-typedef npy_ulong __pyx_t_5numpy_uint_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":718
- * 
- * ctypedef npy_ulong      uint_t
- * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
- * ctypedef npy_ulonglong  ulonglong_t
- * 
- */
-typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":719
- * ctypedef npy_ulong      uint_t
- * ctypedef npy_ulonglong  ulong_t
- * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_intp       intp_t
- */
-typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":721
- * ctypedef npy_ulonglong  ulonglong_t
- * 
- * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uintp      uintp_t
- * 
- */
-typedef npy_intp __pyx_t_5numpy_intp_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":722
- * 
- * ctypedef npy_intp       intp_t
- * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_double     float_t
- */
-typedef npy_uintp __pyx_t_5numpy_uintp_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":724
- * ctypedef npy_uintp      uintp_t
- * 
- * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
- * ctypedef npy_double     double_t
- * ctypedef npy_longdouble longdouble_t
- */
-typedef npy_double __pyx_t_5numpy_float_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":725
- * 
- * ctypedef npy_double     float_t
- * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longdouble longdouble_t
- * 
- */
-typedef npy_double __pyx_t_5numpy_double_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":726
- * ctypedef npy_double     float_t
- * ctypedef npy_double     double_t
- * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_cfloat      cfloat_t
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-  #ifdef __cplusplus
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-  #else
-    typedef float _Complex __pyx_t_float_complex;
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-#else
-    typedef struct { float real, imag; } __pyx_t_float_complex;
-#endif
-static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
-
-/* Declarations.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    typedef ::std::complex< double > __pyx_t_double_complex;
-  #else
-    typedef double _Complex __pyx_t_double_complex;
-  #endif
-#else
-    typedef struct { double real, imag; } __pyx_t_double_complex;
-#endif
-static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
-
-
-/*--- Type declarations ---*/
-struct __pyx_array_obj;
-struct __pyx_MemviewEnum_obj;
-struct __pyx_memoryview_obj;
-struct __pyx_memoryviewslice_obj;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":728
- * ctypedef npy_longdouble longdouble_t
- * 
- * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
- * ctypedef npy_cdouble     cdouble_t
- * ctypedef npy_clongdouble clongdouble_t
- */
-typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":729
- * 
- * ctypedef npy_cfloat      cfloat_t
- * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
- * ctypedef npy_clongdouble clongdouble_t
- * 
- */
-typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":730
- * ctypedef npy_cfloat      cfloat_t
- * ctypedef npy_cdouble     cdouble_t
- * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_cdouble     complex_t
- */
-typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
-
-/* "../../../../tmp/pip-build-env-uwbxg1aj/overlay/lib/python3.10/site-packages/numpy/__init__.pxd":732
- * ctypedef npy_clongdouble clongdouble_t
- * 
- * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
- * 
- * cdef inline object PyArray_MultiIterNew1(a):
- */
-typedef npy_cdouble __pyx_t_5numpy_complex_t;
-struct __pyx_opt_args_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_c;
-
-/* "diff_ttsg/utils/monotonic_align/core.pyx":42
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- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
- *   cdef int b = values.shape[0]
- * 
- */
-struct __pyx_opt_args_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_c {
-  int __pyx_n;
-  float max_neg_val;
-};
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-/* "View.MemoryView":106
- * 
- * @cname("__pyx_array")
- * cdef class array:             # <<<<<<<<<<<<<<
- * 
- *     cdef:
- */
-struct __pyx_array_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_array *__pyx_vtab;
-  char *data;
-  Py_ssize_t len;
-  char *format;
-  int ndim;
-  Py_ssize_t *_shape;
-  Py_ssize_t *_strides;
-  Py_ssize_t itemsize;
-  PyObject *mode;
-  PyObject *_format;
-  void (*callback_free_data)(void *);
-  int free_data;
-  int dtype_is_object;
-};
-
-
-/* "View.MemoryView":280
- * 
- * @cname('__pyx_MemviewEnum')
- * cdef class Enum(object):             # <<<<<<<<<<<<<<
- *     cdef object name
- *     def __init__(self, name):
- */
-struct __pyx_MemviewEnum_obj {
-  PyObject_HEAD
-  PyObject *name;
-};
-
-
-/* "View.MemoryView":331
- * 
- * @cname('__pyx_memoryview')
- * cdef class memoryview(object):             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-struct __pyx_memoryview_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
-  PyObject *obj;
-  PyObject *_size;
-  PyObject *_array_interface;
-  PyThread_type_lock lock;
-  __pyx_atomic_int acquisition_count[2];
-  __pyx_atomic_int *acquisition_count_aligned_p;
-  Py_buffer view;
-  int flags;
-  int dtype_is_object;
-  __Pyx_TypeInfo *typeinfo;
-};
-
-
-/* "View.MemoryView":967
- * 
- * @cname('__pyx_memoryviewslice')
- * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
- *     "Internal class for passing memoryview slices to Python"
- * 
- */
-struct __pyx_memoryviewslice_obj {
-  struct __pyx_memoryview_obj __pyx_base;
-  __Pyx_memviewslice from_slice;
-  PyObject *from_object;
-  PyObject *(*to_object_func)(char *);
-  int (*to_dtype_func)(char *, PyObject *);
-};
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-
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-/* "View.MemoryView":106
- * 
- * @cname("__pyx_array")
- * cdef class array:             # <<<<<<<<<<<<<<
- * 
- *     cdef:
- */
-
-struct __pyx_vtabstruct_array {
-  PyObject *(*get_memview)(struct __pyx_array_obj *);
-};
-static struct __pyx_vtabstruct_array *__pyx_vtabptr_array;
-
-
-/* "View.MemoryView":331
- * 
- * @cname('__pyx_memoryview')
- * cdef class memoryview(object):             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-
-struct __pyx_vtabstruct_memoryview {
-  char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);
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-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
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-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
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-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
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-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
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-static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
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-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
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-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
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-/* PyIntBinop.proto */
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-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
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-/* ListExtend.proto */
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
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-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
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-/* ListAppend.proto */
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-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
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-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
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-/* DivInt[long].proto */
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-/* PySequenceContains.proto */
-static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
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-/* ImportFrom.proto */
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-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
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-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
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-/* PyObject_GenericGetAttr.proto */
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-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
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-#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
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-/* PyObjectGetAttrStrNoError.proto */
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-/* SetupReduce.proto */
-static int __Pyx_setup_reduce(PyObject* type_obj);
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-/* TypeImport.proto */
-#ifndef __PYX_HAVE_RT_ImportType_proto_0_29_35
-#define __PYX_HAVE_RT_ImportType_proto_0_29_35
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-#define __PYX_GET_STRUCT_ALIGNMENT_0_29_35(s) alignof(s)
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-enum __Pyx_ImportType_CheckSize_0_29_35 {
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-#endif
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-/* CLineInTraceback.proto */
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-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
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-/* CodeObjectCache.proto */
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-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
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-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
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-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
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-/* BufferStructDeclare.proto */
-typedef struct {
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-} __Pyx_Buf_DimInfo;
-typedef struct {
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-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
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-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
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-/* MemviewSliceIsContig.proto */
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
-
-/* OverlappingSlices.proto */
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                                __Pyx_memviewslice *slice2,
-                                int ndim, size_t itemsize);
-
-/* Capsule.proto */
-static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);
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-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
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-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type);
-
-/* TypeInfoCompare.proto */
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-/* MemviewSliceValidateAndInit.proto */
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag);
-
-/* GCCDiagnostics.proto */
-#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
-
-/* RealImag.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #define __Pyx_CREAL(z) ((z).real())
-    #define __Pyx_CIMAG(z) ((z).imag())
-  #else
-    #define __Pyx_CREAL(z) (__real__(z))
-    #define __Pyx_CIMAG(z) (__imag__(z))
-  #endif
-#else
-    #define __Pyx_CREAL(z) ((z).real)
-    #define __Pyx_CIMAG(z) ((z).imag)
-#endif
-#if defined(__cplusplus) && CYTHON_CCOMPLEX\
-        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
-    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
-    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
-#else
-    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
-    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq_float(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_float(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_float(a, b) ((a)-(b))
-    #define __Pyx_c_prod_float(a, b) ((a)*(b))
-    #define __Pyx_c_quot_float(a, b) ((a)/(b))
-    #define __Pyx_c_neg_float(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)
-    #define __Pyx_c_conj_float(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_float(z)     (::std::abs(z))
-        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_float(z) ((z)==0)
-    #define __Pyx_c_conj_float(z)    (conjf(z))
-    #if 1
-        #define __Pyx_c_abs_float(z)     (cabsf(z))
-        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    #endif
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_double(a, b) ((a)-(b))
-    #define __Pyx_c_prod_double(a, b) ((a)*(b))
-    #define __Pyx_c_quot_double(a, b) ((a)/(b))
-    #define __Pyx_c_neg_double(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
-    #define __Pyx_c_conj_double(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (::std::abs(z))
-        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_double(z) ((z)==0)
-    #define __Pyx_c_conj_double(z)    (conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (cabs(z))
-        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    #endif
-#endif
-
-/* MemviewSliceCopyTemplate.proto */
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-
-/* Module declarations from 'cython.view' */
-
-/* Module declarations from 'cython' */
-
-/* Module declarations from 'cpython.buffer' */
-
-/* Module declarations from 'libc.string' */
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-/* Module declarations from 'libc.stdio' */
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-/* Module declarations from '__builtin__' */
-
-/* Module declarations from 'cpython.type' */
-static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
-
-/* Module declarations from 'cpython' */
-
-/* Module declarations from 'cpython.object' */
-
-/* Module declarations from 'cpython.ref' */
-
-/* Module declarations from 'cpython.mem' */
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-/* Module declarations from 'numpy' */
-
-/* Module declarations from 'numpy' */
-static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
-static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
-static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
-static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
-static PyTypeObject *__pyx_ptype_5numpy_generic = 0;
-static PyTypeObject *__pyx_ptype_5numpy_number = 0;
-static PyTypeObject *__pyx_ptype_5numpy_integer = 0;
-static PyTypeObject *__pyx_ptype_5numpy_signedinteger = 0;
-static PyTypeObject *__pyx_ptype_5numpy_unsignedinteger = 0;
-static PyTypeObject *__pyx_ptype_5numpy_inexact = 0;
-static PyTypeObject *__pyx_ptype_5numpy_floating = 0;
-static PyTypeObject *__pyx_ptype_5numpy_complexfloating = 0;
-static PyTypeObject *__pyx_ptype_5numpy_flexible = 0;
-static PyTypeObject *__pyx_ptype_5numpy_character = 0;
-static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
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-/* Module declarations from 'diff_ttsg.utils.monotonic_align.core' */
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-static PyTypeObject *__pyx_MemviewEnum_type = 0;
-static PyTypeObject *__pyx_memoryview_type = 0;
-static PyTypeObject *__pyx_memoryviewslice_type = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static int __pyx_memoryview_thread_locks_used;
-static PyThread_type_lock __pyx_memoryview_thread_locks[8];
-static void __pyx_f_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, float); /*proto*/
-static void __pyx_f_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_c *__pyx_optional_args); /*proto*/
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static void *__pyx_align_pointer(void *, size_t); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
-static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 };
-static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };
-#define __Pyx_MODULE_NAME "diff_ttsg.utils.monotonic_align.core"
-extern int __pyx_module_is_main_diff_ttsg__utils__monotonic_align__core;
-int __pyx_module_is_main_diff_ttsg__utils__monotonic_align__core = 0;
-
-/* Implementation of 'diff_ttsg.utils.monotonic_align.core' */
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_ImportError;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-static const char __pyx_k_O[] = "O";
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k_id[] = "id";
-static const char __pyx_k_np[] = "np";
-static const char __pyx_k_new[] = "__new__";
-static const char __pyx_k_obj[] = "obj";
-static const char __pyx_k_base[] = "base";
-static const char __pyx_k_dict[] = "__dict__";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_mode[] = "mode";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ndim[] = "ndim";
-static const char __pyx_k_pack[] = "pack";
-static const char __pyx_k_size[] = "size";
-static const char __pyx_k_step[] = "step";
-static const char __pyx_k_stop[] = "stop";
-static const char __pyx_k_t_xs[] = "t_xs";
-static const char __pyx_k_t_ys[] = "t_ys";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_ASCII[] = "ASCII";
-static const char __pyx_k_class[] = "__class__";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_flags[] = "flags";
-static const char __pyx_k_numpy[] = "numpy";
-static const char __pyx_k_paths[] = "paths";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_pickle[] = "pickle";
-static const char __pyx_k_reduce[] = "__reduce__";
-static const char __pyx_k_struct[] = "struct";
-static const char __pyx_k_unpack[] = "unpack";
-static const char __pyx_k_update[] = "update";
-static const char __pyx_k_values[] = "values";
-static const char __pyx_k_fortran[] = "fortran";
-static const char __pyx_k_memview[] = "memview";
-static const char __pyx_k_Ellipsis[] = "Ellipsis";
-static const char __pyx_k_getstate[] = "__getstate__";
-static const char __pyx_k_itemsize[] = "itemsize";
-static const char __pyx_k_pyx_type[] = "__pyx_type";
-static const char __pyx_k_setstate[] = "__setstate__";
-static const char __pyx_k_TypeError[] = "TypeError";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_pyx_state[] = "__pyx_state";
-static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
-static const char __pyx_k_IndexError[] = "IndexError";
-static const char __pyx_k_ValueError[] = "ValueError";
-static const char __pyx_k_pyx_result[] = "__pyx_result";
-static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static const char __pyx_k_ImportError[] = "ImportError";
-static const char __pyx_k_MemoryError[] = "MemoryError";
-static const char __pyx_k_PickleError[] = "PickleError";
-static const char __pyx_k_max_neg_val[] = "max_neg_val";
-static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
-static const char __pyx_k_stringsource[] = "stringsource";
-static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
-static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
-static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
-static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
-static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
-static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
-static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
-static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
-static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
-static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
-static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
-static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
-static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
-static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
-static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import";
-static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
-static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
-static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name))";
-static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
-static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
-static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
-static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
-static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
-static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-static PyObject *__pyx_n_s_ASCII;
-static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
-static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
-static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;
-static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;
-static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
-static PyObject *__pyx_n_s_Ellipsis;
-static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
-static PyObject *__pyx_n_s_ImportError;
-static PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0;
-static PyObject *__pyx_n_s_IndexError;
-static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
-static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
-static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
-static PyObject *__pyx_n_s_MemoryError;
-static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
-static PyObject *__pyx_kp_s_MemoryView_of_r_object;
-static PyObject *__pyx_n_b_O;
-static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
-static PyObject *__pyx_n_s_PickleError;
-static PyObject *__pyx_n_s_TypeError;
-static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
-static PyObject *__pyx_n_s_ValueError;
-static PyObject *__pyx_n_s_View_MemoryView;
-static PyObject *__pyx_n_s_allocate_buffer;
-static PyObject *__pyx_n_s_base;
-static PyObject *__pyx_n_s_c;
-static PyObject *__pyx_n_u_c;
-static PyObject *__pyx_n_s_class;
-static PyObject *__pyx_n_s_cline_in_traceback;
-static PyObject *__pyx_kp_s_contiguous_and_direct;
-static PyObject *__pyx_kp_s_contiguous_and_indirect;
-static PyObject *__pyx_n_s_dict;
-static PyObject *__pyx_n_s_dtype_is_object;
-static PyObject *__pyx_n_s_encode;
-static PyObject *__pyx_n_s_enumerate;
-static PyObject *__pyx_n_s_error;
-static PyObject *__pyx_n_s_flags;
-static PyObject *__pyx_n_s_format;
-static PyObject *__pyx_n_s_fortran;
-static PyObject *__pyx_n_u_fortran;
-static PyObject *__pyx_n_s_getstate;
-static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
-static PyObject *__pyx_n_s_id;
-static PyObject *__pyx_n_s_import;
-static PyObject *__pyx_n_s_itemsize;
-static PyObject *__pyx_kp_s_itemsize_0_for_cython_array;
-static PyObject *__pyx_n_s_main;
-static PyObject *__pyx_n_s_max_neg_val;
-static PyObject *__pyx_n_s_memview;
-static PyObject *__pyx_n_s_mode;
-static PyObject *__pyx_n_s_name;
-static PyObject *__pyx_n_s_name_2;
-static PyObject *__pyx_n_s_ndim;
-static PyObject *__pyx_n_s_new;
-static PyObject *__pyx_kp_s_no_default___reduce___due_to_non;
-static PyObject *__pyx_n_s_np;
-static PyObject *__pyx_n_s_numpy;
-static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to;
-static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor;
-static PyObject *__pyx_n_s_obj;
-static PyObject *__pyx_n_s_pack;
-static PyObject *__pyx_n_s_paths;
-static PyObject *__pyx_n_s_pickle;
-static PyObject *__pyx_n_s_pyx_PickleError;
-static PyObject *__pyx_n_s_pyx_checksum;
-static PyObject *__pyx_n_s_pyx_getbuffer;
-static PyObject *__pyx_n_s_pyx_result;
-static PyObject *__pyx_n_s_pyx_state;
-static PyObject *__pyx_n_s_pyx_type;
-static PyObject *__pyx_n_s_pyx_unpickle_Enum;
-static PyObject *__pyx_n_s_pyx_vtable;
-static PyObject *__pyx_n_s_range;
-static PyObject *__pyx_n_s_reduce;
-static PyObject *__pyx_n_s_reduce_cython;
-static PyObject *__pyx_n_s_reduce_ex;
-static PyObject *__pyx_n_s_setstate;
-static PyObject *__pyx_n_s_setstate_cython;
-static PyObject *__pyx_n_s_shape;
-static PyObject *__pyx_n_s_size;
-static PyObject *__pyx_n_s_start;
-static PyObject *__pyx_n_s_step;
-static PyObject *__pyx_n_s_stop;
-static PyObject *__pyx_kp_s_strided_and_direct;
-static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
-static PyObject *__pyx_kp_s_strided_and_indirect;
-static PyObject *__pyx_kp_s_stringsource;
-static PyObject *__pyx_n_s_struct;
-static PyObject *__pyx_n_s_t_xs;
-static PyObject *__pyx_n_s_t_ys;
-static PyObject *__pyx_n_s_test;
-static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
-static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
-static PyObject *__pyx_n_s_unpack;
-static PyObject *__pyx_n_s_update;
-static PyObject *__pyx_n_s_values;
-static PyObject *__pyx_pf_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_xs, __Pyx_memviewslice __pyx_v_t_ys, float __pyx_v_max_neg_val); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
-static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_int_0;
-static PyObject *__pyx_int_1;
-static PyObject *__pyx_int_112105877;
-static PyObject *__pyx_int_136983863;
-static PyObject *__pyx_int_184977713;
-static PyObject *__pyx_int_neg_1;
-static float __pyx_k_;
-static PyObject *__pyx_tuple__2;
-static PyObject *__pyx_tuple__3;
-static PyObject *__pyx_tuple__4;
-static PyObject *__pyx_tuple__5;
-static PyObject *__pyx_tuple__6;
-static PyObject *__pyx_tuple__7;
-static PyObject *__pyx_tuple__8;
-static PyObject *__pyx_tuple__9;
-static PyObject *__pyx_slice__18;
-static PyObject *__pyx_tuple__10;
-static PyObject *__pyx_tuple__11;
-static PyObject *__pyx_tuple__12;
-static PyObject *__pyx_tuple__13;
-static PyObject *__pyx_tuple__14;
-static PyObject *__pyx_tuple__15;
-static PyObject *__pyx_tuple__16;
-static PyObject *__pyx_tuple__17;
-static PyObject *__pyx_tuple__19;
-static PyObject *__pyx_tuple__20;
-static PyObject *__pyx_tuple__21;
-static PyObject *__pyx_tuple__22;
-static PyObject *__pyx_tuple__23;
-static PyObject *__pyx_tuple__24;
-static PyObject *__pyx_tuple__25;
-static PyObject *__pyx_tuple__26;
-static PyObject *__pyx_tuple__27;
-static PyObject *__pyx_tuple__28;
-static PyObject *__pyx_codeobj__29;
-/* Late includes */
-
-/* "diff_ttsg/utils/monotonic_align/core.pyx":11
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil:             # <<<<<<<<<<<<<<
- *   cdef int x
- *   cdef int y
- */
-
-static void __pyx_f_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice __pyx_v_path, __Pyx_memviewslice __pyx_v_value, int __pyx_v_t_x, int __pyx_v_t_y, float __pyx_v_max_neg_val) {
-  int __pyx_v_x;
-  int __pyx_v_y;
-  float __pyx_v_v_prev;
-  float __pyx_v_v_cur;
-  int __pyx_v_index;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  long __pyx_t_4;
-  int __pyx_t_5;
-  long __pyx_t_6;
-  long __pyx_t_7;
-  int __pyx_t_8;
-  Py_ssize_t __pyx_t_9;
-  Py_ssize_t __pyx_t_10;
-  float __pyx_t_11;
-  float __pyx_t_12;
-  float __pyx_t_13;
-  Py_ssize_t __pyx_t_14;
-  Py_ssize_t __pyx_t_15;
-  int __pyx_t_16;
-
-  /* "diff_ttsg/utils/monotonic_align/core.pyx":17
- *   cdef float v_cur
- *   cdef float tmp
- *   cdef int index = t_x - 1             # <<<<<<<<<<<<<<
- * 
- *   for y in range(t_y):
- */
-  __pyx_v_index = (__pyx_v_t_x - 1);
-
-  /* "diff_ttsg/utils/monotonic_align/core.pyx":19
- *   cdef int index = t_x - 1
- * 
- *   for y in range(t_y):             # <<<<<<<<<<<<<<
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:
- */
-  __pyx_t_1 = __pyx_v_t_y;
-  __pyx_t_2 = __pyx_t_1;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_y = __pyx_t_3;
-
-    /* "diff_ttsg/utils/monotonic_align/core.pyx":20
- * 
- *   for y in range(t_y):
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):             # <<<<<<<<<<<<<<
- *       if x == y:
- *         v_cur = max_neg_val
- */
-    __pyx_t_4 = (__pyx_v_y + 1);
-    __pyx_t_5 = __pyx_v_t_x;
-    if (((__pyx_t_4 < __pyx_t_5) != 0)) {
-      __pyx_t_6 = __pyx_t_4;
-    } else {
-      __pyx_t_6 = __pyx_t_5;
-    }
-    __pyx_t_4 = __pyx_t_6;
-    __pyx_t_5 = ((__pyx_v_t_x + __pyx_v_y) - __pyx_v_t_y);
-    __pyx_t_6 = 0;
-    if (((__pyx_t_5 > __pyx_t_6) != 0)) {
-      __pyx_t_7 = __pyx_t_5;
-    } else {
-      __pyx_t_7 = __pyx_t_6;
-    }
-    __pyx_t_6 = __pyx_t_4;
-    for (__pyx_t_5 = __pyx_t_7; __pyx_t_5 < __pyx_t_6; __pyx_t_5+=1) {
-      __pyx_v_x = __pyx_t_5;
-
-      /* "diff_ttsg/utils/monotonic_align/core.pyx":21
- *   for y in range(t_y):
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:             # <<<<<<<<<<<<<<
- *         v_cur = max_neg_val
- *       else:
- */
-      __pyx_t_8 = ((__pyx_v_x == __pyx_v_y) != 0);
-      if (__pyx_t_8) {
-
-        /* "diff_ttsg/utils/monotonic_align/core.pyx":22
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:
- *         v_cur = max_neg_val             # <<<<<<<<<<<<<<
- *       else:
- *         v_cur = value[x, y-1]
- */
-        __pyx_v_v_cur = __pyx_v_max_neg_val;
-
-        /* "diff_ttsg/utils/monotonic_align/core.pyx":21
- *   for y in range(t_y):
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:             # <<<<<<<<<<<<<<
- *         v_cur = max_neg_val
- *       else:
- */
-        goto __pyx_L7;
-      }
-
-      /* "diff_ttsg/utils/monotonic_align/core.pyx":24
- *         v_cur = max_neg_val
- *       else:
- *         v_cur = value[x, y-1]             # <<<<<<<<<<<<<<
- *       if x == 0:
- *         if y == 0:
- */
-      /*else*/ {
-        __pyx_t_9 = __pyx_v_x;
-        __pyx_t_10 = (__pyx_v_y - 1);
-        __pyx_v_v_cur = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) )));
-      }
-      __pyx_L7:;
-
-      /* "diff_ttsg/utils/monotonic_align/core.pyx":25
- *       else:
- *         v_cur = value[x, y-1]
- *       if x == 0:             # <<<<<<<<<<<<<<
- *         if y == 0:
- *           v_prev = 0.
- */
-      __pyx_t_8 = ((__pyx_v_x == 0) != 0);
-      if (__pyx_t_8) {
-
-        /* "diff_ttsg/utils/monotonic_align/core.pyx":26
- *         v_cur = value[x, y-1]
- *       if x == 0:
- *         if y == 0:             # <<<<<<<<<<<<<<
- *           v_prev = 0.
- *         else:
- */
-        __pyx_t_8 = ((__pyx_v_y == 0) != 0);
-        if (__pyx_t_8) {
-
-          /* "diff_ttsg/utils/monotonic_align/core.pyx":27
- *       if x == 0:
- *         if y == 0:
- *           v_prev = 0.             # <<<<<<<<<<<<<<
- *         else:
- *           v_prev = max_neg_val
- */
-          __pyx_v_v_prev = 0.;
-
-          /* "diff_ttsg/utils/monotonic_align/core.pyx":26
- *         v_cur = value[x, y-1]
- *       if x == 0:
- *         if y == 0:             # <<<<<<<<<<<<<<
- *           v_prev = 0.
- *         else:
- */
-          goto __pyx_L9;
-        }
-
-        /* "diff_ttsg/utils/monotonic_align/core.pyx":29
- *           v_prev = 0.
- *         else:
- *           v_prev = max_neg_val             # <<<<<<<<<<<<<<
- *       else:
- *         v_prev = value[x-1, y-1]
- */
-        /*else*/ {
-          __pyx_v_v_prev = __pyx_v_max_neg_val;
-        }
-        __pyx_L9:;
-
-        /* "diff_ttsg/utils/monotonic_align/core.pyx":25
- *       else:
- *         v_cur = value[x, y-1]
- *       if x == 0:             # <<<<<<<<<<<<<<
- *         if y == 0:
- *           v_prev = 0.
- */
-        goto __pyx_L8;
-      }
-
-      /* "diff_ttsg/utils/monotonic_align/core.pyx":31
- *           v_prev = max_neg_val
- *       else:
- *         v_prev = value[x-1, y-1]             # <<<<<<<<<<<<<<
- *       value[x, y] = max(v_cur, v_prev) + value[x, y]
- * 
- */
-      /*else*/ {
-        __pyx_t_10 = (__pyx_v_x - 1);
-        __pyx_t_9 = (__pyx_v_y - 1);
-        __pyx_v_v_prev = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_10 * __pyx_v_value.strides[0]) )) + __pyx_t_9)) )));
-      }
-      __pyx_L8:;
-
-      /* "diff_ttsg/utils/monotonic_align/core.pyx":32
- *       else:
- *         v_prev = value[x-1, y-1]
- *       value[x, y] = max(v_cur, v_prev) + value[x, y]             # <<<<<<<<<<<<<<
- * 
- *   for y in range(t_y - 1, -1, -1):
- */
-      __pyx_t_11 = __pyx_v_v_prev;
-      __pyx_t_12 = __pyx_v_v_cur;
-      if (((__pyx_t_11 > __pyx_t_12) != 0)) {
-        __pyx_t_13 = __pyx_t_11;
-      } else {
-        __pyx_t_13 = __pyx_t_12;
-      }
-      __pyx_t_9 = __pyx_v_x;
-      __pyx_t_10 = __pyx_v_y;
-      __pyx_t_14 = __pyx_v_x;
-      __pyx_t_15 = __pyx_v_y;
-      *((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_14 * __pyx_v_value.strides[0]) )) + __pyx_t_15)) )) = (__pyx_t_13 + (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) ))));
-    }
-  }
-
-  /* "diff_ttsg/utils/monotonic_align/core.pyx":34
- *       value[x, y] = max(v_cur, v_prev) + value[x, y]
- * 
- *   for y in range(t_y - 1, -1, -1):             # <<<<<<<<<<<<<<
- *     path[index, y] = 1
- *     if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]):
- */
-  for (__pyx_t_1 = (__pyx_v_t_y - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_y = __pyx_t_1;
-
-    /* "diff_ttsg/utils/monotonic_align/core.pyx":35
- * 
- *   for y in range(t_y - 1, -1, -1):
- *     path[index, y] = 1             # <<<<<<<<<<<<<<
- *     if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]):
- *       index = index - 1
- */
-    __pyx_t_10 = __pyx_v_index;
-    __pyx_t_9 = __pyx_v_y;
-    *((int *) ( /* dim=1 */ ((char *) (((int *) ( /* dim=0 */ (__pyx_v_path.data + __pyx_t_10 * __pyx_v_path.strides[0]) )) + __pyx_t_9)) )) = 1;
-
-    /* "diff_ttsg/utils/monotonic_align/core.pyx":36
- *   for y in range(t_y - 1, -1, -1):
- *     path[index, y] = 1
- *     if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]):             # <<<<<<<<<<<<<<
- *       index = index - 1
- * 
- */
-    __pyx_t_16 = ((__pyx_v_index != 0) != 0);
-    if (__pyx_t_16) {
-    } else {
-      __pyx_t_8 = __pyx_t_16;
-      goto __pyx_L13_bool_binop_done;
-    }
-    __pyx_t_16 = ((__pyx_v_index == __pyx_v_y) != 0);
-    if (!__pyx_t_16) {
-    } else {
-      __pyx_t_8 = __pyx_t_16;
-      goto __pyx_L13_bool_binop_done;
-    }
-    __pyx_t_9 = __pyx_v_index;
-    __pyx_t_10 = (__pyx_v_y - 1);
-    __pyx_t_15 = (__pyx_v_index - 1);
-    __pyx_t_14 = (__pyx_v_y - 1);
-    __pyx_t_16 = (((*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) ))) < (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_15 * __pyx_v_value.strides[0]) )) + __pyx_t_14)) )))) != 0);
-    __pyx_t_8 = __pyx_t_16;
-    __pyx_L13_bool_binop_done:;
-    if (__pyx_t_8) {
-
-      /* "diff_ttsg/utils/monotonic_align/core.pyx":37
- *     path[index, y] = 1
- *     if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]):
- *       index = index - 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_index = (__pyx_v_index - 1);
-
-      /* "diff_ttsg/utils/monotonic_align/core.pyx":36
- *   for y in range(t_y - 1, -1, -1):
- *     path[index, y] = 1
- *     if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]):             # <<<<<<<<<<<<<<
- *       index = index - 1
- * 
- */
-    }
-  }
-
-  /* "diff_ttsg/utils/monotonic_align/core.pyx":11
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil:             # <<<<<<<<<<<<<<
- *   cdef int x
- *   cdef int y
- */
-
-  /* function exit code */
-}
-
-/* "diff_ttsg/utils/monotonic_align/core.pyx":42
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
- *   cdef int b = values.shape[0]
- * 
- */
-
-static PyObject *__pyx_pw_9diff_ttsg_5utils_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static void __pyx_f_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_xs, __Pyx_memviewslice __pyx_v_t_ys, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_9diff_ttsg_5utils_15monotonic_align_4core_maximum_path_c *__pyx_optional_args) {
-  float __pyx_v_max_neg_val = __pyx_k_;
-  CYTHON_UNUSED int __pyx_v_b;
-  int __pyx_v_i;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  __Pyx_memviewslice __pyx_t_4 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  Py_ssize_t __pyx_t_6;
-  Py_ssize_t __pyx_t_7;
-  if (__pyx_optional_args) {
-    if (__pyx_optional_args->__pyx_n > 0) {
-      __pyx_v_max_neg_val = __pyx_optional_args->max_neg_val;
-    }
-  }
-
-  /* "diff_ttsg/utils/monotonic_align/core.pyx":43
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil:
- *   cdef int b = values.shape[0]             # <<<<<<<<<<<<<<
- * 
- *   cdef int i
- */
-  __pyx_v_b = (__pyx_v_values.shape[0]);
-
-  /* "diff_ttsg/utils/monotonic_align/core.pyx":46
- * 
- *   cdef int i
- *   for i in prange(b, nogil=True):             # <<<<<<<<<<<<<<
- *     maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val)
- */
-  {
-      #ifdef WITH_THREAD
-      PyThreadState *_save;
-      Py_UNBLOCK_THREADS
-      __Pyx_FastGIL_Remember();
-      #endif
-      /*try:*/ {
-        __pyx_t_1 = __pyx_v_b;
-        if ((1 == 0)) abort();
-        {
-            #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
-                #undef likely
-                #undef unlikely
-                #define likely(x)   (x)
-                #define unlikely(x) (x)
-            #endif
-            __pyx_t_3 = (__pyx_t_1 - 0 + 1 - 1/abs(1)) / 1;
-            if (__pyx_t_3 > 0)
-            {
-                #ifdef _OPENMP
-                #pragma omp parallel private(__pyx_t_6, __pyx_t_7) firstprivate(__pyx_t_4, __pyx_t_5)
-                #endif /* _OPENMP */
-                {
-                    #ifdef _OPENMP
-                    #pragma omp for firstprivate(__pyx_v_i) lastprivate(__pyx_v_i)
-                    #endif /* _OPENMP */
-                    for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_3; __pyx_t_2++){
-                        {
-                            __pyx_v_i = (int)(0 + 1 * __pyx_t_2);
-
-                            /* "diff_ttsg/utils/monotonic_align/core.pyx":47
- *   cdef int i
- *   for i in prange(b, nogil=True):
- *     maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val)             # <<<<<<<<<<<<<<
- */
-                            __pyx_t_4.data = __pyx_v_paths.data;
-                            __pyx_t_4.memview = __pyx_v_paths.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_4, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_paths.strides[0];
-        __pyx_t_4.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_4.shape[0] = __pyx_v_paths.shape[1];
-__pyx_t_4.strides[0] = __pyx_v_paths.strides[1];
-    __pyx_t_4.suboffsets[0] = -1;
-
-__pyx_t_4.shape[1] = __pyx_v_paths.shape[2];
-__pyx_t_4.strides[1] = __pyx_v_paths.strides[2];
-    __pyx_t_4.suboffsets[1] = -1;
-
-__pyx_t_5.data = __pyx_v_values.data;
-                            __pyx_t_5.memview = __pyx_v_values.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_5, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_values.strides[0];
-        __pyx_t_5.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_5.shape[0] = __pyx_v_values.shape[1];
-__pyx_t_5.strides[0] = __pyx_v_values.strides[1];
-    __pyx_t_5.suboffsets[0] = -1;
-
-__pyx_t_5.shape[1] = __pyx_v_values.shape[2];
-__pyx_t_5.strides[1] = __pyx_v_values.strides[2];
-    __pyx_t_5.suboffsets[1] = -1;
-
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-    __pyx_t_1 = (0 <= __pyx_v_start);
-    if (__pyx_t_1) {
-      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
-    }
-    __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":834
- *             start += shape
- *         if not 0 <= start < shape:
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
- *     else:
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 834, __pyx_L1_error)
-
-      /* "View.MemoryView":833
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    }
-
-    /* "View.MemoryView":829
- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":837
- *     else:
- * 
- *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<
- * 
- *         if have_step and step == 0:
- */
-  /*else*/ {
-    __pyx_t_1 = ((__pyx_v_have_step != 0) != 0);
-    if (__pyx_t_1) {
-    } else {
-      __pyx_t_2 = __pyx_t_1;
-      goto __pyx_L6_bool_binop_done;
-    }
-    __pyx_t_1 = ((__pyx_v_step < 0) != 0);
-    __pyx_t_2 = __pyx_t_1;
-    __pyx_L6_bool_binop_done:;
-    __pyx_v_negative_step = __pyx_t_2;
-
-    /* "View.MemoryView":839
- *         negative_step = have_step != 0 and step < 0
- * 
- *         if have_step and step == 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
- * 
- */
-    __pyx_t_1 = (__pyx_v_have_step != 0);
-    if (__pyx_t_1) {
-    } else {
-      __pyx_t_2 = __pyx_t_1;
-      goto __pyx_L9_bool_binop_done;
-    }
-    __pyx_t_1 = ((__pyx_v_step == 0) != 0);
-    __pyx_t_2 = __pyx_t_1;
-    __pyx_L9_bool_binop_done:;
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":840
- * 
- *         if have_step and step == 0:
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 840, __pyx_L1_error)
-
-      /* "View.MemoryView":839
- *         negative_step = have_step != 0 and step < 0
- * 
- *         if have_step and step == 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
- * 
- */
-    }
-
-    /* "View.MemoryView":843
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-    __pyx_t_2 = (__pyx_v_have_start != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":844
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-      __pyx_t_2 = ((__pyx_v_start < 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":845
- *         if have_start:
- *             if start < 0:
- *                 start += shape             # <<<<<<<<<<<<<<
- *                 if start < 0:
- *                     start = 0
- */
-        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-        /* "View.MemoryView":846
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        __pyx_t_2 = ((__pyx_v_start < 0) != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":847
- *                 start += shape
- *                 if start < 0:
- *                     start = 0             # <<<<<<<<<<<<<<
- *             elif start >= shape:
- *                 if negative_step:
- */
-          __pyx_v_start = 0;
-
-          /* "View.MemoryView":846
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        }
-
-        /* "View.MemoryView":844
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-        goto __pyx_L12;
-      }
-
-      /* "View.MemoryView":848
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":849
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-        __pyx_t_2 = (__pyx_v_negative_step != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":850
- *             elif start >= shape:
- *                 if negative_step:
- *                     start = shape - 1             # <<<<<<<<<<<<<<
- *                 else:
- *                     start = shape
- */
-          __pyx_v_start = (__pyx_v_shape - 1);
-
-          /* "View.MemoryView":849
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-          goto __pyx_L14;
-        }
-
-        /* "View.MemoryView":852
- *                     start = shape - 1
- *                 else:
- *                     start = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        /*else*/ {
-          __pyx_v_start = __pyx_v_shape;
-        }
-        __pyx_L14:;
-
-        /* "View.MemoryView":848
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      }
-      __pyx_L12:;
-
-      /* "View.MemoryView":843
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-      goto __pyx_L11;
-    }
-
-    /* "View.MemoryView":854
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-    /*else*/ {
-      __pyx_t_2 = (__pyx_v_negative_step != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":855
- *         else:
- *             if negative_step:
- *                 start = shape - 1             # <<<<<<<<<<<<<<
- *             else:
- *                 start = 0
- */
-        __pyx_v_start = (__pyx_v_shape - 1);
-
-        /* "View.MemoryView":854
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-        goto __pyx_L15;
-      }
-
-      /* "View.MemoryView":857
- *                 start = shape - 1
- *             else:
- *                 start = 0             # <<<<<<<<<<<<<<
- * 
- *         if have_stop:
- */
-      /*else*/ {
-        __pyx_v_start = 0;
-      }
-      __pyx_L15:;
-    }
-    __pyx_L11:;
-
-    /* "View.MemoryView":859
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-    __pyx_t_2 = (__pyx_v_have_stop != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":860
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-      __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":861
- *         if have_stop:
- *             if stop < 0:
- *                 stop += shape             # <<<<<<<<<<<<<<
- *                 if stop < 0:
- *                     stop = 0
- */
-        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
-
-        /* "View.MemoryView":862
- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":863
- *                 stop += shape
- *                 if stop < 0:
- *                     stop = 0             # <<<<<<<<<<<<<<
- *             elif stop > shape:
- *                 stop = shape
- */
-          __pyx_v_stop = 0;
-
-          /* "View.MemoryView":862
- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        }
-
-        /* "View.MemoryView":860
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-        goto __pyx_L17;
-      }
-
-      /* "View.MemoryView":864
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":865
- *                     stop = 0
- *             elif stop > shape:
- *                 stop = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        __pyx_v_stop = __pyx_v_shape;
-
-        /* "View.MemoryView":864
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      }
-      __pyx_L17:;
-
-      /* "View.MemoryView":859
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-      goto __pyx_L16;
-    }
-
-    /* "View.MemoryView":867
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-    /*else*/ {
-      __pyx_t_2 = (__pyx_v_negative_step != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":868
- *         else:
- *             if negative_step:
- *                 stop = -1             # <<<<<<<<<<<<<<
- *             else:
- *                 stop = shape
- */
-        __pyx_v_stop = -1L;
-
-        /* "View.MemoryView":867
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-        goto __pyx_L19;
-      }
-
-      /* "View.MemoryView":870
- *                 stop = -1
- *             else:
- *                 stop = shape             # <<<<<<<<<<<<<<
- * 
- *         if not have_step:
- */
-      /*else*/ {
-        __pyx_v_stop = __pyx_v_shape;
-      }
-      __pyx_L19:;
-    }
-    __pyx_L16:;
-
-    /* "View.MemoryView":872
- *                 stop = shape
- * 
- *         if not have_step:             # <<<<<<<<<<<<<<
- *             step = 1
- * 
- */
-    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":873
- * 
- *         if not have_step:
- *             step = 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_step = 1;
-
-      /* "View.MemoryView":872
- *                 stop = shape
- * 
- *         if not have_step:             # <<<<<<<<<<<<<<
- *             step = 1
- * 
- */
-    }
-
-    /* "View.MemoryView":877
- * 
- *         with cython.cdivision(True):
- *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
- * 
- *             if (stop - start) - step * new_shape:
- */
-    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);
-
-    /* "View.MemoryView":879
- *             new_shape = (stop - start) // step
- * 
- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
- *                 new_shape += 1
- * 
- */
-    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":880
- * 
- *             if (stop - start) - step * new_shape:
- *                 new_shape += 1             # <<<<<<<<<<<<<<
- * 
- *         if new_shape < 0:
- */
-      __pyx_v_new_shape = (__pyx_v_new_shape + 1);
-
-      /* "View.MemoryView":879
- *             new_shape = (stop - start) // step
- * 
- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
- *                 new_shape += 1
- * 
- */
-    }
-
-    /* "View.MemoryView":882
- *                 new_shape += 1
- * 
- *         if new_shape < 0:             # <<<<<<<<<<<<<<
- *             new_shape = 0
- * 
- */
-    __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":883
- * 
- *         if new_shape < 0:
- *             new_shape = 0             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_new_shape = 0;
-
-      /* "View.MemoryView":882
- *                 new_shape += 1
- * 
- *         if new_shape < 0:             # <<<<<<<<<<<<<<
- *             new_shape = 0
- * 
- */
-    }
-
-    /* "View.MemoryView":886
- * 
- * 
- *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
- *         dst.shape[new_ndim] = new_shape
- *         dst.suboffsets[new_ndim] = suboffset
- */
-    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);
-
-    /* "View.MemoryView":887
- * 
- *         dst.strides[new_ndim] = stride * step
- *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<
- *         dst.suboffsets[new_ndim] = suboffset
- * 
- */
-    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;
-
-    /* "View.MemoryView":888
- *         dst.strides[new_ndim] = stride * step
- *         dst.shape[new_ndim] = new_shape
- *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":891
- * 
- * 
- *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
- *         dst.data += start * stride
- *     else:
- */
-  __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":892
- * 
- *     if suboffset_dim[0] < 0:
- *         dst.data += start * stride             # <<<<<<<<<<<<<<
- *     else:
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- */
-    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));
-
-    /* "View.MemoryView":891
- * 
- * 
- *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
- *         dst.data += start * stride
- *     else:
- */
-    goto __pyx_L23;
-  }
-
-  /* "View.MemoryView":894
- *         dst.data += start * stride
- *     else:
- *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<
- * 
- *     if suboffset >= 0:
- */
-  /*else*/ {
-    __pyx_t_3 = (__pyx_v_suboffset_dim[0]);
-    (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride));
-  }
-  __pyx_L23:;
-
-  /* "View.MemoryView":896
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- * 
- *     if suboffset >= 0:             # <<<<<<<<<<<<<<
- *         if not is_slice:
- *             if new_ndim == 0:
- */
-  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":897
- * 
- *     if suboffset >= 0:
- *         if not is_slice:             # <<<<<<<<<<<<<<
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- */
-    __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":898
- *     if suboffset >= 0:
- *         if not is_slice:
- *             if new_ndim == 0:             # <<<<<<<<<<<<<<
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- */
-      __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":899
- *         if not is_slice:
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_dim(IndexError, "All dimensions preceding dimension %d "
- */
-        __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);
-
-        /* "View.MemoryView":898
- *     if suboffset >= 0:
- *         if not is_slice:
- *             if new_ndim == 0:             # <<<<<<<<<<<<<<
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- */
-        goto __pyx_L26;
-      }
-
-      /* "View.MemoryView":901
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- *                 _err_dim(IndexError, "All dimensions preceding dimension %d "             # <<<<<<<<<<<<<<
- *                                      "must be indexed and not sliced", dim)
- *         else:
- */
-      /*else*/ {
-
-        /* "View.MemoryView":902
- *             else:
- *                 _err_dim(IndexError, "All dimensions preceding dimension %d "
- *                                      "must be indexed and not sliced", dim)             # <<<<<<<<<<<<<<
- *         else:
- *             suboffset_dim[0] = new_ndim
- */
-        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"All dimensions preceding dimension %d must be indexed and not sliced"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 901, __pyx_L1_error)
-      }
-      __pyx_L26:;
-
-      /* "View.MemoryView":897
- * 
- *     if suboffset >= 0:
- *         if not is_slice:             # <<<<<<<<<<<<<<
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- */
-      goto __pyx_L25;
-    }
-
-    /* "View.MemoryView":904
- *                                      "must be indexed and not sliced", dim)
- *         else:
- *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<
- * 
- *     return 0
- */
-    /*else*/ {
-      (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim;
-    }
-    __pyx_L25:;
-
-    /* "View.MemoryView":896
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- * 
- *     if suboffset >= 0:             # <<<<<<<<<<<<<<
- *         if not is_slice:
- *             if new_ndim == 0:
- */
-  }
-
-  /* "View.MemoryView":906
- *             suboffset_dim[0] = new_ndim
- * 
- *     return 0             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_r = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":809
- * 
- * @cname('__pyx_memoryview_slice_memviewslice')
- * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
- *         __Pyx_memviewslice *dst,
- *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  {
-    #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-    #endif
-    __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    #ifdef WITH_THREAD
-    __Pyx_PyGILState_Release(__pyx_gilstate_save);
-    #endif
-  }
-  __pyx_r = -1;
-  __pyx_L0:;
-  return __pyx_r;
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-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0);
-
-  /* "View.MemoryView":1096
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- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         to_object_func = (<_memoryviewslice> memview).to_object_func
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
- */
-  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); 
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1097
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         to_object_func = (<_memoryviewslice> memview).to_object_func             # <<<<<<<<<<<<<<
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
- *     else:
- */
-    __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func;
-    __pyx_v_to_object_func = __pyx_t_3;
-
-    /* "View.MemoryView":1098
- *     if isinstance(memview, _memoryviewslice):
- *         to_object_func = (<_memoryviewslice> memview).to_object_func
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func             # <<<<<<<<<<<<<<
- *     else:
- *         to_object_func = NULL
- */
-    __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func;
-    __pyx_v_to_dtype_func = __pyx_t_4;
-
-    /* "View.MemoryView":1096
- *     cdef int (*to_dtype_func)(char *, object) except 0
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         to_object_func = (<_memoryviewslice> memview).to_object_func
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1100
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
- *     else:
- *         to_object_func = NULL             # <<<<<<<<<<<<<<
- *         to_dtype_func = NULL
- * 
- */
-  /*else*/ {
-    __pyx_v_to_object_func = NULL;
-
-    /* "View.MemoryView":1101
- *     else:
- *         to_object_func = NULL
- *         to_dtype_func = NULL             # <<<<<<<<<<<<<<
- * 
- *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,
- */
-    __pyx_v_to_dtype_func = NULL;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1103
- *         to_dtype_func = NULL
- * 
- *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,             # <<<<<<<<<<<<<<
- *                                 to_object_func, to_dtype_func,
- *                                 memview.dtype_is_object)
- */
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-
-  /* "View.MemoryView":1105
- *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,
- *                                 to_object_func, to_dtype_func,
- *                                 memview.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1103, __pyx_L1_error)
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-  __pyx_t_5 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1089
- * 
- * @cname('__pyx_memoryview_copy_object_from_slice')
- * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<
- *     """
- *     Create a new memoryview object from a given memoryview object and slice.
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1111
- * 
- * 
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
- *     if arg < 0:
- *         return -arg
- */
-
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-
-  /* "View.MemoryView":1112
- * 
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
- *     if arg < 0:             # <<<<<<<<<<<<<<
- *         return -arg
- *     else:
- */
-  __pyx_t_1 = ((__pyx_v_arg < 0) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1113
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
- *     if arg < 0:
- *         return -arg             # <<<<<<<<<<<<<<
- *     else:
- *         return arg
- */
-    __pyx_r = (-__pyx_v_arg);
-    goto __pyx_L0;
-
-    /* "View.MemoryView":1112
- * 
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
- *     if arg < 0:             # <<<<<<<<<<<<<<
- *         return -arg
- *     else:
- */
-  }
-
-  /* "View.MemoryView":1115
- *         return -arg
- *     else:
- *         return arg             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_get_best_slice_order')
- */
-  /*else*/ {
-    __pyx_r = __pyx_v_arg;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1111
- * 
- * 
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
- *     if arg < 0:
- *         return -arg
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1118
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  Py_ssize_t __pyx_v_c_stride;
-  Py_ssize_t __pyx_v_f_stride;
-  char __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
-  /* "View.MemoryView":1123
- *     """
- *     cdef int i
- *     cdef Py_ssize_t c_stride = 0             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t f_stride = 0
- * 
- */
-  __pyx_v_c_stride = 0;
-
-  /* "View.MemoryView":1124
- *     cdef int i
- *     cdef Py_ssize_t c_stride = 0
- *     cdef Py_ssize_t f_stride = 0             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim - 1, -1, -1):
- */
-  __pyx_v_f_stride = 0;
-
-  /* "View.MemoryView":1126
- *     cdef Py_ssize_t f_stride = 0
- * 
- *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]
- */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_i = __pyx_t_1;
-
-    /* "View.MemoryView":1127
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             c_stride = mslice.strides[i]
- *             break
- */
-    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1128
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
- *             break
- * 
- */
-      __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-      /* "View.MemoryView":1129
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-      goto __pyx_L4_break;
-
-      /* "View.MemoryView":1127
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             c_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L4_break:;
-
-  /* "View.MemoryView":1131
- *             break
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- */
-  __pyx_t_1 = __pyx_v_ndim;
-  __pyx_t_3 = __pyx_t_1;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-
-    /* "View.MemoryView":1132
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1133
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
- *             break
- * 
- */
-      __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-      /* "View.MemoryView":1134
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- */
-      goto __pyx_L7_break;
-
-      /* "View.MemoryView":1132
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L7_break:;
-
-  /* "View.MemoryView":1136
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1137
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- *         return 'C'             # <<<<<<<<<<<<<<
- *     else:
- *         return 'F'
- */
-    __pyx_r = 'C';
-    goto __pyx_L0;
-
-    /* "View.MemoryView":1136
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  }
-
-  /* "View.MemoryView":1139
- *         return 'C'
- *     else:
- *         return 'F'             # <<<<<<<<<<<<<<
- * 
- * @cython.cdivision(True)
- */
-  /*else*/ {
-    __pyx_r = 'F';
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1118
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1142
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
-  Py_ssize_t __pyx_v_dst_extent;
-  Py_ssize_t __pyx_v_src_stride;
-  Py_ssize_t __pyx_v_dst_stride;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-  Py_ssize_t __pyx_t_6;
-
-  /* "View.MemoryView":1149
- * 
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- */
-  __pyx_v_src_extent = (__pyx_v_src_shape[0]);
-
-  /* "View.MemoryView":1150
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- */
-  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
-
-  /* "View.MemoryView":1151
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- */
-  __pyx_v_src_stride = (__pyx_v_src_strides[0]);
-
-  /* "View.MemoryView":1152
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
- * 
- *     if ndim == 1:
- */
-  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
-
-  /* "View.MemoryView":1154
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1155
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-    __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-
-    /* "View.MemoryView":1156
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- */
-    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
-    if (__pyx_t_2) {
-      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
-    }
-    __pyx_t_3 = (__pyx_t_2 != 0);
-    __pyx_t_1 = __pyx_t_3;
-    __pyx_L5_bool_binop_done:;
-
-    /* "View.MemoryView":1155
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":1157
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
- *        else:
- *            for i in range(dst_extent):
- */
-      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
-
-      /* "View.MemoryView":1155
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-      goto __pyx_L4;
-    }
-
-    /* "View.MemoryView":1159
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- */
-    /*else*/ {
-      __pyx_t_4 = __pyx_v_dst_extent;
-      __pyx_t_5 = __pyx_t_4;
-      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-        __pyx_v_i = __pyx_t_6;
-
-        /* "View.MemoryView":1160
- *        else:
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
- *                src_data += src_stride
- *                dst_data += dst_stride
- */
-        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
-
-        /* "View.MemoryView":1161
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride             # <<<<<<<<<<<<<<
- *                dst_data += dst_stride
- *     else:
- */
-        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-        /* "View.MemoryView":1162
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- *                dst_data += dst_stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(dst_extent):
- */
-        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-      }
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":1154
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1164
- *                dst_data += dst_stride
- *     else:
- *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *             _copy_strided_to_strided(src_data, src_strides + 1,
- *                                      dst_data, dst_strides + 1,
- */
-  /*else*/ {
-    __pyx_t_4 = __pyx_v_dst_extent;
-    __pyx_t_5 = __pyx_t_4;
-    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-      __pyx_v_i = __pyx_t_6;
-
-      /* "View.MemoryView":1165
- *     else:
- *         for i in range(dst_extent):
- *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
- *                                      dst_data, dst_strides + 1,
- *                                      src_shape + 1, dst_shape + 1,
- */
-      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
-      /* "View.MemoryView":1169
- *                                      src_shape + 1, dst_shape + 1,
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride             # <<<<<<<<<<<<<<
- *             dst_data += dst_stride
- * 
- */
-      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-      /* "View.MemoryView":1170
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride
- *             dst_data += dst_stride             # <<<<<<<<<<<<<<
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
-      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1142
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1172
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
-  /* "View.MemoryView":1175
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
- *                              src.shape, dst.shape, ndim, itemsize)
- * 
- */
-  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1172
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1179
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
-  Py_ssize_t __pyx_v_shape;
-  Py_ssize_t __pyx_v_size;
-  Py_ssize_t __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  Py_ssize_t *__pyx_t_3;
-  Py_ssize_t *__pyx_t_4;
-
-  /* "View.MemoryView":1181
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     for shape in src.shape[:ndim]:
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_size = __pyx_t_1;
-
-  /* "View.MemoryView":1183
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- * 
- *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
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- * 
- */
-  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
-  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
-    __pyx_t_2 = __pyx_t_4;
-    __pyx_v_shape = (__pyx_t_2[0]);
-
-    /* "View.MemoryView":1184
- * 
- *     for shape in src.shape[:ndim]:
- *         size *= shape             # <<<<<<<<<<<<<<
- * 
- *     return size
- */
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-  }
-
-  /* "View.MemoryView":1186
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- * 
- *     return size             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_fill_contig_strides_array')
- */
-  __pyx_r = __pyx_v_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1179
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1189
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
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- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1199
- * 
- *     if order == 'F':
- *         for idx in range(ndim):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-    __pyx_t_2 = __pyx_v_ndim;
-    __pyx_t_3 = __pyx_t_2;
-    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-      __pyx_v_idx = __pyx_t_4;
-
-      /* "View.MemoryView":1200
- *     if order == 'F':
- *         for idx in range(ndim):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- *     else:
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1201
- *         for idx in range(ndim):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-
-    /* "View.MemoryView":1198
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1203
- *             stride *= shape[idx]
- *     else:
- *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-  /*else*/ {
-    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
-      __pyx_v_idx = __pyx_t_2;
-
-      /* "View.MemoryView":1204
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- * 
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1205
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- * 
- *     return stride
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-  }
-  __pyx_L3:;
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-  /* "View.MemoryView":1207
- *             stride *= shape[idx]
- * 
- *     return stride             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- */
-  __pyx_r = __pyx_v_stride;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1189
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) nogil:
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1210
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                              __Pyx_memviewslice *tmpslice,
- *                              char order,
- */
-
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  void *__pyx_v_result;
-  size_t __pyx_v_itemsize;
-  size_t __pyx_v_size;
-  void *__pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  struct __pyx_memoryview_obj *__pyx_t_4;
-  int __pyx_t_5;
-  int __pyx_t_6;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-
-  /* "View.MemoryView":1221
- *     cdef void *result
- * 
- *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- *     cdef size_t size = slice_get_size(src, ndim)
- * 
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
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-  /* "View.MemoryView":1222
- * 
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<
- * 
- *     result = malloc(size)
- */
-  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);
-
-  /* "View.MemoryView":1224
- *     cdef size_t size = slice_get_size(src, ndim)
- * 
- *     result = malloc(size)             # <<<<<<<<<<<<<<
- *     if not result:
- *         _err(MemoryError, NULL)
- */
-  __pyx_v_result = malloc(__pyx_v_size);
-
-  /* "View.MemoryView":1225
- * 
- *     result = malloc(size)
- *     if not result:             # <<<<<<<<<<<<<<
- *         _err(MemoryError, NULL)
- * 
- */
-  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1226
- *     result = malloc(size)
- *     if not result:
- *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<
- * 
- * 
- */
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-
-    /* "View.MemoryView":1225
- * 
- *     result = malloc(size)
- *     if not result:             # <<<<<<<<<<<<<<
- *         _err(MemoryError, NULL)
- * 
- */
-  }
-
-  /* "View.MemoryView":1229
- * 
- * 
- *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):
- */
-  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);
-
-  /* "View.MemoryView":1230
- * 
- *     tmpslice.data = <char *> result
- *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]
- */
-  __pyx_t_4 = __pyx_v_src->memview;
-  __pyx_v_tmpslice->memview = __pyx_t_4;
-
-  /* "View.MemoryView":1231
- *     tmpslice.data = <char *> result
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         tmpslice.shape[i] = src.shape[i]
- *         tmpslice.suboffsets[i] = -1
- */
-  __pyx_t_3 = __pyx_v_ndim;
-  __pyx_t_5 = __pyx_t_3;
-  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-    __pyx_v_i = __pyx_t_6;
-
-    /* "View.MemoryView":1232
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<
- *         tmpslice.suboffsets[i] = -1
- * 
- */
-    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);
-
-    /* "View.MemoryView":1233
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]
- *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
- * 
- *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,
- */
-    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L;
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-
-  /* "View.MemoryView":1235
- *         tmpslice.suboffsets[i] = -1
- * 
- *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<
- *                               ndim, order)
- * 
- */
-  (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order));
-
-  /* "View.MemoryView":1239
- * 
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
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-  __pyx_t_3 = __pyx_v_ndim;
-  __pyx_t_5 = __pyx_t_3;
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-
-    /* "View.MemoryView":1240
- * 
- *     for i in range(ndim):
- *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
- *             tmpslice.strides[i] = 0
- * 
- */
-    __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1241
- *     for i in range(ndim):
- *         if tmpslice.shape[i] == 1:
- *             tmpslice.strides[i] = 0             # <<<<<<<<<<<<<<
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- *     if slice_is_contig(src[0], order, ndim):
- */
-      (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0;
-
-      /* "View.MemoryView":1240
- * 
- *     for i in range(ndim):
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- */
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- * 
- *     if slice_is_contig(src[0], order, ndim):             # <<<<<<<<<<<<<<
- *         memcpy(result, src.data, size)
- *     else:
- */
-  __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1244
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- *         memcpy(result, src.data, size)             # <<<<<<<<<<<<<<
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- *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
- */
-    (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size));
-
-    /* "View.MemoryView":1243
- *             tmpslice.strides[i] = 0
- * 
- *     if slice_is_contig(src[0], order, ndim):             # <<<<<<<<<<<<<<
- *         memcpy(result, src.data, size)
- *     else:
- */
-    goto __pyx_L9;
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-
-  /* "View.MemoryView":1246
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- *     else:
- *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)             # <<<<<<<<<<<<<<
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- *     return result
- */
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- * 
- *     return result             # <<<<<<<<<<<<<<
- * 
- * 
- */
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-  goto __pyx_L0;
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-  /* "View.MemoryView":1210
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- */
-
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- * 
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- *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
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- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- */
-    __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1302
- * 
- *         if src.suboffsets[i] >= 0:
- *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- */
-      __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(2, 1302, __pyx_L1_error)
-
-      /* "View.MemoryView":1301
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- */
-    }
-  }
-
-  /* "View.MemoryView":1304
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1306
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1307
- * 
- *         if not slice_is_contig(src, order, ndim):
- *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- */
-      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
-
-      /* "View.MemoryView":1306
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    }
-
-    /* "View.MemoryView":1309
- *             order = get_best_order(&dst, ndim)
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
- *         src = tmp
- * 
- */
-    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1309, __pyx_L1_error)
-    __pyx_v_tmpdata = __pyx_t_7;
-
-    /* "View.MemoryView":1310
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- *         src = tmp             # <<<<<<<<<<<<<<
- * 
- *     if not broadcasting:
- */
-    __pyx_v_src = __pyx_v_tmp;
-
-    /* "View.MemoryView":1304
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  }
-
-  /* "View.MemoryView":1312
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1315
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1316
- * 
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
-
-      /* "View.MemoryView":1315
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-      goto __pyx_L12;
-    }
-
-    /* "View.MemoryView":1317
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1318
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
- * 
- *         if direct_copy:
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
-
-      /* "View.MemoryView":1317
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    }
-    __pyx_L12:;
-
-    /* "View.MemoryView":1320
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    __pyx_t_2 = (__pyx_v_direct_copy != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1322
- *         if direct_copy:
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-      /* "View.MemoryView":1323
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)
- */
-      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
-
-      /* "View.MemoryView":1324
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
- *             free(tmpdata)
- *             return 0
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-      /* "View.MemoryView":1325
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)             # <<<<<<<<<<<<<<
- *             return 0
- * 
- */
-      free(__pyx_v_tmpdata);
-
-      /* "View.MemoryView":1326
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)
- *             return 0             # <<<<<<<<<<<<<<
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):
- */
-      __pyx_r = 0;
-      goto __pyx_L0;
-
-      /* "View.MemoryView":1320
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    }
-
-    /* "View.MemoryView":1312
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1328
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (__pyx_v_order == 'F');
-  if (__pyx_t_2) {
-    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
-  }
-  __pyx_t_8 = (__pyx_t_2 != 0);
-  if (__pyx_t_8) {
-
-    /* "View.MemoryView":1331
- * 
- * 
- *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
- *         transpose_memslice(&dst)
- * 
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1331, __pyx_L1_error)
-
-    /* "View.MemoryView":1332
- * 
- *         transpose_memslice(&src)
- *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1332, __pyx_L1_error)
-
-    /* "View.MemoryView":1328
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1334
- *         transpose_memslice(&dst)
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, True)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-  /* "View.MemoryView":1335
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
- *     refcount_copying(&dst, dtype_is_object, ndim, True)
- * 
- */
-  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1336
- *     refcount_copying(&dst, dtype_is_object, ndim, False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
- * 
- *     free(tmpdata)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-  /* "View.MemoryView":1338
- *     refcount_copying(&dst, dtype_is_object, ndim, True)
- * 
- *     free(tmpdata)             # <<<<<<<<<<<<<<
- *     return 0
- * 
- */
-  free(__pyx_v_tmpdata);
-
-  /* "View.MemoryView":1339
- * 
- *     free(tmpdata)
- *     return 0             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- */
-  __pyx_r = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1270
- * 
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice dst,
- *                                   int src_ndim, int dst_ndim,
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  {
-    #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-    #endif
-    __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    #ifdef WITH_THREAD
-    __Pyx_PyGILState_Release(__pyx_gilstate_save);
-    #endif
-  }
-  __pyx_r = -1;
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1342
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
- *                             int ndim,
- *                             int ndim_other) nogil:
- */
-
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) {
-  int __pyx_v_i;
-  int __pyx_v_offset;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-
-  /* "View.MemoryView":1346
- *                             int ndim_other) nogil:
- *     cdef int i
- *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim - 1, -1, -1):
- */
-  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);
-
-  /* "View.MemoryView":1348
- *     cdef int offset = ndim_other - ndim
- * 
- *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *         mslice.shape[i + offset] = mslice.shape[i]
- *         mslice.strides[i + offset] = mslice.strides[i]
- */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_i = __pyx_t_1;
-
-    /* "View.MemoryView":1349
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         mslice.shape[i + offset] = mslice.shape[i]             # <<<<<<<<<<<<<<
- *         mslice.strides[i + offset] = mslice.strides[i]
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- */
-    (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);
-
-    /* "View.MemoryView":1350
- *     for i in range(ndim - 1, -1, -1):
- *         mslice.shape[i + offset] = mslice.shape[i]
- *         mslice.strides[i + offset] = mslice.strides[i]             # <<<<<<<<<<<<<<
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- * 
- */
-    (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-    /* "View.MemoryView":1351
- *         mslice.shape[i + offset] = mslice.shape[i]
- *         mslice.strides[i + offset] = mslice.strides[i]
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]             # <<<<<<<<<<<<<<
- * 
- *     for i in range(offset):
- */
-    (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);
-  }
-
-  /* "View.MemoryView":1353
- *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
- * 
- *     for i in range(offset):             # <<<<<<<<<<<<<<
- *         mslice.shape[i] = 1
- *         mslice.strides[i] = mslice.strides[0]
- */
-  __pyx_t_1 = __pyx_v_offset;
-  __pyx_t_2 = __pyx_t_1;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
-
-    /* "View.MemoryView":1354
- * 
- *     for i in range(offset):
- *         mslice.shape[i] = 1             # <<<<<<<<<<<<<<
- *         mslice.strides[i] = mslice.strides[0]
- *         mslice.suboffsets[i] = -1
- */
-    (__pyx_v_mslice->shape[__pyx_v_i]) = 1;
-
-    /* "View.MemoryView":1355
- *     for i in range(offset):
- *         mslice.shape[i] = 1
- *         mslice.strides[i] = mslice.strides[0]             # <<<<<<<<<<<<<<
- *         mslice.suboffsets[i] = -1
- * 
- */
-    (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]);
-
-    /* "View.MemoryView":1356
- *         mslice.shape[i] = 1
- *         mslice.strides[i] = mslice.strides[0]
- *         mslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L;
-  }
-
-  /* "View.MemoryView":1342
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
- *                             int ndim,
- *                             int ndim_other) nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1364
- * 
- * @cname('__pyx_memoryview_refcount_copying')
- * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
- *                            int ndim, bint inc) nogil:
- * 
- */
-
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {
-  int __pyx_t_1;
-
-  /* "View.MemoryView":1368
- * 
- * 
- *     if dtype_is_object:             # <<<<<<<<<<<<<<
- *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,
- *                                            dst.strides, ndim, inc)
- */
-  __pyx_t_1 = (__pyx_v_dtype_is_object != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1369
- * 
- *     if dtype_is_object:
- *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,             # <<<<<<<<<<<<<<
- *                                            dst.strides, ndim, inc)
- * 
- */
-    __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);
-
-    /* "View.MemoryView":1368
- * 
- * 
- *     if dtype_is_object:             # <<<<<<<<<<<<<<
- *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,
- *                                            dst.strides, ndim, inc)
- */
-  }
-
-  /* "View.MemoryView":1364
- * 
- * @cname('__pyx_memoryview_refcount_copying')
- * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
- *                            int ndim, bint inc) nogil:
- * 
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1373
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
- * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                                              Py_ssize_t *strides, int ndim,
- *                                              bint inc) with gil:
- */
-
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
-  __Pyx_RefNannyDeclarations
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
-  #endif
-  __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0);
-
-  /* "View.MemoryView":1376
- *                                              Py_ssize_t *strides, int ndim,
- *                                              bint inc) with gil:
- *     refcount_objects_in_slice(data, shape, strides, ndim, inc)             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice')
- */
-  __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc);
-
-  /* "View.MemoryView":1373
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-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/
-  0, /*tp_doc*/
-  0, /*tp_traverse*/
-  0, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_array, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_array, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_array, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-  0, /*tp_print*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
-  struct __pyx_MemviewEnum_obj *p;
-  PyObject *o;
-  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_MemviewEnum_obj *)o);
-  p->name = Py_None; Py_INCREF(Py_None);
-  return o;
-}
-
-static void __pyx_tp_dealloc_Enum(PyObject *o) {
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  Py_CLEAR(p->name);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  if (p->name) {
-    e = (*v)(p->name, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_Enum(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  tmp = ((PyObject*)p->name);
-  p->name = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  return 0;
-}
-
-static PyMethodDef __pyx_methods_Enum[] = {
-  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0},
-  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0},
-  {0, 0, 0, 0}
-};
-
-static PyTypeObject __pyx_type___pyx_MemviewEnum = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "diff_ttsg.utils.monotonic_align.core.Enum", /*tp_name*/
-  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_MemviewEnum___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_Enum, /*tp_traverse*/
-  __pyx_tp_clear_Enum, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_Enum, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  __pyx_MemviewEnum___init__, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_Enum, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-  0, /*tp_print*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
-
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryview_obj *p;
-  PyObject *o;
-  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryview_obj *)o);
-  p->__pyx_vtab = __pyx_vtabptr_memoryview;
-  p->obj = Py_None; Py_INCREF(Py_None);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  p->view.obj = NULL;
-  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
-  return o;
-  bad:
-  Py_DECREF(o); o = 0;
-  return NULL;
-}
-
-static void __pyx_tp_dealloc_memoryview(PyObject *o) {
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryview___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->obj);
-  Py_CLEAR(p->_size);
-  Py_CLEAR(p->_array_interface);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  if (p->obj) {
-    e = (*v)(p->obj, a); if (e) return e;
-  }
-  if (p->_size) {
-    e = (*v)(p->_size, a); if (e) return e;
-  }
-  if (p->_array_interface) {
-    e = (*v)(p->_array_interface, a); if (e) return e;
-  }
-  if (p->view.obj) {
-    e = (*v)(p->view.obj, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  tmp = ((PyObject*)p->obj);
-  p->obj = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_size);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_array_interface);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  Py_CLEAR(p->view.obj);
-  return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_memoryview___setitem__(o, i, v);
-  }
-  else {
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
-  {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},
-  {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},
-  {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},
-  {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},
-  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},
-  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
-  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
-  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
-  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
-  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
-  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
-  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
-  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
-  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
-  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
-  __pyx_memoryview___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_memoryview, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
-  __pyx_memoryview___len__, /*mp_length*/
-  __pyx_memoryview___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "diff_ttsg.utils.monotonic_align.core.memoryview", /*tp_name*/
-  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_memoryview___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  __pyx_memoryview___str__, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_memoryview, /*tp_traverse*/
-  __pyx_tp_clear_memoryview, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_memoryview, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_memoryview, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_memoryview, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-  0, /*tp_print*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryviewslice_obj *p;
-  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryviewslice_obj *)o);
-  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  p->from_slice.memview = NULL;
-  return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryviewslice___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->from_object);
-  PyObject_GC_Track(o);
-  __pyx_tp_dealloc_memoryview(o);
-}
-
-static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
-  if (p->from_object) {
-    e = (*v)(p->from_object, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  __pyx_tp_clear_memoryview(o);
-  tmp = ((PyObject*)p->from_object);
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
-  return 0;
-}
-
-static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods__memoryviewslice[] = {
-  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},
-  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
-  {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-
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-{
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-{
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-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
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-{
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-        #else
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-/* ParseKeywords */
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-    PyObject *kwds2,
-    PyObject *values[],
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-    const char* function_name)
-{
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-                        && _PyString_Eq(**name, key)) {
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-                             && _PyString_Eq(**argname, key))) {
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-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
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-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
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-    if (unlikely(!exc_type)) return 0;
-    if (unlikely(PyTuple_Check(err)))
-        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
-    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyErrFetchRestore */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    tmp_type = tstate->curexc_type;
-    tmp_value = tstate->curexc_value;
-    tmp_tb = tstate->curexc_traceback;
-    tstate->curexc_type = type;
-    tstate->curexc_value = value;
-    tstate->curexc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    *type = tstate->curexc_type;
-    *value = tstate->curexc_value;
-    *tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-}
-#endif
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
-                        CYTHON_UNUSED PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-#if CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
-        name, type->tp_name, Py_TYPE(obj)->tp_name);
-    return 0;
-}
-
-/* PyCFunctionFastCall */
-#if CYTHON_FAST_PYCCALL
-static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
-    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
-    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
-    PyObject *self = PyCFunction_GET_SELF(func);
-    int flags = PyCFunction_GET_FLAGS(func);
-    assert(PyCFunction_Check(func));
-    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
-    assert(nargs >= 0);
-    assert(nargs == 0 || args != NULL);
-    /* _PyCFunction_FastCallDict() must not be called with an exception set,
-       because it may clear it (directly or indirectly) and so the
-       caller loses its exception */
-    assert(!PyErr_Occurred());
-    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
-        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
-    } else {
-        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
-    }
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-#if 1 || PY_VERSION_HEX < 0x030600B1
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args, *result = NULL;
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyFunction_FastCall(function, args, 2);
-    }
-    #endif
-    #if CYTHON_FAST_PYCCALL
-    if (__Pyx_PyFastCFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyCFunction_FastCall(function, args, 2);
-    }
-    #endif
-    args = PyTuple_New(2);
-    if (unlikely(!args)) goto done;
-    Py_INCREF(arg1);
-    PyTuple_SET_ITEM(args, 0, arg1);
-    Py_INCREF(arg2);
-    PyTuple_SET_ITEM(args, 1, arg2);
-    Py_INCREF(function);
-    result = __Pyx_PyObject_Call(function, args, NULL);
-    Py_DECREF(args);
-    Py_DECREF(function);
-done:
-    return result;
-}
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallOneArg */
-#if CYTHON_COMPILING_IN_CPYTHON
-static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_New(1);
-    if (unlikely(!args)) return NULL;
-    Py_INCREF(arg);
-    PyTuple_SET_ITEM(args, 0, arg);
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, &arg, 1);
-    }
-#endif
-    if (likely(PyCFunction_Check(func))) {
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-            return __Pyx_PyObject_CallMethO(func, arg);
-#if CYTHON_FAST_PYCCALL
-        } else if (__Pyx_PyFastCFunction_Check(func)) {
-            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
-#endif
-        }
-    }
-    return __Pyx__PyObject_CallOneArg(func, arg);
-}
-#else
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_Pack(1, arg);
-    if (unlikely(!args)) return NULL;
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-#endif
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000)
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* DivInt[Py_ssize_t] */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
-    Py_ssize_t q = a / b;
-    Py_ssize_t r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* GetAttr */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_USE_TYPE_SLOTS
-#if PY_MAJOR_VERSION >= 3
-    if (likely(PyUnicode_Check(n)))
-#else
-    if (likely(PyString_Check(n)))
-#endif
-        return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
-    return PyObject_GetAttr(o, n);
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (!j) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return m->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
-    if (unlikely(!(m && m->sq_item))) {
-        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
-        return NULL;
-    }
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
-    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(m && m->mp_subscript)) {
-        return m->mp_subscript(obj, key);
-    }
-    return __Pyx_PyObject_GetIndex(obj, key);
-}
-#endif
-
-/* decode_c_string */
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
-         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
-         const char* encoding, const char* errors,
-         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
-    Py_ssize_t length;
-    if (unlikely((start < 0) | (stop < 0))) {
-        size_t slen = strlen(cstring);
-        if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "c-string too long to convert to Python");
-            return NULL;
-        }
-        length = (Py_ssize_t) slen;
-        if (start < 0) {
-            start += length;
-            if (start < 0)
-                start = 0;
-        }
-        if (stop < 0)
-            stop += length;
-    }
-    if (unlikely(stop <= start))
-        return __Pyx_NewRef(__pyx_empty_unicode);
-    length = stop - start;
-    cstring += start;
-    if (decode_func) {
-        return decode_func(cstring, length, errors);
-    } else {
-        return PyUnicode_Decode(cstring, length, encoding, errors);
-    }
-}
-
-/* GetAttr3 */
-static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        return NULL;
-    __Pyx_PyErr_Clear();
-    Py_INCREF(d);
-    return d;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
-    PyObject *r = __Pyx_GetAttr(o, n);
-    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
-}
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* ExtTypeTest */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(__Pyx_TypeCheck(obj, type)))
-        return 1;
-    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
-                 Py_TYPE(obj)->tp_name, type->tp_name);
-    return 0;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-    #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *empty_list = 0;
-    PyObject *module = 0;
-    PyObject *global_dict = 0;
-    PyObject *empty_dict = 0;
-    PyObject *list;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (!py_import)
-        goto bad;
-    #endif
-    if (from_list)
-        list = from_list;
-    else {
-        empty_list = PyList_New(0);
-        if (!empty_list)
-            goto bad;
-        list = empty_list;
-    }
-    global_dict = PyModule_GetDict(__pyx_m);
-    if (!global_dict)
-        goto bad;
-    empty_dict = PyDict_New();
-    if (!empty_dict)
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                module = PyImport_ImportModuleLevelObject(
-                    name, global_dict, empty_dict, list, 1);
-                if (!module) {
-                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (!py_level)
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, global_dict, empty_dict, list, level);
-            #endif
-        }
-    }
-bad:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    Py_XDECREF(empty_list);
-    Py_XDECREF(empty_dict);
-    return module;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = a->tp_base;
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
-    if (!res) {
-        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-    return res;
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
-    (void)inplace;
-    (void)zerodivision_check;
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-            x = (long)((unsigned long)a + b);
-            if (likely((x^a) >= 0 || (x^b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_add(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        const digit* digits = ((PyLongObject*)op1)->ob_digit;
-        const Py_ssize_t size = Py_SIZE(op1);
-        if (likely(__Pyx_sst_abs(size) <= 1)) {
-            a = likely(size) ? digits[0] : 0;
-            if (size == -1) a = -a;
-        } else {
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
-            }
-        }
-                x = a + b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla + llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-        double a = PyFloat_AS_DOUBLE(op1);
-            double result;
-            PyFPE_START_PROTECT("add", return NULL)
-            result = ((double)a) + (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* DivInt[long] */
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* HasAttr */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
-    PyObject *r;
-    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "hasattr(): attribute name must be string");
-        return -1;
-    }
-    r = __Pyx_GetAttr(o, n);
-    if (unlikely(!r)) {
-        PyErr_Clear();
-        return 0;
-    } else {
-        Py_DECREF(r);
-        return 1;
-    }
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'%.50s' object has no attribute '%U'",
-                 tp->tp_name, attr_name);
-#else
-                 "'%.50s' object has no attribute '%.400s'",
-                 tp->tp_name, PyString_AS_STRING(attr_name));
-#endif
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* PyObject_GenericGetAttr */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
-    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
-}
-#endif
-
-/* SetVTable */
-static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
-#if PY_VERSION_HEX >= 0x02070000
-    PyObject *ob = PyCapsule_New(vtable, 0, 0);
-#else
-    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
-#endif
-    if (!ob)
-        goto bad;
-    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
-        goto bad;
-    Py_DECREF(ob);
-    return 0;
-bad:
-    Py_XDECREF(ob);
-    return -1;
-}
-
-/* PyObjectGetAttrStrNoError */
-static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        __Pyx_PyErr_Clear();
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {
-    PyObject *result;
-#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {
-        return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);
-    }
-#endif
-    result = __Pyx_PyObject_GetAttrStr(obj, attr_name);
-    if (unlikely(!result)) {
-        __Pyx_PyObject_GetAttrStr_ClearAttributeError();
-    }
-    return result;
-}
-
-/* SetupReduce */
-static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
-  int ret;
-  PyObject *name_attr;
-  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);
-  if (likely(name_attr)) {
-      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
-  } else {
-      ret = -1;
-  }
-  if (unlikely(ret < 0)) {
-      PyErr_Clear();
-      ret = 0;
-  }
-  Py_XDECREF(name_attr);
-  return ret;
-}
-static int __Pyx_setup_reduce(PyObject* type_obj) {
-    int ret = 0;
-    PyObject *object_reduce = NULL;
-    PyObject *object_getstate = NULL;
-    PyObject *object_reduce_ex = NULL;
-    PyObject *reduce = NULL;
-    PyObject *reduce_ex = NULL;
-    PyObject *reduce_cython = NULL;
-    PyObject *setstate = NULL;
-    PyObject *setstate_cython = NULL;
-    PyObject *getstate = NULL;
-#if CYTHON_USE_PYTYPE_LOOKUP
-    getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate);
-#else
-    getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate);
-    if (!getstate && PyErr_Occurred()) {
-        goto __PYX_BAD;
-    }
-#endif
-    if (getstate) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate);
-#else
-        object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate);
-        if (!object_getstate && PyErr_Occurred()) {
-            goto __PYX_BAD;
-        }
-#endif
-        if (object_getstate != getstate) {
-            goto __PYX_GOOD;
-        }
-    }
-#if CYTHON_USE_PYTYPE_LOOKUP
-    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#else
-    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#endif
-    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
-    if (reduce_ex == object_reduce_ex) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#else
-        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#endif
-        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
-        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
-            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
-            if (likely(reduce_cython)) {
-                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-            } else if (reduce == object_reduce || PyErr_Occurred()) {
-                goto __PYX_BAD;
-            }
-            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
-            if (!setstate) PyErr_Clear();
-            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
-                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
-                if (likely(setstate_cython)) {
-                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                } else if (!setstate || PyErr_Occurred()) {
-                    goto __PYX_BAD;
-                }
-            }
-            PyType_Modified((PyTypeObject*)type_obj);
-        }
-    }
-    goto __PYX_GOOD;
-__PYX_BAD:
-    if (!PyErr_Occurred())
-        PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
-    ret = -1;
-__PYX_GOOD:
-#if !CYTHON_USE_PYTYPE_LOOKUP
-    Py_XDECREF(object_reduce);
-    Py_XDECREF(object_reduce_ex);
-    Py_XDECREF(object_getstate);
-    Py_XDECREF(getstate);
-#endif
-    Py_XDECREF(reduce);
-    Py_XDECREF(reduce_ex);
-    Py_XDECREF(reduce_cython);
-    Py_XDECREF(setstate);
-    Py_XDECREF(setstate_cython);
-    return ret;
-}
-
-/* TypeImport */
-#ifndef __PYX_HAVE_RT_ImportType_0_29_35
-#define __PYX_HAVE_RT_ImportType_0_29_35
-static PyTypeObject *__Pyx_ImportType_0_29_35(PyObject *module, const char *module_name, const char *class_name,
-    size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_0_29_35 check_size)
-{
-    PyObject *result = 0;
-    char warning[200];
-    Py_ssize_t basicsize;
-    Py_ssize_t itemsize;
-#ifdef Py_LIMITED_API
-    PyObject *py_basicsize;
-    PyObject *py_itemsize;
-#endif
-    result = PyObject_GetAttrString(module, class_name);
-    if (!result)
-        goto bad;
-    if (!PyType_Check(result)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s.%.200s is not a type object",
-            module_name, class_name);
-        goto bad;
-    }
-#ifndef Py_LIMITED_API
-    basicsize = ((PyTypeObject *)result)->tp_basicsize;
-    itemsize = ((PyTypeObject *)result)->tp_itemsize;
-#else
-    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
-    if (!py_basicsize)
-        goto bad;
-    basicsize = PyLong_AsSsize_t(py_basicsize);
-    Py_DECREF(py_basicsize);
-    py_basicsize = 0;
-    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
-        goto bad;
-    py_itemsize = PyObject_GetAttrString(result, "__itemsize__");
-    if (!py_itemsize)
-        goto bad;
-    itemsize = PyLong_AsSsize_t(py_itemsize);
-    Py_DECREF(py_itemsize);
-    py_itemsize = 0;
-    if (itemsize == (Py_ssize_t)-1 && PyErr_Occurred())
-        goto bad;
-#endif
-    if (itemsize) {
-        if (size % alignment) {
-            alignment = size % alignment;
-        }
-        if (itemsize < (Py_ssize_t)alignment)
-            itemsize = (Py_ssize_t)alignment;
-    }
-    if ((size_t)(basicsize + itemsize) < size) {
-        PyErr_Format(PyExc_ValueError,
-            "%.200s.%.200s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        goto bad;
-    }
-    if (check_size == __Pyx_ImportType_CheckSize_Error_0_29_35 && (size_t)basicsize != size) {
-        PyErr_Format(PyExc_ValueError,
-            "%.200s.%.200s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        goto bad;
-    }
-    else if (check_size == __Pyx_ImportType_CheckSize_Warn_0_29_35 && (size_t)basicsize > size) {
-        PyOS_snprintf(warning, sizeof(warning),
-            "%s.%s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
-    }
-    return (PyTypeObject *)result;
-bad:
-    Py_XDECREF(result);
-    return NULL;
-}
-#endif
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
-    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-    if ((0)) {}
-    view->obj = NULL;
-    Py_DECREF(obj);
-}
-#endif
-
-
-/* MemviewSliceIsContig */
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
-{
-    int i, index, step, start;
-    Py_ssize_t itemsize = mvs.memview->view.itemsize;
-    if (order == 'F') {
-        step = 1;
-        start = 0;
-    } else {
-        step = -1;
-        start = ndim - 1;
-    }
-    for (i = 0; i < ndim; i++) {
-        index = start + step * i;
-        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
-            return 0;
-        itemsize *= mvs.shape[index];
-    }
-    return 1;
-}
-
-/* OverlappingSlices */
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
-                               void **out_start, void **out_end,
-                               int ndim, size_t itemsize)
-{
-    char *start, *end;
-    int i;
-    start = end = slice->data;
-    for (i = 0; i < ndim; i++) {
-        Py_ssize_t stride = slice->strides[i];
-        Py_ssize_t extent = slice->shape[i];
-        if (extent == 0) {
-            *out_start = *out_end = start;
-            return;
-        } else {
-            if (stride > 0)
-                end += stride * (extent - 1);
-            else
-                start += stride * (extent - 1);
-        }
-    }
-    *out_start = start;
-    *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                     __Pyx_memviewslice *slice2,
-                     int ndim, size_t itemsize)
-{
-    void *start1, *end1, *start2, *end2;
-    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
-    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
-    return (start1 < end2) && (start2 < end1);
-}
-
-/* Capsule */
-static CYTHON_INLINE PyObject *
-__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
-{
-    PyObject *cobj;
-#if PY_VERSION_HEX >= 0x02070000
-    cobj = PyCapsule_New(p, sig, NULL);
-#else
-    cobj = PyCObject_FromVoidPtr(p, NULL);
-#endif
-    return cobj;
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
-  union {
-    uint32_t u32;
-    uint8_t u8[4];
-  } S;
-  S.u32 = 0x01020304;
-  return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t <= '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case '?': return "'bool'";
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparseable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number, ndim;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    ndim = ctx->head->field->type->ndim;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-        CYTHON_FALLTHROUGH;
-      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
-            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-        CYTHON_FALLTHROUGH;
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-
-/* TypeInfoCompare */
-  static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
-    int i;
-    if (!a || !b)
-        return 0;
-    if (a == b)
-        return 1;
-    if (a->size != b->size || a->typegroup != b->typegroup ||
-            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
-        if (a->typegroup == 'H' || b->typegroup == 'H') {
-            return a->size == b->size;
-        } else {
-            return 0;
-        }
-    }
-    if (a->ndim) {
-        for (i = 0; i < a->ndim; i++)
-            if (a->arraysize[i] != b->arraysize[i])
-                return 0;
-    }
-    if (a->typegroup == 'S') {
-        if (a->flags != b->flags)
-            return 0;
-        if (a->fields || b->fields) {
-            if (!(a->fields && b->fields))
-                return 0;
-            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
-                __Pyx_StructField *field_a = a->fields + i;
-                __Pyx_StructField *field_b = b->fields + i;
-                if (field_a->offset != field_b->offset ||
-                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
-                    return 0;
-            }
-            return !a->fields[i].type && !b->fields[i].type;
-        }
-    }
-    return 1;
-}
-
-/* MemviewSliceValidateAndInit */
-  static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    if (buf->shape[dim] <= 1)
-        return 1;
-    if (buf->strides) {
-        if (spec & __Pyx_MEMVIEW_CONTIG) {
-            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
-                if (unlikely(buf->strides[dim] != sizeof(void *))) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "Buffer is not indirectly contiguous "
-                                 "in dimension %d.", dim);
-                    goto fail;
-                }
-            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-        if (spec & __Pyx_MEMVIEW_FOLLOW) {
-            Py_ssize_t stride = buf->strides[dim];
-            if (stride < 0)
-                stride = -stride;
-            if (unlikely(stride < buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-    } else {
-        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not contiguous in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not indirect in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(buf->suboffsets)) {
-            PyErr_SetString(PyExc_ValueError,
-                            "Buffer exposes suboffsets but no strides");
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
-{
-    if (spec & __Pyx_MEMVIEW_DIRECT) {
-        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer not compatible with direct access "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    if (spec & __Pyx_MEMVIEW_PTR) {
-        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer is not indirectly accessible "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
-    int i;
-    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = 0; i < ndim; i++) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not fortran contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = ndim - 1; i >- 1; i--) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not C contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj)
-{
-    struct __pyx_memoryview_obj *memview, *new_memview;
-    __Pyx_RefNannyDeclarations
-    Py_buffer *buf;
-    int i, spec = 0, retval = -1;
-    __Pyx_BufFmt_Context ctx;
-    int from_memoryview = __pyx_memoryview_check(original_obj);
-    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
-    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
-                                                            original_obj)->typeinfo)) {
-        memview = (struct __pyx_memoryview_obj *) original_obj;
-        new_memview = NULL;
-    } else {
-        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                            original_obj, buf_flags, 0, dtype);
-        new_memview = memview;
-        if (unlikely(!memview))
-            goto fail;
-    }
-    buf = &memview->view;
-    if (unlikely(buf->ndim != ndim)) {
-        PyErr_Format(PyExc_ValueError,
-                "Buffer has wrong number of dimensions (expected %d, got %d)",
-                ndim, buf->ndim);
-        goto fail;
-    }
-    if (new_memview) {
-        __Pyx_BufFmt_Init(&ctx, stack, dtype);
-        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
-    }
-    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
-        PyErr_Format(PyExc_ValueError,
-                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
-                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
-                     buf->itemsize,
-                     (buf->itemsize > 1) ? "s" : "",
-                     dtype->name,
-                     dtype->size,
-                     (dtype->size > 1) ? "s" : "");
-        goto fail;
-    }
-    if (buf->len > 0) {
-        for (i = 0; i < ndim; i++) {
-            spec = axes_specs[i];
-            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
-                goto fail;
-            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
-                goto fail;
-        }
-        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
-            goto fail;
-    }
-    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
-                                         new_memview != NULL) == -1)) {
-        goto fail;
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    Py_XDECREF(new_memview);
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_float, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* CIntFromPyVerify */
-  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* Declarations */
-  #if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return ::std::complex< float >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return x + y*(__pyx_t_float_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      __pyx_t_float_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-  #if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabsf(b.real) >= fabsf(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                float r = b.imag / b.real;
-                float s = (float)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_float_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            float r = b.real / b.imag;
-            float s = (float)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_float_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            float denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_float_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrtf(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypotf(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-            __pyx_t_float_complex z;
-            float r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    float denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_float(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_float(a, a);
-                        return __Pyx_c_prod_float(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_float(a, a);
-                        return __Pyx_c_prod_float(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if ((b.imag == 0) && (a.real >= 0)) {
-                    z.real = powf(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2f(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_float(a);
-                theta = atan2f(a.imag, a.real);
-            }
-            lnr = logf(r);
-            z_r = expf(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cosf(z_theta);
-            z.imag = z_r * sinf(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* Declarations */
-  #if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return ::std::complex< double >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return x + y*(__pyx_t_double_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      __pyx_t_double_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-  #if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabs(b.real) >= fabs(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                double r = b.imag / b.real;
-                double s = (double)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_double_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            double r = b.real / b.imag;
-            double s = (double)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_double_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            double denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_double_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrt(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypot(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-            __pyx_t_double_complex z;
-            double r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    double denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_double(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if ((b.imag == 0) && (a.real >= 0)) {
-                    z.real = pow(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_double(a);
-                theta = atan2(a.imag, a.real);
-            }
-            lnr = log(r);
-            z_r = exp(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cos(z_theta);
-            z.imag = z_r * sin(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* MemviewSliceCopyTemplate */
-  static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object)
-{
-    __Pyx_RefNannyDeclarations
-    int i;
-    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
-    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
-    Py_buffer *buf = &from_memview->view;
-    PyObject *shape_tuple = NULL;
-    PyObject *temp_int = NULL;
-    struct __pyx_array_obj *array_obj = NULL;
-    struct __pyx_memoryview_obj *memview_obj = NULL;
-    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
-    for (i = 0; i < ndim; i++) {
-        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
-            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
-                                           "indirect dimensions (axis %d)", i);
-            goto fail;
-        }
-    }
-    shape_tuple = PyTuple_New(ndim);
-    if (unlikely(!shape_tuple)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(shape_tuple);
-    for(i = 0; i < ndim; i++) {
-        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
-        if(unlikely(!temp_int)) {
-            goto fail;
-        } else {
-            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
-            temp_int = NULL;
-        }
-    }
-    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
-    if (unlikely(!array_obj)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(array_obj);
-    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                    (PyObject *) array_obj, contig_flag,
-                                    dtype_is_object,
-                                    from_mvs->memview->typeinfo);
-    if (unlikely(!memview_obj))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
-        goto fail;
-    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
-                                                dtype_is_object) < 0))
-        goto fail;
-    goto no_fail;
-fail:
-    __Pyx_XDECREF(new_mvs.memview);
-    new_mvs.memview = NULL;
-    new_mvs.data = NULL;
-no_fail:
-    __Pyx_XDECREF(shape_tuple);
-    __Pyx_XDECREF(temp_int);
-    __Pyx_XDECREF(array_obj);
-    __Pyx_RefNannyFinishContext();
-    return new_mvs;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
-                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
-                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
-                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
-                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
-                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
-                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const char neg_one = (char) -1, const_zero = (char) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(char) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
-                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
-                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
-                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (char) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(char) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(char) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            char val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (char) -1;
-        }
-    } else {
-        char val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to char");
-    return (char) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to char");
-    return (char) -1;
-}
-
-/* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[5];
-    int same=1, i, found_dot;
-    const char* rt_from_call = Py_GetVersion();
-    PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    found_dot = 0;
-    for (i = 0; i < 4; i++) {
-        if (!ctversion[i]) {
-            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');
-            break;
-        }
-        if (rt_from_call[i] != ctversion[i]) {
-            same = 0;
-            break;
-        }
-    }
-    if (!same) {
-        char rtversion[5] = {'\0'};
-        char message[200];
-        for (i=0; i<4; ++i) {
-            if (rt_from_call[i] == '.') {
-                if (found_dot) break;
-                found_dot = 1;
-            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {
-                break;
-            }
-            rtversion[i] = rt_from_call[i];
-        }
-        PyOS_snprintf(message, sizeof(message),
-                      "compiletime version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION < 3
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        #else
-        if (t->is_unicode | t->is_str) {
-            if (t->intern) {
-                *t->p = PyUnicode_InternFromString(t->s);
-            } else if (t->encoding) {
-                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
-            } else {
-                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
-            }
-        } else {
-            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
-        }
-        #endif
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
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-                PyUnicode_AsASCIIString(o);
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-            }
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-#endif
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-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
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-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type %.200s).  "
-                "The ability to return an instance of a strict subclass of int "
-                "is deprecated, and may be removed in a future version of Python.",
-                Py_TYPE(result)->tp_name)) {
-            Py_DECREF(result);
-            return NULL;
-        }
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type %.200s)",
-                 type_name, type_name, Py_TYPE(result)->tp_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    const digit* digits = ((PyLongObject*)b)->ob_digit;
-    const Py_ssize_t size = Py_SIZE(b);
-    if (likely(__Pyx_sst_abs(size) <= 1)) {
-        ival = likely(size) ? digits[0] : 0;
-        if (size == -1) ival = -ival;
-        return ival;
-    } else {
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
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-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
-  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
-    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
-  } else if (likely(PyInt_CheckExact(o))) {
-    return PyInt_AS_LONG(o);
-#endif
-  } else {
-    Py_ssize_t ival;
-    PyObject *x;
-    x = PyNumber_Index(o);
-    if (!x) return -1;
-    ival = PyInt_AsLong(x);
-    Py_DECREF(x);
-    return ival;
-  }
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/spaces/shiwan10000/CodeFormer/CodeFormer/facelib/detection/align_trans.py b/spaces/shiwan10000/CodeFormer/CodeFormer/facelib/detection/align_trans.py
deleted file mode 100644
index 07f1eb365462c2ec5bbac6d1854c786b6fd6be90..0000000000000000000000000000000000000000
--- a/spaces/shiwan10000/CodeFormer/CodeFormer/facelib/detection/align_trans.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import cv2
-import numpy as np
-
-from .matlab_cp2tform import get_similarity_transform_for_cv2
-
-# reference facial points, a list of coordinates (x,y)
-REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
-                           [33.54930115, 92.3655014], [62.72990036, 92.20410156]]
-
-DEFAULT_CROP_SIZE = (96, 112)
-
-
-class FaceWarpException(Exception):
-
-    def __str__(self):
-        return 'In File {}:{}'.format(__file__, super.__str__(self))
-
-
-def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
-    """
-    Function:
-    ----------
-        get reference 5 key points according to crop settings:
-        0. Set default crop_size:
-            if default_square:
-                crop_size = (112, 112)
-            else:
-                crop_size = (96, 112)
-        1. Pad the crop_size by inner_padding_factor in each side;
-        2. Resize crop_size into (output_size - outer_padding*2),
-            pad into output_size with outer_padding;
-        3. Output reference_5point;
-    Parameters:
-    ----------
-        @output_size: (w, h) or None
-            size of aligned face image
-        @inner_padding_factor: (w_factor, h_factor)
-            padding factor for inner (w, h)
-        @outer_padding: (w_pad, h_pad)
-            each row is a pair of coordinates (x, y)
-        @default_square: True or False
-            if True:
-                default crop_size = (112, 112)
-            else:
-                default crop_size = (96, 112);
-        !!! make sure, if output_size is not None:
-                (output_size - outer_padding)
-                = some_scale * (default crop_size * (1.0 +
-                inner_padding_factor))
-    Returns:
-    ----------
-        @reference_5point: 5x2 np.array
-            each row is a pair of transformed coordinates (x, y)
-    """
-
-    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
-    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
-
-    # 0) make the inner region a square
-    if default_square:
-        size_diff = max(tmp_crop_size) - tmp_crop_size
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += size_diff
-
-    if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
-
-        return tmp_5pts
-
-    if (inner_padding_factor == 0 and outer_padding == (0, 0)):
-        if output_size is None:
-            return tmp_5pts
-        else:
-            raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
-
-    # check output size
-    if not (0 <= inner_padding_factor <= 1.0):
-        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
-
-    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
-        output_size = tmp_crop_size * \
-            (1 + inner_padding_factor * 2).astype(np.int32)
-        output_size += np.array(outer_padding)
-    if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
-        raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
-
-    # 1) pad the inner region according inner_padding_factor
-    if inner_padding_factor > 0:
-        size_diff = tmp_crop_size * inner_padding_factor * 2
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += np.round(size_diff).astype(np.int32)
-
-    # 2) resize the padded inner region
-    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
-
-    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
-        raise FaceWarpException('Must have (output_size - outer_padding)'
-                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
-
-    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
-    tmp_5pts = tmp_5pts * scale_factor
-    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
-    #    tmp_5pts = tmp_5pts + size_diff / 2
-    tmp_crop_size = size_bf_outer_pad
-
-    # 3) add outer_padding to make output_size
-    reference_5point = tmp_5pts + np.array(outer_padding)
-    tmp_crop_size = output_size
-
-    return reference_5point
-
-
-def get_affine_transform_matrix(src_pts, dst_pts):
-    """
-    Function:
-    ----------
-        get affine transform matrix 'tfm' from src_pts to dst_pts
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points matrix, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points matrix, each row is a pair of coordinates (x, y)
-    Returns:
-    ----------
-        @tfm: 2x3 np.array
-            transform matrix from src_pts to dst_pts
-    """
-
-    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
-    n_pts = src_pts.shape[0]
-    ones = np.ones((n_pts, 1), src_pts.dtype)
-    src_pts_ = np.hstack([src_pts, ones])
-    dst_pts_ = np.hstack([dst_pts, ones])
-
-    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
-
-    if rank == 3:
-        tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
-    elif rank == 2:
-        tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
-
-    return tfm
-
-
-def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
-    """
-    Function:
-    ----------
-        apply affine transform 'trans' to uv
-    Parameters:
-    ----------
-        @src_img: 3x3 np.array
-            input image
-        @facial_pts: could be
-            1)a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        @reference_pts: could be
-            1) a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        or
-            3) None
-            if None, use default reference facial points
-        @crop_size: (w, h)
-            output face image size
-        @align_type: transform type, could be one of
-            1) 'similarity': use similarity transform
-            2) 'cv2_affine': use the first 3 points to do affine transform,
-                    by calling cv2.getAffineTransform()
-            3) 'affine': use all points to do affine transform
-    Returns:
-    ----------
-        @face_img: output face image with size (w, h) = @crop_size
-    """
-
-    if reference_pts is None:
-        if crop_size[0] == 96 and crop_size[1] == 112:
-            reference_pts = REFERENCE_FACIAL_POINTS
-        else:
-            default_square = False
-            inner_padding_factor = 0
-            outer_padding = (0, 0)
-            output_size = crop_size
-
-            reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
-                                                        default_square)
-
-    ref_pts = np.float32(reference_pts)
-    ref_pts_shp = ref_pts.shape
-    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
-        raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if ref_pts_shp[0] == 2:
-        ref_pts = ref_pts.T
-
-    src_pts = np.float32(facial_pts)
-    src_pts_shp = src_pts.shape
-    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
-        raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if src_pts_shp[0] == 2:
-        src_pts = src_pts.T
-
-    if src_pts.shape != ref_pts.shape:
-        raise FaceWarpException('facial_pts and reference_pts must have the same shape')
-
-    if align_type == 'cv2_affine':
-        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
-    elif align_type == 'affine':
-        tfm = get_affine_transform_matrix(src_pts, ref_pts)
-    else:
-        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
-
-    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
-
-    return face_img
diff --git a/spaces/showlab/Show-1/showone/pipelines/pipeline_t2v_interp_pixel.py b/spaces/showlab/Show-1/showone/pipelines/pipeline_t2v_interp_pixel.py
deleted file mode 100644
index 984685e5dd0bca445ccfe8362afc02c17f1febed..0000000000000000000000000000000000000000
--- a/spaces/showlab/Show-1/showone/pipelines/pipeline_t2v_interp_pixel.py
+++ /dev/null
@@ -1,798 +0,0 @@
-import html
-import inspect
-import re
-import urllib.parse as ul
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import torch
-from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
-
-from diffusers.schedulers import DDPMScheduler
-from diffusers.utils import (
-    BACKENDS_MAPPING,
-    is_accelerate_available,
-    is_accelerate_version,
-    is_bs4_available,
-    is_ftfy_available,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-
-from ..models import UNet3DConditionModel
-from . import TextToVideoPipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-if is_bs4_available():
-    from bs4 import BeautifulSoup
-
-if is_ftfy_available():
-    import ftfy
-
-
-def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]:
-    # This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
-    # reshape to ncfhw
-    mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1)
-    std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1)
-    # unnormalize back to [0,1]
-    video = video.mul_(std).add_(mean)
-    video.clamp_(0, 1)
-    # prepare the final outputs
-    i, c, f, h, w = video.shape
-    images = video.permute(2, 3, 0, 4, 1).reshape(
-        f, h, i * w, c
-    )  # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
-    images = images.unbind(dim=0)  # prepare a list of indvidual (consecutive frames)
-    images = [(image.cpu().numpy() * 255).astype("uint8") for image in images]  # f h w c
-    return images
-
-
-class TextToVideoIFInterpPipeline(DiffusionPipeline):
-    tokenizer: T5Tokenizer
-    text_encoder: T5EncoderModel
-
-    unet: UNet3DConditionModel
-    scheduler: DDPMScheduler
-
-    feature_extractor: Optional[CLIPImageProcessor]
-    # safety_checker: Optional[IFSafetyChecker]
-
-    # watermarker: Optional[IFWatermarker]
-
-    bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
-    )  # noqa
-
-    _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]
-
-    def __init__(
-        self,
-        tokenizer: T5Tokenizer,
-        text_encoder: T5EncoderModel,
-        unet: UNet3DConditionModel,
-        scheduler: DDPMScheduler,
-        feature_extractor: Optional[CLIPImageProcessor],
-    ):
-        super().__init__()
-
-        self.register_modules(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            feature_extractor=feature_extractor,
-        )
-        self.safety_checker = None
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
-        models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
-        when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        models = [
-            self.text_encoder,
-            self.unet,
-        ]
-        for cpu_offloaded_model in models:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-        if self.safety_checker is not None:
-            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-      
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-
-        if self.text_encoder is not None:
-            _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
-
-            # Accelerate will move the next model to the device _before_ calling the offload hook of the
-            # previous model. This will cause both models to be present on the device at the same time.
-            # IF uses T5 for its text encoder which is really large. We can manually call the offload
-            # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
-            # the GPU.
-            self.text_encoder_offload_hook = hook
-
-        _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
-
-        # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
-        self.unet_offload_hook = hook
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    def remove_all_hooks(self):
-        if is_accelerate_available():
-            from accelerate.hooks import remove_hook_from_module
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        for model in [self.text_encoder, self.unet, self.safety_checker]:
-            if model is not None:
-                remove_hook_from_module(model, recurse=True)
-
-        self.unet_offload_hook = None
-        self.text_encoder_offload_hook = None
-        self.final_offload_hook = None
-
-    @property
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    @torch.no_grad()
-    def encode_prompt(
-        self,
-        prompt,
-        do_classifier_free_guidance=True,
-        num_images_per_prompt=1,
-        device=None,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        clean_caption: bool = False,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`, *optional*):
-                torch device to place the resulting embeddings on
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        if prompt is not None and negative_prompt is not None:
-            if type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-
-        if device is None:
-            device = self._execution_device
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
-        max_length = 77
-
-        if prompt_embeds is None:
-            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {max_length} tokens: {removed_text}"
-                )
-
-            attention_mask = text_inputs.attention_mask.to(device)
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        if self.text_encoder is not None:
-            dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            dtype = self.unet.dtype
-        else:
-            dtype = None
-
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_attention_mask=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            attention_mask = uncond_input.attention_mask.to(device)
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-        else:
-            negative_prompt_embeds = None
-
-        return prompt_embeds, negative_prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def prepare_intermediate_images(self, batch_size, num_channels, num_frames, height, width, dtype, device, generator):
-        shape = (batch_size, num_channels, num_frames, height, width)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        intermediate_images = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        intermediate_images = intermediate_images * self.scheduler.init_noise_sigma
-        return intermediate_images
-
-    def _text_preprocessing(self, text, clean_caption=False):
-        if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if not isinstance(text, (tuple, list)):
-            text = [text]
-
-        def process(text: str):
-            if clean_caption:
-                text = self._clean_caption(text)
-                text = self._clean_caption(text)
-            else:
-                text = text.lower().strip()
-            return text
-
-        return [process(t) for t in text]
-
-    def _clean_caption(self, caption):
-        caption = str(caption)
-        caption = ul.unquote_plus(caption)
-        caption = caption.strip().lower()
-        caption = re.sub("<person>", "person", caption)
-        # urls:
-        caption = re.sub(
-            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        caption = re.sub(
-            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        # html:
-        caption = BeautifulSoup(caption, features="html.parser").text
-
-        # @<nickname>
-        caption = re.sub(r"@[\w\d]+\b", "", caption)
-
-        # 31C0—31EF CJK Strokes
-        # 31F0—31FF Katakana Phonetic Extensions
-        # 3200—32FF Enclosed CJK Letters and Months
-        # 3300—33FF CJK Compatibility
-        # 3400—4DBF CJK Unified Ideographs Extension A
-        # 4DC0—4DFF Yijing Hexagram Symbols
-        # 4E00—9FFF CJK Unified Ideographs
-        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
-        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
-        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
-        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
-        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
-        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
-        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
-        #######################################################
-
-        # все виды тире / all types of dash --> "-"
-        caption = re.sub(
-            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
-            "-",
-            caption,
-        )
-
-        # кавычки к одному стандарту
-        caption = re.sub(r"[`´«»“”¨]", '"', caption)
-        caption = re.sub(r"[‘’]", "'", caption)
-
-        # &quot;
-        caption = re.sub(r"&quot;?", "", caption)
-        # &amp
-        caption = re.sub(r"&amp", "", caption)
-
-        # ip adresses:
-        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
-
-        # article ids:
-        caption = re.sub(r"\d:\d\d\s+$", "", caption)
-
-        # \n
-        caption = re.sub(r"\\n", " ", caption)
-
-        # "#123"
-        caption = re.sub(r"#\d{1,3}\b", "", caption)
-        # "#12345.."
-        caption = re.sub(r"#\d{5,}\b", "", caption)
-        # "123456.."
-        caption = re.sub(r"\b\d{6,}\b", "", caption)
-        # filenames:
-        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
-
-        #
-        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
-        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
-
-        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
-        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
-
-        # this-is-my-cute-cat / this_is_my_cute_cat
-        regex2 = re.compile(r"(?:\-|\_)")
-        if len(re.findall(regex2, caption)) > 3:
-            caption = re.sub(regex2, " ", caption)
-
-        caption = ftfy.fix_text(caption)
-        caption = html.unescape(html.unescape(caption))
-
-        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
-        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
-        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
-
-        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
-        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
-        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
-        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
-        caption = re.sub(r"\bpage\s+\d+\b", "", caption)
-
-        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...
-
-        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
-
-        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
-        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
-        caption = re.sub(r"\s+", " ", caption)
-
-        caption.strip()
-
-        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
-        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
-        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
-        caption = re.sub(r"^\.\S+$", "", caption)
-
-        return caption.strip()
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        pixel_values,
-        prompt: Union[str, List[str]] = None,
-        num_inference_steps: int = 100,
-        timesteps: List[int] = None,
-        guidance_scale: float = 7.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_frames: int = 16,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "np",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        clean_caption: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        init_noise = None,
-        cond_interpolation = False,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
-                timesteps are used. Must be in descending order.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size):
-                The width in pixels of the generated image.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            clean_caption (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
-                be installed. If the dependencies are not installed, the embeddings will be created from the raw
-                prompt.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
-            returning a tuple, the first element is a list with the generated images, and the second element is a list
-            of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw)
-            or watermarked content, according to the `safety_checker`.
-        """
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
-
-        # 2. Define call parameters
-        height = height or self.unet.config.sample_size
-        width = width or self.unet.config.sample_size
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            do_classifier_free_guidance,
-            num_images_per_prompt=num_images_per_prompt,
-            device=device,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            clean_caption=clean_caption,
-        )
-
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        # 4. Prepare timesteps
-        if timesteps is not None:
-            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
-            timesteps = self.scheduler.timesteps
-            num_inference_steps = len(timesteps)
-        else:
-            self.scheduler.set_timesteps(num_inference_steps, device=device)
-            timesteps = self.scheduler.timesteps
-
-        # 5. Prepare intermediate images
-        pixel_values = pixel_values.to(device)
-        if init_noise is not None:
-            intermediate_images = init_noise
-        else:
-            intermediate_images = self.prepare_intermediate_images(
-                batch_size * num_images_per_prompt,
-                # self.unet.config.in_channels, # mask not noise.
-                pixel_values.shape[1],
-                num_frames,
-                height,
-                width,
-                prompt_embeds.dtype,
-                device,
-                generator,
-            )
-
-        bsz = intermediate_images.shape[0]
-        interp_mask = torch.zeros(bsz, 1, *intermediate_images.shape[2:], device=device, dtype=intermediate_images.dtype)
-        interp_mask[:, :, 0, :, :] = 1
-        interp_mask[:, :, -1, :, :] = 1
-
-        if cond_interpolation:
-            import torch.nn.functional as F
-            pixel_values = F.interpolate(pixel_values[:, :, [0, -1], ...], pixel_values.shape[2:],
-                                         mode="trilinear", align_corners=True)
-        else:
-            raise Exception("apply mask to pixel_values")
-
-        # intermediate_images[:, :, 0, :, :] = pixel_values[:, :, 0, :, :]
-        # intermediate_images[:, :, -1, :, :] = pixel_values[:, :, -1, :, :]
-        pixel_values_condition = torch.cat((pixel_values, interp_mask), dim=1)
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # HACK: see comment in `enable_model_cpu_offload`
-        if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
-            self.text_encoder_offload_hook.offload()
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                intermediate_images_input = torch.cat((intermediate_images, pixel_values_condition), dim=1)
-                model_input = (
-                    torch.cat([intermediate_images_input] * 2) if do_classifier_free_guidance else intermediate_images_input
-                )
-                model_input = self.scheduler.scale_model_input(model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                ).sample
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred_uncond, _ = noise_pred_uncond.split(intermediate_images.shape[1], dim=1)
-                    noise_pred_text, predicted_variance = noise_pred_text.split(intermediate_images.shape[1], dim=1)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                    noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
-
-                if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
-                    noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)
-
-                # reshape latents
-                bsz, channel, frames, width, height = intermediate_images.shape
-                intermediate_images = intermediate_images.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
-                noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, -1, width, height)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                intermediate_images = self.scheduler.step(
-                    noise_pred, t, intermediate_images, **extra_step_kwargs
-                ).prev_sample
-
-                # reshape latents back
-                intermediate_images = intermediate_images[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, intermediate_images)
-
-        video_tensor = intermediate_images
-
-        if output_type == "pt":
-            video = video_tensor
-        else:
-            video = tensor2vid(video_tensor)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (video,)
-
-        return TextToVideoPipelineOutput(frames=video)
\ No newline at end of file
diff --git a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/__init__.py b/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/__init__.py
deleted file mode 100644
index 903d09793c39d08491dd9a6fecefd463b058a251..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# 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.
-"""Python wrappers for third party tools."""
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Ludo King Hack Mod APK for Free in 2022 and Enjoy the Ultimate Board Game Experience.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Ludo King Hack Mod APK for Free in 2022 and Enjoy the Ultimate Board Game Experience.md
deleted file mode 100644
index fd9d280dbc3ccac2a42f4a8aba6488289a33ef3f..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Ludo King Hack Mod APK for Free in 2022 and Enjoy the Ultimate Board Game Experience.md	
+++ /dev/null
@@ -1,130 +0,0 @@
-<br />
-<h1>Ludo King Hack Mod APK Download 2022: Everything You Need to Know</h1>
-<p>Ludo King is one of the most popular and addictive board games in the world. It is a modern version of the ancient Indian game of Pachisi, which was played by kings and queens in the past. Ludo King is a game that can be played by anyone, anywhere, and anytime. It is a game that brings people together and creates fun memories.</p>
-<h2>ludo king hack mod apk download 2022</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733; <a href="https://ssurll.com/2uNZkf">https://ssurll.com/2uNZkf</a></b></p><br /><br />
-<p>But what if you want to spice up your Ludo King experience and get some extra advantages over your opponents? What if you want to unlock all the features and themes of the game without spending any money? What if you want to win every game and become the ultimate Ludo King?</p>
-<p>If you are looking for answers to these questions, then you are in the right place. In this article, we will tell you everything you need to know about Ludo King Hack Mod APK, a modified version of the original game that gives you unlimited access to everything. We will also give you some tips and tricks to play Ludo King like a pro and impress your friends and family.</p>
-<h2>What is Ludo King?</h2>
-<p>Ludo King is a free-to-play mobile game application developed by Gametion Technologies Pvt Ltd, a game studio based in Navi Mumbai, India.  The game is developed on the Unity game engine  and is available on Android, iOS, Kindle, Windows Phone and Microsoft Windows platforms. The game is a modernization of the board game Ludo, which is based on the ancient Indian game of Pachisi. </p>
-<p>Ludo King follows the traditional rules and the old school look of the Ludo game. The objective of the game is to move 4 tokens from the starting point around the board to the center of the board, also known as home. The first person to do this wins the game.  Ludo King has four modes: vs Computer, Local Mode (i.e. pass and play), Online Multiplayer (play with other players around the world) and Private Multiplayer (play with friends in private rooms).   </p>
-<h3>Features of Ludo King</h3>
-<p>Ludo King has many features that make it an enjoyable and engaging game for all ages. Some of these features are:</p>
-<ul>
-<li>Voice Chat: You can talk to your opponents while playing and have fun with your friends and family. </li>
-<li>6 Player Online Multiplayer: You can play with up to 6 players online in the same game and challenge them to a thrilling match. </li>
-<li>Quick Mode: You can play a fast game that finishes quickly with the same excitement as a full-length multiplayer game. </li>
-<li>Team Up Mode: You can team up with your friends and have interesting 2 vs 2 matches against other teams. </li>
-<li>Amazing Themes: You can choose from different themes to enjoy Ludo in different audio-visual worlds. Some of the themes are Nature, Egyptian, Disco, Cake, Candy, Christmas, Penguin, Battle, Diwali, Pirate, Sui Dhaaga, Marble, Alien, Octopus, Taj Mahal, etc.  </li>
-<li>Tournament Mode: You can participate in exciting 8 player tournaments with 6x winning rewards. </li>
-<li>Ludo King Exciting Inventory: You can access various items such as dice, emojis, voice notes, rewards, etc <h3>History of Ludo King</h3>
-<p>Ludo King was launched in February 2016 by Vikash Jaiswal, the founder and CEO of Gametion Technologies Pvt Ltd.   Jaiswal had a passion for games since childhood and wanted to create a game that would appeal to the masses. He was inspired by the classic board game Ludo, which he used to play with his family and friends. He decided to make a digital version of Ludo that would be easy to play and accessible to everyone.  </p>
-<p>Ludo King became an instant hit among the users and received positive reviews from critics and media. It became the first Indian game to cross 100 million downloads on Google Play Store in December 2018.  It also became the top free game on Apple App Store and Windows Store in India.   Ludo King has been downloaded over 500 million times worldwide and has more than 50 million daily active users.  It has also won several awards and accolades, such as the Best Casual Game of 2018 by Google Play Store, the Best Mobile Game of 2019 by IAMAI, the Best Game of 2020 by Google Play Store, etc.   </p>
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-<h3>Rules of Ludo King</h3>
-<p>Ludo King follows the same rules as the traditional board game Ludo. The game can be played by 2 to 6 players, each having a set of 4 tokens of different colors (red, blue, green, yellow, pink or purple). The players have to roll a six-sided die and move their tokens according to the number rolled. The tokens start from their respective corners and move along a fixed path on the board. The objective is to reach the center of the board, also known as home, before the other players. The first player to do so wins the game. </p>
-<p>There are some special rules that make the game more interesting and challenging. These are:</p>
-<ul>
-<li>If a player rolls a six, they get another chance to roll the die. They can also choose to move any token out of their corner if they have not done so yet.</li>
-<li>If a player's token lands on a square occupied by another player's token, the latter is sent back to their corner and has to start again.</li>
-<li>If a player's token lands on a star square, they get a shortcut to reach home faster.</li>
-<li>If a player's token lands on an arrow square, they have to move their token in the direction of the arrow.</li>
-<li>If a player has all four tokens in their home, they can use their turn to move any other player's token on the board.</li>
-</ul>
-<h2>What is Ludo King Hack Mod APK?</h2>
-<p>Ludo King Hack Mod APK is a modified version of the original Ludo King game that gives you unlimited access to everything in the game. It is not an official app from Gametion Technologies Pvt Ltd, but rather a third-party app created by some unknown developers. It is also not available on any official app store, but rather on some unofficial websites or platforms. </p>
-<p>Ludo King Hack Mod APK allows you to enjoy all the features and themes of Ludo King without spending any money or watching any ads. It also gives you some extra advantages over your opponents, such as unlimited coins, gems, dice, sixes, etc. You can also unlock all the characters and modes in the game and play with anyone you want. You can even customize your game settings and preferences according to your liking.</p> <h3>Benefits of Ludo King Hack Mod APK</h3>
-<p>Ludo King Hack Mod APK has many benefits that make it a tempting option for many Ludo King fans. Some of these benefits are:</p>
-<ul>
-<li>You can enjoy all the features and themes of Ludo King without spending any money or watching any ads. You can choose from a variety of themes and customize your game board and tokens as you wish.</li>
-<li>You can get unlimited coins, gems, dice, sixes, etc. that you can use to buy items, unlock characters, play tournaments, etc. You can also use them to boost your game performance and increase your chances of winning.</li>
-<li>You can play with anyone you want, whether they are online or offline. You can invite your friends and family to join your private room and have fun with them. You can also play with random players from around the world and challenge them to a thrilling match.</li>
-<li>You can customize your game settings and preferences according to your liking. You can change the game speed, the number of players, the rules, the sound effects, etc. You can also enable or disable the voice chat feature and talk to your opponents while playing.</li>
-</ul>
-<h3>Risks of Ludo King Hack Mod APK</h3>
-<p>However, Ludo King Hack Mod APK is not without its risks and drawbacks. There are some things that you should be aware of before downloading and installing this app. Some of these risks are:</p>
-<ul>
-<li>Ludo King Hack Mod APK is not an official app from Gametion Technologies Pvt Ltd, but rather a third-party app created by some unknown developers. Therefore, it is not safe or secure to use. It may contain viruses, malware, spyware, etc. that can harm your device or steal your personal information.</li>
-<li>Ludo King Hack Mod APK is not available on any official app store, but rather on some unofficial websites or platforms. Therefore, it is not legal or ethical to use. It may violate the terms and conditions of the original Ludo King game and cause legal issues or penalties.</li>
-<li>Ludo King Hack Mod APK may not work properly or smoothly on your device. It may crash, freeze, lag, or glitch frequently. It may also cause compatibility issues with your device or other apps.</li>
-<li>Ludo King Hack Mod APK may ruin the fun and excitement of the original Ludo King game. It may make the game too easy or boring for you and your opponents. It may also make you lose interest in the game or lose respect from other players.</li>
-</ul>
-<h3>How to Download and Install Ludo King Hack Mod APK</h3>
-<p>If you still want to try Ludo King Hack Mod APK despite its risks and drawbacks, then you need to follow some steps to download and install it on your device. These steps are:</p>
-<ol>
-<li>First, you need to find a reliable and trustworthy website or platform that offers Ludo King Hack Mod APK for free download. You can search for it on Google or any other search engine.</li>
-<li>Next, you need to click on the download link or button provided by the website or platform and wait for the download process to complete.</li>
-<li>Then, you need to go to your device settings and enable the option of installing apps from unknown sources. This will allow you to install Ludo King Hack Mod APK on your device.</li>
-<li>After that, you need to locate the downloaded Ludo King Hack Mod APK file on your device storage and tap on it to start the installation process.</li>
-<li>Finally, you need to follow the instructions on the screen and complete the installation process. Once done, you can launch Ludo King Hack Mod APK on your device and enjoy its features.</li>
-</ol> <h2>Tips and Tricks to Play Ludo King Like a Pro</h2>
-<p>Ludo King is a game of luck and strategy, where you need to use your skills and tactics to win. However, there are some tips and tricks that can help you improve your game and increase your chances of winning. Here are some of them:</p>
-<h3>Open all your pieces</h3>
-<p>One of the first things you should do in Ludo King is to open all your pieces as soon as possible. This will give you more options to move and more opportunities to attack or defend. To open a piece, you need to roll a six on the die. You can also use gems or dice to get a six if you have them.</p>
-<h3>Be patient</h3>
-<p>Another important thing you should do in Ludo King is to be patient and not rush your moves. Sometimes, it is better to wait for the right moment to move or strike, rather than moving randomly or impulsively. You should also avoid moving your pieces too close to your home, as they can be easily captured by your opponents. You should also avoid moving your pieces too far from your home, as they can be easily blocked by your opponents.</p>
-<h3>Time to attack</h3>
-<p>One of the most exciting and satisfying things you can do in Ludo King is to attack your opponents and send their pieces back to their corner. However, you should not attack blindly or recklessly, as it can backfire on you. You should attack when you have a clear advantage or opportunity, such as when your opponent has only one piece left, when your opponent is near your home, when your opponent is blocking your way, etc. You should also attack when you have a backup plan or a safe place to move in case of retaliation.</p>
-<h3>Understand the rules</h3>
-<p>One of the most basic and essential things you should do in Ludo King is to understand the rules of the game and follow them accordingly. You should know how to move your pieces, how to capture your opponents' pieces, how to use the star and arrow squares, how to team up with your allies, etc. You should also know the different game modes and their rules, such as quick mode, team up mode, tournament mode, etc.</p>
-<h3>Try out different game modes</h3>
-<p>One of the most fun and challenging things you can do in Ludo King is to try out different game modes and test your skills against different opponents. You can play with up to 6 players online in the same game and challenge them to a thrilling match. You can also play with your friends and family in private rooms and have fun with them. You can also participate in exciting 8 player tournaments with 6x winning rewards. You can also play with the computer and practice your moves.</p>
-<h2>Conclusion</h2>
-<p>Ludo King is a game that can be enjoyed by anyone, anywhere, and anytime. It is a game that brings people together and creates fun memories. However, if you want to spice up your Ludo King experience and get some extra advantages over your opponents, you can try Ludo King Hack Mod APK, a modified version of the original game that gives you unlimited access to everything.</p>
-<p>Ludo King Hack Mod APK has many benefits that make it a tempting option for many Ludo King fans. However, it also has many risks and drawbacks that make it a risky option for many Ludo King fans. Therefore, you should be careful before downloading and installing this app on your device.</p>
-<p>If you want to play Ludo King like a pro and impress your friends and family, you can follow some tips and tricks that can help you improve your game and increase your chances of winning. You can also try out different game modes and test your skills against different opponents.</p>
-<p>We hope this article has helped you learn everything you need to know about Ludo King Hack Mod APK Download 2022. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some frequently asked questions about Ludo King Hack Mod APK Download 2022:</p>
-<ul>
-<li><b>Q: Is Ludo King Hack Mod APK safe to use?</b></li>
-<li>A: No, Ludo King Hack Mod APK is not safe or secure to use. It may contain viruses, malware, spyware, etc. that can harm your device or steal your personal information.</li>
-<li><b>Q: Is Ludo King Hack Mod APK legal or ethical to use?</b></li>
-<li>A: No, Ludo King Hack Mod APK is not legal or ethical to use. It may violate the terms and conditions of the original Ludo King game and cause legal issues or penalties.</li>
-<li><b>Q: Is Ludo King Hack Mod APK compatible with my device?</b></li>
-<li>A: Ludo King Hack Mod APK may not be compatible with your device. It may crash, freeze, lag, or glitch frequently. It may also cause compatibility issues with your device or other apps.</li>
-<li><b>Q: Is Ludo King Hack Mod APK fun to use?</b></li>
-<li>A: Ludo King Hack Mod APK may not be fun to use. It may make the game too easy or boring for you and your opponents. It may also make you lose interest in the game or lose respect from other players.</li>
-<li><b>Q: How can I download and install Ludo King Hack Mod APK?</b></li>
-<li>A: You can download and install Ludo King Hack Mod APK by following the steps mentioned in this article. However, we do not recommend doing so, as it is not safe, legal, or ethical to use.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Noodly Chaos of Totally Reliable Delivery Service on Your Android Phone.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Noodly Chaos of Totally Reliable Delivery Service on Your Android Phone.md
deleted file mode 100644
index a7f6deb34c563cf31895d8d46aba13717fb8cb4b..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the Noodly Chaos of Totally Reliable Delivery Service on Your Android Phone.md	
+++ /dev/null
@@ -1,120 +0,0 @@
-<br />
-<h1>APK Download Totally Reliable Delivery Service</h1>
-<p>Do you love physics games or chaotic multiplayer titles? Do you want to have some silly fun with your friends or strangers online? Do you want to experience the thrill and hilarity of being a terrible delivery driver? If you answered yes to any of these questions, then you should definitely check out <strong>Totally Reliable Delivery Service</strong>, a wacky physics game where you and up to three other players struggle to deliver packages in an interactive sandbox world. In this article, we will tell you everything you need to know about this game, and how to download and install it on your Android device using APK files.</p>
-<h2>apk download totally reliable delivery service</h2><br /><p><b><b>Download Zip</b> &#9733; <a href="https://ssurll.com/2uNZjP">https://ssurll.com/2uNZjP</a></b></p><br /><br />
- <h2>What is Totally Reliable Delivery Service?</h2>
-<p>Totally Reliable Delivery Service is a ragdoll physics-driven sandbox game that was released in 2020 by We're Five Games and tinyBuild. It is available for Windows, macOS, Linux, PlayStation 4, Xbox One, Nintendo Switch, iOS, and Android platforms. The game has received mostly positive reviews from critics and players, who praised its humor, gameplay, and multiplayer features.</p>
- <h3>A physics-based sandbox game about terrible delivery drivers</h3>
-<p>The main premise of the game is simple: you are a delivery driver who has to deliver packages to customers using various vehicles and machines. However, the game is anything but simple, as you have to deal with unpredictable ragdoll physics, clumsy controls, fragile packages, and a host of obstacles and hazards along the way. The game is designed to be shambolic by design, in order to create maximum comedy from the resulting unpredictability. You can expect to see your character fall flat on their face, crash into walls, lose their grip on packages, get knocked out by flying objects, and more.</p>
- <h3>A multiplayer comedy game with ragdoll physics and noodly chaos</h3>
-<p>While the game can technically be played solo, it is much more fun and enjoyable when you play it with other people. You can join up to three friends or strangers online or locally in split-screen mode, and work together (or against each other) to complete deliveries. You can also communicate with each other using voice chat or emotes, and interact with each other using your hands or gadgets. The game is full of hilarious moments and situations that will make you laugh out loud or scream in frustration.</p>
- <h3>A game with tons of vehicles, gadgets, and distractions to play with</h3>
-<p>The game features a large open-world map that is divided into several zones, each with its own theme, environment, and challenges. You can explore the map using a variety of vehicles, such as cars, trucks, vans, bikes, boats, helicopters, planes, rockets, hovercrafts, UFOs, and more. You can also use different gadgets and tools to help you with your deliveries or just have fun with them, such as grappling hooks, fire extinguishers, jetpacks, balloons, parachutes, cannons, launchers, magnets, explosives, and more. The game also has a lot of distractions that you can take a break from deliveries and play around with them. The world is full of toys, animals, machines, events, secrets, easter eggs, and other surprises that will keep you entertained for hours.</p>
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- <h2>How to download Totally Reliable Delivery Service APK for Android?</h2>
-<p>If you want to play Totally Reliable Delivery Service on your Android device , you have two options: the official way and the alternative way. Let's take a look at each of them and see their advantages and disadvantages.</p>
- <h3>The official way: Google Play Store</h3>
-<p>The official way to download Totally Reliable Delivery Service APK for Android is to use the Google Play Store, the official app store for Android devices. This is the easiest and safest way to get the game, as you just need to search for the game on the store, tap on the install button, and wait for the download and installation to finish. You will also get automatic updates for the game whenever they are available, and you can access the game's online features without any problems.</p>
-<p>However, there are some drawbacks to using the Google Play Store. First of all, you need to have a Google account and a stable internet connection to use the store. Second, you may not be able to download the game if it is not available in your region or if your device is not compatible with it. Third, you may have to pay a fee to download the game, as it is not free on the store. The game costs $4.99 USD on the Google Play Store, which may be too expensive for some people.</p>
- <h3>The alternative way: APKCombo</h3>
-<p>The alternative way to download Totally Reliable Delivery Service APK for Android is to use a third-party website that offers APK files for various apps and games. APK files are the installation files for Android applications, and they can be downloaded and installed manually on your device without using the Google Play Store. One of the most popular and reliable websites that offer APK files is APKCombo, which has a huge collection of APK files for different apps and games, including Totally Reliable Delivery Service.</p>
-<p>Using APKCombo has some advantages over using the Google Play Store. First of all, you don't need a Google account or an internet connection to use it. You can simply visit the website on your browser, search for the game, and download the APK file to your device. Second, you can download the game even if it is not available in your region or if your device is not compatible with it. Third, you can download the game for free, as there is no fee involved in using APKCombo.</p>
-<p>However, there are also some risks and challenges involved in using APKCombo. First of all, you need to enable unknown sources on your device settings to allow the installation of APK files from outside sources. This may expose your device to potential security threats or malware infections. Second, you may not get the latest version of the game or its updates, as APKCombo may not always have them. Third, you may face some issues with the game's online features or compatibility, as APKCombo does not guarantee that they will work properly.</p>
- <h3>The advantages and disadvantages of each way</h3>
-<p>To summarize, here are the pros and cons of each way to download Totally Reliable Delivery Service APK for Android:</p>
- | Way | Advantages | Disadvantages | | --- | --- | --- | | Google Play Store | Easy and safe | Requires Google account and internet connection | |  | Automatic updates | May not be available in your region or compatible with your device | |  | Online features work well | Costs $4.99 USD | | APKCombo | No Google account or internet connection required | Requires unknown sources enabled | |  | Available in any region and compatible with any device | May not have latest version or updates | |  | Free of charge | Online features may not work well | <p>You can choose either way depending on your preferences and circumstances, but we recommend that you use the official way if possible, as it is more secure and reliable.</p>
- <h2>How to install and play Totally Reliable Delivery Service APK on Android?</h2>
-<p>Now that you know how to download Totally Reliable Delivery Service APK for Android, let's see how to install and play it on your device. The installation steps are slightly different depending on which way you used to download the game.</p>
- <h3>The installation steps for Google Play Store and APKCombo</h3>
-<p>If you used the Google Play Store to download the game, then you don't need to do anything else after tapping on the install button. The game will be automatically downloaded and installed on your device, and you can launch it from your app drawer or home screen.</p>
-<p>If you used APKCombo to download the game, then you need to follow these steps after downloading the APK file:</p>
-<ol>
-<li>Go to your device settings and enable unknown sources under security or applications.</li>
-<li>Locate the downloaded APK file on your device using a file manager app or your browser's downloads folder.</li>
-<li>Tap on the APK file and follow the instructions on the screen to install it.</li>
-<li>Launch the game from your app drawer or home screen .</li>
-</ol>
-<p>That's it! You have successfully installed Totally Reliable Delivery Service APK on your Android device using either way.</p>
- <h3>The minimum system requirements and compatibility issues</h3>
-<p>Before you start playing Totally Reliable Delivery Service on your Android device, you should make sure that your device meets the minimum system requirements for the game. According to the game's developers, these are the minimum specifications that your device should have:</p>
-<ul>
-<li>Android version: 5.0 or higher</li>
-<li>RAM: 2 GB or more</li>
-<li>Storage: 1 GB or more of free space</li>
-<li>Processor: Quad-core or better</li>
-<li>Graphics: Adreno 530 or better</li>
-</ul>
-<p>If your device does not meet these requirements, you may experience lag, crashes, glitches, or other performance issues while playing the game. You may also encounter compatibility issues with some devices or models, especially if you used APKCombo to download the game. In that case, you may need to update your device's software, change your device's settings, or try a different APK file from another source.</p>
- <h3>The tips and tricks for playing the game and having fun</h3>
-<p>Now that you have installed and launched Totally Reliable Delivery Service on your Android device, you are ready to play and have fun with it. Here are some tips and tricks that will help you enjoy the game more:</p>
-<ul>
-<li>Customize your character's appearance and outfit using the wardrobe in the main menu. You can choose from different hairstyles, clothes, accessories, and colors.</li>
-<li>Explore the map and discover new vehicles, gadgets, and locations. You can use the map in the pause menu to see where you are and where you can go.</li>
-<li>Complete deliveries and earn money and rewards. You can find delivery boxes scattered around the map, or use the delivery app on your phone to accept delivery requests. You can also see your delivery stats and achievements in the pause menu.</li>
-<li>Play with other players online or locally. You can join or host a public or private session using the online menu in the main menu. You can also use the split-screen mode to play with up to four players on the same device.</li>
-<li>Communicate and interact with other players using voice chat or emotes. You can also use your hands or gadgets to grab, push, pull, throw, or slap other players.</li>
-<li>Have fun and be creative with the physics and sandbox elements of the game. You can do anything you want in the game, as long as it is hilarious and chaotic.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Totally Reliable Delivery Service is a hilarious physics-based sandbox game that lets you experience the joy and madness of being a terrible delivery driver. You can play it solo or with up to three other players online or locally, and use various vehicles, gadgets, and distractions to complete deliveries or just mess around. The game is available for Android devices, and you can download it from the Google Play Store or APKCombo. However, you should be aware of the advantages and disadvantages of each way, as well as the minimum system requirements and compatibility issues for the game. We hope that this article has helped you learn more about Totally Reliable Delivery Service APK for Android, and how to download, install, and play it on your device. Now go ahead and try this game for yourself, and have some totally unreliable fun!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most common questions that people ask about Totally Reliable Delivery Service APK for Android:</p>
- <h3>Is Totally Reliable Delivery Service free on Android?</h3>
-<p>No, Totally Reliable Delivery Service is not free on Android. The game costs $4.99 USD on the Google Play Store. However, you can download it for free from APKCombo or other third-party websites that offer APK files.</p>
- <h3>Is Totally Reliable Delivery Service safe on Android?</h3>
-<p>Yes, Totally Reliable Delivery Service is safe on Android if you download it from the Google Play Store, as it is verified by Google and does not contain any malware or viruses. However, if you download it from APKCombo or other third-party websites that offer APK files, you may expose your device to potential security risks or malware infections. Therefore, you should always be careful when downloading APK files from outside sources, and scan them with an antivirus app before installing them.</p>
- <h3>Is Totally Reliable Delivery Service offline on Android?</h3>
-<p>No, Totally Reliable Delivery Service is not offline on Android. The game requires an internet connection to play online multiplayer mode or access some of its online features. However, you can play solo mode offline without an internet connection.</p>
- <h3>How to update Totally Reliable Delivery Service on Android?</ <p>How to update Totally Reliable Delivery Service on Android?</p>
-<p>The way to update Totally Reliable Delivery Service on Android depends on which way you used to download the game. If you used the Google Play Store, then you will get automatic updates for the game whenever they are available. You can also check for updates manually by going to the game's page on the store and tapping on the update button if there is one. If you used APKCombo or other third-party websites that offer APK files, then you will not get automatic updates for the game. You will have to download and install the latest version of the APK file from the website whenever there is a new update.</p>
- <h3>How to uninstall Totally Reliable Delivery Service on Android?</h3>
-<p>The way to uninstall Totally Reliable Delivery Service on Android is the same regardless of which way you used to download the game. You can simply go to your device settings and find the game under applications or apps. Then, tap on the game and select uninstall or remove. You can also long-press on the game's icon on your home screen or app drawer and drag it to the uninstall or remove option.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/utils/matlab_functions.py b/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/utils/matlab_functions.py
deleted file mode 100644
index c6ce1004a2c9f8521505c4b5889d3c24a909c70d..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/utils/matlab_functions.py
+++ /dev/null
@@ -1,347 +0,0 @@
-import math
-import numpy as np
-import torch
-
-
-def cubic(x):
-    """cubic function used for calculate_weights_indices."""
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5 * absx3 - 2.5 * absx2 + 1) * (
-        (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (((absx > 1) *
-                                                                                     (absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    """Calculate weights and indices, used for imresize function.
-
-    Args:
-        in_length (int): Input length.
-        out_length (int): Output length.
-        scale (float): Scale factor.
-        kernel_width (int): Kernel width.
-        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
-    """
-
-    if (scale < 1) and antialiasing:
-        # Use a modified kernel (larger kernel width) to simultaneously
-        # interpolate and antialias
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5 + scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    p = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand(
-        out_length, p)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices
-
-    # apply cubic kernel
-    if (scale < 1) and antialiasing:
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, p)
-
-    # If a column in weights is all zero, get rid of it. only consider the
-    # first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, p - 2)
-        weights = weights.narrow(1, 1, p - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, p - 2)
-        weights = weights.narrow(1, 0, p - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-@torch.no_grad()
-def imresize(img, scale, antialiasing=True):
-    """imresize function same as MATLAB.
-
-    It now only supports bicubic.
-    The same scale applies for both height and width.
-
-    Args:
-        img (Tensor | Numpy array):
-            Tensor: Input image with shape (c, h, w), [0, 1] range.
-            Numpy: Input image with shape (h, w, c), [0, 1] range.
-        scale (float): Scale factor. The same scale applies for both height
-            and width.
-        antialisaing (bool): Whether to apply anti-aliasing when downsampling.
-            Default: True.
-
-    Returns:
-        Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round.
-    """
-    if type(img).__module__ == np.__name__:  # numpy type
-        numpy_type = True
-        img = torch.from_numpy(img.transpose(2, 0, 1)).float()
-    else:
-        numpy_type = False
-
-    in_c, in_h, in_w = img.size()
-    out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # get weights and indices
-    weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(in_h, out_h, scale, kernel, kernel_width,
-                                                                             antialiasing)
-    weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(in_w, out_w, scale, kernel, kernel_width,
-                                                                             antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w)
-    img_aug.narrow(1, sym_len_hs, in_h).copy_(img)
-
-    sym_patch = img[:, :sym_len_hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_he:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_c, out_h, in_w)
-    kernel_width = weights_h.size(1)
-    for i in range(out_h):
-        idx = int(indices_h[i][0])
-        for j in range(in_c):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we)
-    out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_we:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_c, out_h, out_w)
-    kernel_width = weights_w.size(1)
-    for i in range(out_w):
-        idx = int(indices_w[i][0])
-        for j in range(in_c):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i])
-
-    if numpy_type:
-        out_2 = out_2.numpy().transpose(1, 2, 0)
-    return out_2
-
-
-def rgb2ycbcr(img, y_only=False):
-    """Convert a RGB image to YCbCr image.
-
-    This function produces the same results as Matlab's `rgb2ycbcr` function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def bgr2ycbcr(img, y_only=False):
-    """Convert a BGR image to YCbCr image.
-
-    The bgr version of rgb2ycbcr.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2rgb(img):
-    """Convert a YCbCr image to RGB image.
-
-    This function produces the same results as Matlab's ycbcr2rgb function.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted RGB image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                              [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]  # noqa: E126
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def ycbcr2bgr(img):
-    """Convert a YCbCr image to BGR image.
-
-    The bgr version of ycbcr2rgb.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        ndarray: The converted BGR image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img) * 255
-    out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0],
-                              [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921]  # noqa: E126
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
-
-
-def _convert_input_type_range(img):
-    """Convert the type and range of the input image.
-
-    It converts the input image to np.float32 type and range of [0, 1].
-    It is mainly used for pre-processing the input image in colorspace
-    convertion functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with type of np.float32 and range of
-            [0, 1].
-    """
-    img_type = img.dtype
-    img = img.astype(np.float32)
-    if img_type == np.float32:
-        pass
-    elif img_type == np.uint8:
-        img /= 255.
-    else:
-        raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}')
-    return img
-
-
-def _convert_output_type_range(img, dst_type):
-    """Convert the type and range of the image according to dst_type.
-
-    It converts the image to desired type and range. If `dst_type` is np.uint8,
-    images will be converted to np.uint8 type with range [0, 255]. If
-    `dst_type` is np.float32, it converts the image to np.float32 type with
-    range [0, 1].
-    It is mainly used for post-processing images in colorspace convertion
-    functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The image to be converted with np.float32 type and
-            range [0, 255].
-        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
-            converts the image to np.uint8 type with range [0, 255]. If
-            dst_type is np.float32, it converts the image to np.float32 type
-            with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with desired type and range.
-    """
-    if dst_type not in (np.uint8, np.float32):
-        raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}')
-    if dst_type == np.uint8:
-        img = img.round()
-    else:
-        img /= 255.
-    return img.astype(dst_type)
diff --git a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/static/index.js b/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/static/index.js
deleted file mode 100644
index 60d35e78bf9e280f9884eb95d7df3ee4ff61bbeb..0000000000000000000000000000000000000000
--- a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/static/index.js
+++ /dev/null
@@ -1,273 +0,0 @@
-
-
-let cvsIn = document.getElementById("inputimg");
-let ctxIn = cvsIn.getContext('2d');
-let style = document.getElementById("style");
-let svgGraph = null;
-let mouselbtn = false;
-var current_time = (new Date()).getTime();
-var user_id = Math.floor(Math.random() * 1000000000);
-var infer_interval = null;
-
-
-function setInferInterval() {
-    // call 'check_gpu' function to check if GPU is available
-    $.ajax({
-        url: './check_gpu',
-        type: 'GET',
-        contentType: 'application/json',
-    }).done(function (data) {
-        console.log(data);
-        if (data == false) {
-            console.log("GPU is not available. Use CPU for inference.");
-            infer_interval = 5000;
-        }
-        else {
-            console.log("GPU is available. Use GPU for inference.");
-            infer_interval = 300;
-        }
-    }).fail(function (XMLHttpRequest, textStatus, errorThrown) {
-        console.log(XMLHttpRequest);
-        alert("error");
-    })
-}
-
-
-// initilize
-window.onload = function () {
-    ctxIn.fillStyle = "#87ceeb";
-    ctxIn.fillRect(0, 0, cvsIn.width, 300);
-    ctxIn.fillStyle = "#567d46";
-    ctxIn.fillRect(0, 300, cvsIn.width, 512);
-
-    ctxIn.color = "#b0d49b";
-    ctxIn.lineWidth = 30;
-    ctxIn.lineJoin = ctxIn.lineCap = 'round';
-    setInferInterval();
-}
-
-
-// add cavas events
-cvsIn.addEventListener("mousedown", function (e) {
-    if (e.button == 0) {
-        let rect = e.target.getBoundingClientRect();
-        let x = e.clientX - rect.left;
-        let y = e.clientY - rect.top;
-        mouselbtn = true;
-        ctxIn.beginPath();
-        ctxIn.moveTo(x, y);
-    }
-    else if (e.button == 2) {
-        onClear();  // right click for clear input
-    }
-});
-
-cvsIn.addEventListener("mouseup", function (e) {
-    if (e.button == 0) {
-        mouselbtn = false;
-        move_range = domainSlider.value;
-        onRecognition(move_range);
-    }
-});
-cvsIn.addEventListener("mousemove", function (e) {
-    let rect = e.target.getBoundingClientRect();
-    let x = e.clientX - rect.left;
-    let y = e.clientY - rect.top;
-    if (mouselbtn) {
-        ctxIn.lineTo(x, y);
-        ctxIn.strokeStyle = ctxIn.color;
-        ctxIn.stroke();
-        if (((new Date).getTime() - current_time) >= infer_interval) {
-            move_range = domainSlider.value;
-            onRecognition(move_range);
-            current_time = (new Date).getTime();
-        }
-
-    }
-});
-
-cvsIn.addEventListener("touchstart", function (e) {
-    // for touch device
-    if (e.targetTouches.length == 1) {
-        let rect = e.target.getBoundingClientRect();
-        let touch = e.targetTouches[0];
-        let x = touch.clientX - rect.left;
-        let y = touch.clientY - rect.top;
-        ctxIn.beginPath();
-        ctxIn.moveTo(x, y);
-    }
-});
-
-cvsIn.addEventListener("touchmove", function (e) {
-    // for touch device
-    if (e.targetTouches.length == 1) {
-        let rect = e.target.getBoundingClientRect();
-        let touch = e.targetTouches[0];
-        let x = touch.clientX - rect.left;
-        let y = touch.clientY - rect.top;
-        ctxIn.lineTo(x, y);
-        ctxIn.strokeStyle = ctxIn.color;
-        ctxIn.stroke();
-        e.preventDefault();
-    }
-});
-
-cvsIn.addEventListener("touchend", function (e) {
-    // for touch device
-    move_range = domainSlider.value;
-    onRecognition(move_range);
-});
-
-// prevent display the contextmenu
-cvsIn.addEventListener('contextmenu', function (e) {
-    e.preventDefault();
-});
-
-document.getElementById("clearbtn").onclick = onClear;
-function onClear() {
-    mouselbtn = false;
-    ctxIn.clearRect(0, 0, 512, 512);
-    ctxIn.fillStyle = "#87ceeb";
-    ctxIn.fillRect(0, 0, cvsIn.width, 300);
-    ctxIn.fillStyle = "#567d46";
-    ctxIn.fillRect(0, 300, cvsIn.width, 512);
-}
-
-
-document.getElementById("random_pick").addEventListener("click", function () {
-    //ctxIn.color = "#F5F5F5";
-    onRecognition_random();
-});
-
-
-document.getElementById("color1").addEventListener("click", function () {
-    //ctxIn.color = "#D5D5D5";
-    ctxIn.color = "#87ceeb";
-});
-document.getElementById("color2").addEventListener("click", function () {
-    //ctxIn.color = "#696969";
-    ctxIn.color = "#9b7653"
-});
-document.getElementById("color3").addEventListener("click", function () {
-    //ctxIn.color = "#676767";
-    ctxIn.color = "#b0d49b"
-});
-document.getElementById("color4").addEventListener("click", function () {
-    //ctxIn.color = "#F5F5F5";
-    ctxIn.color = "#5abcd8"
-});
-document.getElementById("color5").addEventListener("click", function () {
-    //ctxIn.color = "#F5F5F5";
-    ctxIn.color = "#C1BEBA"
-});
-document.getElementById("color6").addEventListener("click", function () {
-    ctxIn.color = "#5A4D41"
-});
-document.getElementById("color7").addEventListener("click", function () {
-    ctxIn.color = "#567d46"
-});
-document.getElementById("color8").addEventListener("click", function () {
-    ctxIn.color = "#42692f"
-});
-
-document.getElementById("color9").addEventListener("click", function () {
-    ctxIn.color = "#1577be"
-});
-//document.getElementById("color10").addEventListener("click", function(){
-//ctxIn.color = "#676767";
-//    ctxIn.color = "#808080"
-//});
-document.getElementById("color11").addEventListener("click", function () {
-    //ctxIn.color = "#F5F5F5";
-    ctxIn.color = "#3a2e27"
-});
-document.getElementById("color12").addEventListener("click", function () {
-    //ctxIn.color = "#F5F5F5";
-    ctxIn.color = "#4D415A"
-});
-//document.getElementById("color13").addEventListener("click", function(){
-//    ctxIn.color = "#74cc8c"
-//});
-document.getElementById("color14").addEventListener("click", function () {
-    ctxIn.color = "#FDDA16"
-});
-document.getElementById("color15").addEventListener("click", function () {
-    ctxIn.color = "#d0cccc"
-});
-
-var brushSlider = document.getElementById("brushSlider");
-ctxIn.lineWidth = brushSlider.value;
-
-brushSlider.addEventListener("change", function () {
-    ctxIn.lineWidth = brushSlider.value;
-});
-
-
-
-
-var move_range = 3;//domainSlider_1.value;
-
-
-document.getElementById('style').addEventListener('change', function (event) {
-    domainSlider.value = 3;
-    onRecognition(domainSlider.value);
-})
-
-
-domainSlider.addEventListener("change", function () {
-    // style.value = "ink";
-    move_range = domainSlider.value;
-    onRecognition(move_range);
-});
-
-
-// post data to server for recognition
-function onRecognition(range) {
-    $.ajax({
-        url: './predict',
-        type: 'POST',
-        data: JSON.stringify({
-            img: cvsIn.toDataURL("image/png").replace('data:image/png;base64,', ''),
-            model: style.value,
-            move_range: range,
-            user_id: user_id
-        }),
-        contentType: 'application/json',
-    }).done(function (data) {
-        drawImgToCanvas("outputimg", data)
-
-    }).fail(function (XMLHttpRequest, textStatus, errorThrown) {
-        console.log(XMLHttpRequest);
-        alert("error");
-    })
-}
-
-function onRecognition_random(range) {
-    $.ajax({
-        url: './predict_random',
-        type: 'POST',
-        data: JSON.stringify({
-            img: cvsIn.toDataURL("image/png").replace('data:image/png;base64,', ''),
-            model: style.value,
-            move_range: range,
-            user_id: user_id
-        }),
-        contentType: 'application/json',
-    }).done(function (data) {
-        drawImgToCanvas("outputimg", data)
-
-    }).fail(function (XMLHttpRequest, textStatus, errorThrown) {
-        console.log(XMLHttpRequest);
-        alert("error");
-    })
-}
-
-function drawImgToCanvas(canvasId, b64Img) {
-    let canvas = document.getElementById(canvasId);
-    let ctx = canvas.getContext('2d');
-    let img = new Image();
-    img.src = "data:image/png;base64," + b64Img;
-    img.onload = function () {
-        ctx.drawImage(img, 0, 0, img.width, img.height, 0, 0, canvas.width, canvas.height);
-    }
-}
diff --git a/spaces/society-ethics/disaggregators/prep_data.py b/spaces/society-ethics/disaggregators/prep_data.py
deleted file mode 100644
index 169cac8b8ae5bbf46743e279c5cb4e9e1ad84485..0000000000000000000000000000000000000000
--- a/spaces/society-ethics/disaggregators/prep_data.py
+++ /dev/null
@@ -1,151 +0,0 @@
-from datasets import load_dataset
-from disaggregators import Disaggregator, DisaggregationModuleLabels, CustomDisaggregator
-from disaggregators.disaggregation_modules.age import Age, AgeLabels, AgeConfig
-
-import matplotlib
-matplotlib.use('TKAgg')
-
-import joblib
-import os
-
-cache_file = "cached_data.pkl"
-cache_dict = {}
-
-if os.path.exists(cache_file):
-    cache_dict = joblib.load("cached_data.pkl")
-
-class MeSHAgeLabels(AgeLabels):
-    INFANT = "infant"
-    CHILD_PRESCHOOL = "child_preschool"
-    CHILD = "child"
-    ADOLESCENT = "adolescent"
-    ADULT = "adult"
-    MIDDLE_AGED = "middle_aged"
-    AGED = "aged"
-    AGED_80_OVER = "aged_80_over"
-
-
-age = Age(
-    config=AgeConfig(
-        labels=MeSHAgeLabels,
-        ages=[list(MeSHAgeLabels)],
-        breakpoints=[0, 2, 5, 12, 18, 44, 64, 79]
-    ),
-    column="question"
-)
-
-
-class TabsSpacesLabels(DisaggregationModuleLabels):
-    TABS = "tabs"
-    SPACES = "spaces"
-
-
-class TabsSpaces(CustomDisaggregator):
-    module_id = "tabs_spaces"
-    labels = TabsSpacesLabels
-
-    def __call__(self, row, *args, **kwargs):
-        if "\t" in row[self.column]:
-            return {self.labels.TABS: True, self.labels.SPACES: False}
-        else:
-            return {self.labels.TABS: False, self.labels.SPACES: True}
-
-
-class ReactComponentLabels(DisaggregationModuleLabels):
-    CLASS = "class"
-    FUNCTION = "function"
-
-
-class ReactComponent(CustomDisaggregator):
-    module_id = "react_component"
-    labels = ReactComponentLabels
-
-    def __call__(self, row, *args, **kwargs):
-        if "extends React.Component" in row[self.column] or "extends Component" in row[self.column]:
-            return {self.labels.CLASS: True, self.labels.FUNCTION: False}
-        else:
-            return {self.labels.CLASS: False, self.labels.FUNCTION: True}
-
-
-configs = {
-    "laion": {
-        "disaggregation_modules": ["continent"],
-        "dataset_name": "society-ethics/laion2B-en_continents",
-        "column": "TEXT",
-        "feature_names": {
-            "continent.africa": "Africa",
-            "continent.americas": "Americas",
-            "continent.asia": "Asia",
-            "continent.europe": "Europe",
-            "continent.oceania": "Oceania",
-
-            # Parent level
-            "continent": "Continent",
-        }
-    },
-    "medmcqa": {
-        "disaggregation_modules": [age, "gender"],
-        "dataset_name": "society-ethics/medmcqa_age_gender_custom",
-        "column": "question",
-        "feature_names": {
-            "age.infant": "Infant",
-            "age.child_preschool": "Preschool",
-            "age.child": "Child",
-            "age.adolescent": "Adolescent",
-            "age.adult": "Adult",
-            "age.middle_aged": "Middle Aged",
-            "age.aged": "Aged",
-            "age.aged_80_over": "Aged 80+",
-            "gender.male": "Male",
-            "gender.female": "Female",
-
-            # Parent level
-            "gender": "Gender",
-            "age": "Age",
-            "Both": "Age + Gender",
-        }
-    },
-    "stack": {
-        "disaggregation_modules": [TabsSpaces, ReactComponent],
-        "dataset_name": "society-ethics/the-stack-tabs_spaces",
-        "column": "content",
-        "feature_names": {
-            "react_component.class": "Class",
-            "react_component.function": "Function",
-            "tabs_spaces.tabs": "Tabs",
-            "tabs_spaces.spaces": "Spaces",
-
-            # Parent level
-            "react_component": "React Component Syntax",
-            "tabs_spaces": "Tabs vs. Spaces",
-            "Both": "React Component Syntax + Tabs vs. Spaces",
-        }
-    }
-}
-
-
-def generate_cached_data(disaggregation_modules, dataset_name, column, feature_names):
-    disaggregator = Disaggregator(disaggregation_modules, column=column)
-    ds = load_dataset(dataset_name, split="train")
-    df = ds.to_pandas()
-
-    all_fields = {*disaggregator.fields, "None"}
-    distributions = df[sorted(list(disaggregator.fields))].value_counts()
-
-    return {
-        "fields": all_fields,
-        "data_fields": disaggregator.fields,
-        "distributions": distributions,
-        "disaggregators": [module.name for module in disaggregator.modules],
-        "column": column,
-        "feature_names": feature_names,
-    }
-
-
-cache_dict.update({
-    "laion": generate_cached_data(**configs["laion"]),
-    "medmcqa": generate_cached_data(**configs["medmcqa"]),
-    "stack": generate_cached_data(**configs["stack"])
-})
-
-joblib.dump(cache_dict, cache_file)
diff --git a/spaces/spuun/AI-image-detector/README.md b/spaces/spuun/AI-image-detector/README.md
deleted file mode 100644
index 942d5b1e2ba0e619292ce9b354a6ffc09f719c0c..0000000000000000000000000000000000000000
--- a/spaces/spuun/AI-image-detector/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Image Detector
-emoji: 🚀
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-duplicated_from: umm-maybe/AI-image-detector
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/spuun/nsfw-det/README.md b/spaces/spuun/nsfw-det/README.md
deleted file mode 100644
index 1f7ef2d42a012a950a5001d8584d5f8bb08f8697..0000000000000000000000000000000000000000
--- a/spaces/spuun/nsfw-det/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Nsfw Det
-emoji: 🏢
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/roberta/model_camembert.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/roberta/model_camembert.py
deleted file mode 100644
index 46447546fafb4a0a887b481022cac07631047c80..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/roberta/model_camembert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-CamemBERT: a Tasty French Language Model
-"""
-
-from fairseq.models import register_model
-
-from .hub_interface import RobertaHubInterface
-from .model import RobertaModel
-
-
-@register_model("camembert")
-class CamembertModel(RobertaModel):
-    @classmethod
-    def hub_models(cls):
-        return {
-            "camembert": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz",
-            "camembert.v0": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz",
-            "camembert-base": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz",
-            "camembert-large": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz",
-            "camembert-base-ccnet": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz",
-            "camembert-base-ccnet-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz",
-            "camembert-base-wikipedia-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz",
-            "camembert-base-oscar-4gb": "http://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz",
-        }
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_name_or_path,
-        checkpoint_file="model.pt",
-        data_name_or_path=".",
-        bpe="sentencepiece",
-        **kwargs
-    ):
-        from fairseq import hub_utils
-
-        x = hub_utils.from_pretrained(
-            model_name_or_path,
-            checkpoint_file,
-            data_name_or_path,
-            archive_map=cls.hub_models(),
-            bpe=bpe,
-            load_checkpoint_heads=True,
-            **kwargs,
-        )
-        return RobertaHubInterface(x["args"], x["task"], x["models"][0])
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/quantization/pq/em.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/quantization/pq/em.py
deleted file mode 100644
index 6f15c3e46bd052b1e00929e7ece9355fb03846c7..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/quantization/pq/em.py
+++ /dev/null
@@ -1,211 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import random
-from collections import Counter
-
-import torch
-
-
-class EM:
-    """
-    EM algorithm used to quantize the columns of W to minimize
-
-                         ||W - W_hat||^2
-
-    Args:
-        - W: weight matrix of size (in_features x out_features)
-        - n_iter: number of k-means iterations
-        - n_centroids: number of centroids (size of codebook)
-        - eps: for cluster reassignment when an empty cluster is found
-        - max_tentatives for cluster reassignment when an empty cluster is found
-        - verbose: print error after each iteration
-
-    Remarks:
-        - If one cluster is empty, the most populated cluster is split into
-          two clusters
-        - All the relevant dimensions are specified in the code
-    """
-
-    def __init__(
-        self, W, n_centroids=256, n_iter=20, eps=1e-6, max_tentatives=30, verbose=True
-    ):
-        self.W = W
-        self.n_centroids = n_centroids
-        self.n_iter = n_iter
-        self.eps = eps
-        self.max_tentatives = max_tentatives
-        self.verbose = verbose
-        self.centroids = torch.Tensor()
-        self.assignments = torch.Tensor()
-        self.objective = []
-
-    def initialize_centroids(self):
-        """
-        Initializes the centroids by sampling random columns from W.
-        """
-
-        in_features, out_features = self.W.size()
-        indices = torch.randint(
-            low=0, high=out_features, size=(self.n_centroids,)
-        ).long()
-        self.centroids = self.W[:, indices].t()  # (n_centroids x in_features)
-
-    def step(self, i):
-        """
-        There are two standard steps for each iteration: expectation (E) and
-        minimization (M). The E-step (assignment) is performed with an exhaustive
-        search and the M-step (centroid computation) is performed with
-        the exact solution.
-
-        Args:
-            - i: step number
-
-        Remarks:
-            - The E-step heavily uses PyTorch broadcasting to speed up computations
-              and reduce the memory overhead
-        """
-
-        # assignments (E-step)
-        distances = self.compute_distances()  # (n_centroids x out_features)
-        self.assignments = torch.argmin(distances, dim=0)  # (out_features)
-        n_empty_clusters = self.resolve_empty_clusters()
-
-        # centroids (M-step)
-        for k in range(self.n_centroids):
-            W_k = self.W[:, self.assignments == k]  # (in_features x size_of_cluster_k)
-            self.centroids[k] = W_k.mean(dim=1)  # (in_features)
-
-        # book-keeping
-        obj = (self.centroids[self.assignments].t() - self.W).norm(p=2).item()
-        self.objective.append(obj)
-        if self.verbose:
-            logging.info(
-                f"Iteration: {i},\t"
-                f"objective: {obj:.6f},\t"
-                f"resolved empty clusters: {n_empty_clusters}"
-            )
-
-    def resolve_empty_clusters(self):
-        """
-        If one cluster is empty, the most populated cluster is split into
-        two clusters by shifting the respective centroids. This is done
-        iteratively for a fixed number of tentatives.
-        """
-
-        # empty clusters
-        counts = Counter(map(lambda x: x.item(), self.assignments))
-        empty_clusters = set(range(self.n_centroids)) - set(counts.keys())
-        n_empty_clusters = len(empty_clusters)
-
-        tentatives = 0
-        while len(empty_clusters) > 0:
-            # given an empty cluster, find most populated cluster and split it into two
-            k = random.choice(list(empty_clusters))
-            m = counts.most_common(1)[0][0]
-            e = torch.randn_like(self.centroids[m]) * self.eps
-            self.centroids[k] = self.centroids[m].clone()
-            self.centroids[k] += e
-            self.centroids[m] -= e
-
-            # recompute assignments
-            distances = self.compute_distances()  # (n_centroids x out_features)
-            self.assignments = torch.argmin(distances, dim=0)  # (out_features)
-
-            # check for empty clusters
-            counts = Counter(map(lambda x: x.item(), self.assignments))
-            empty_clusters = set(range(self.n_centroids)) - set(counts.keys())
-
-            # increment tentatives
-            if tentatives == self.max_tentatives:
-                logging.info(
-                    f"Could not resolve all empty clusters, {len(empty_clusters)} remaining"
-                )
-                raise EmptyClusterResolveError
-            tentatives += 1
-
-        return n_empty_clusters
-
-    def compute_distances(self):
-        """
-        For every centroid m, computes
-
-                          ||M - m[None, :]||_2
-
-        Remarks:
-            - We rely on PyTorch's broadcasting to speed up computations
-              and reduce the memory overhead
-            - Without chunking, the sizes in the broadcasting are modified as:
-              (n_centroids x n_samples x out_features) -> (n_centroids x out_features)
-            - The broadcasting computation is automatically chunked so that
-              the tensors fit into the memory of the GPU
-        """
-
-        nb_centroids_chunks = 1
-
-        while True:
-            try:
-                return torch.cat(
-                    [
-                        (self.W[None, :, :] - centroids_c[:, :, None]).norm(p=2, dim=1)
-                        for centroids_c in self.centroids.chunk(
-                            nb_centroids_chunks, dim=0
-                        )
-                    ],
-                    dim=0,
-                )
-            except RuntimeError:
-                nb_centroids_chunks *= 2
-
-    def assign(self):
-        """
-        Assigns each column of W to its closest centroid, thus essentially
-        performing the E-step in train().
-
-        Remarks:
-            - The function must be called after train() or after loading
-              centroids using self.load(), otherwise it will return empty tensors
-        """
-
-        distances = self.compute_distances()  # (n_centroids x out_features)
-        self.assignments = torch.argmin(distances, dim=0)  # (out_features)
-
-    def save(self, path, layer):
-        """
-        Saves centroids and assignments.
-
-        Args:
-            - path: folder used to save centroids and assignments
-        """
-
-        torch.save(self.centroids, os.path.join(path, "{}_centroids.pth".format(layer)))
-        torch.save(
-            self.assignments, os.path.join(path, "{}_assignments.pth".format(layer))
-        )
-        torch.save(self.objective, os.path.join(path, "{}_objective.pth".format(layer)))
-
-    def load(self, path, layer):
-        """
-        Loads centroids and assignments from a given path
-
-        Args:
-            - path: folder use to load centroids and assignments
-        """
-
-        self.centroids = torch.load(
-            os.path.join(path, "{}_centroids.pth".format(layer))
-        )
-        self.assignments = torch.load(
-            os.path.join(path, "{}_assignments.pth".format(layer))
-        )
-        self.objective = torch.load(
-            os.path.join(path, "{}_objective.pth".format(layer))
-        )
-
-
-class EmptyClusterResolveError(Exception):
-    pass
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Colos Create Professional 5.0 Keygen !FREE!.rarl.md b/spaces/stomexserde/gpt4-ui/Examples/Colos Create Professional 5.0 Keygen !FREE!.rarl.md
deleted file mode 100644
index e82438bc2c6d7af5a982a26836271fff574de83f..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Colos Create Professional 5.0 Keygen !FREE!.rarl.md	
+++ /dev/null
@@ -1,123 +0,0 @@
-
-<h1>Colos Create Professional 5.0 Keygen.rarl: What Is It and How to Use It?</h1>
- <p>If you are looking for a software solution that can help you with message design and management for your coding and printing needs, you may have come across Colos Create Professional 5.0. This software product is developed by Markem-Imaje, a global leader in industrial coding solutions. But what is Colos Create Professional 5.0, and how can you use it? And what is a keygen.rarl file that claims to provide you with a free activation code for this software? In this article, we will answer these questions and more, as we explore the features, benefits, risks, drawbacks, and alternatives of using a keygen for Colos Create Professional 5.0.</p>
- <h2>Introduction</h2>
- <p>Before we dive into the details of using a keygen for Colos Create Professional 5.0, let's first understand what this software product is, what a keygen is, what a .rar file is, and why someone would need a keygen for this software.</p>
-<h2>Colos Create Professional 5.0 Keygen.rarl</h2><br /><p><b><b>DOWNLOAD</b> &bull;&bull;&bull; <a href="https://urlgoal.com/2uI8Sd">https://urlgoal.com/2uI8Sd</a></b></p><br /><br />
- <h3>What is Colos Create Professional 5.0?</h3>
- <p>Colos Create Professional 5.0 is a software product that allows you to create and manage messages for various coding and printing technologies, such as inkjet printers, laser coders, thermal transfer printers, print and apply systems, etc. With this software, you can design messages using text, graphics, barcodes, logos, etc., and apply them to different types of products and packages. You can also manage your messages centrally, store them in a database, edit them easily, and distribute them to multiple devices across your network.</p>
- <h3>What is a keygen?</h3>
- <p>A keygen is a software program that generates an activation code or serial number for another software product, usually without the authorization of the original developer or vendor. A keygen is often used by people who want to use a software product without paying for it or obtaining a legitimate license from the source.</p>
- <h3>What is a .rar file?</h3>
- <p>A .rar file is a compressed file format that can contain one or more files or folders inside it. A .rar file can reduce the size of the original files or folders by using compression algorithms, making it easier to store or transfer them over the internet or other media. A .rar file can be opened or extracted by using a software program that supports the .rar format, such as WinRAR, 7-Zip, etc.</p>
-<p></p>
- <h3>Why would someone need a keygen for Colos Create Professional 5.0?</h3>
- <p>Someone who wants to use Colos Create Professional 5.0 without paying for it or obtaining a legitimate license from Markem-Imaje may resort to using a keygen for this software. A keygen for Colos Create Professional 5.0 may claim to provide a free activation code or serial number that can bypass the software's security and verification system, and allow the user to install and use the software without any restrictions or limitations.</p>
- <h2>Features and Benefits of Colos Create Professional 5.0</h2>
- <p>Now that we have a basic understanding of what Colos Create Professional 5.0 is and what a keygen is, let's take a look at some of the features and benefits of this software product, and why it may be useful for your coding and printing needs.</p>
- <h3>How Colos Create Professional 5.0 helps with message design and management</h3>
- <p>One of the main features of Colos Create Professional 5.0 is that it allows you to create and manage messages for various coding and printing technologies, such as inkjet printers, laser coders, thermal transfer printers, print and apply systems, etc. With this software, you can design messages using text, graphics, barcodes, logos, etc., and apply them to different types of products and packages. You can also customize your messages according to your specific requirements, such as font size, color, alignment, orientation, rotation, etc.</p>
- <p>Another feature of Colos Create Professional 5.0 is that it allows you to manage your messages centrally, store them in a database, edit them easily, and distribute them to multiple devices across your network. This way, you can ensure consistency and accuracy of your messages across your production lines, reduce errors and rework, and save time and resources.</p>
- <h3>How Colos Create Professional 5.0 supports various coding and printing technologies</h3>
- <p>Another feature of Colos Create Professional 5.0 is that it supports various coding and printing technologies, such as inkjet printers, laser coders, thermal transfer printers, print and apply systems, etc. This means that you can use this software with different types of devices from Markem-Imaje or other vendors, and create messages that are compatible with different types of substrates, such as paper, plastic, metal, glass, etc. You can also use this software with different types of coding standards, such as GS1 DataMatrix, QR Code, EAN-13, etc.</p>
- <h3>How Colos Create Professional 5.0 enhances productivity and quality</h3>
- <p>Another benefit of Colos Create Professional 5.0 is that it enhances productivity and quality of your coding and printing operations. By using this software, you can create and manage messages faster and easier than using manual methods or other software products. You can also reduce errors and rework by ensuring consistency and accuracy of your messages across your production lines. You can also improve quality by creating messages that are clear, legible, durable, and compliant with industry standards and regulations.</p>
- <h2>Risks and Drawbacks of Using a Keygen for Colos Create Professional 5.0</h2>
- <p>While Colos Create Professional 5.0 is a useful software product that offers many features and benefits for your coding and printing needs, using a keygen for this software is not a good idea, as it involves many risks and drawbacks that can outweigh the benefits. Here are some of the reasons why you should avoid using a keygen for Colos Create Professional 5.0.</p>
- <h3>How a keygen can be illegal and unethical</h3>
- <p>One of the main risks of using a keygen for Colos Create Professional 5.0 is that it can be illegal and unethical. By using a keygen, you are violating the intellectual property rights of Markem-Imaje, the developer and vendor of this software product. You are also depriving them of the revenue that they deserve for creating and maintaining this software product. This can have negative consequences for both you and them, such as legal actions, fines, penalties, lawsuits, etc. You are also acting unethically by using a software product that you have not paid for or obtained a legitimate license for, which can damage your reputation and credibility.</p>
- <h3>How a keygen can be unsafe and harmful</h3>
- <p>Another risk of using a keygen for Colos Create Professional 5.0 is that it can be unsafe and harmful. By using a keygen, you are exposing your computer and network to potential threats, such as viruses, malware, spyware, ransomware, etc. These threats can infect your system, corrupt your files, steal your data, damage your hardware, etc. They can also compromise your security and privacy, by accessing your personal or confidential information, such as passwords, bank accounts, credit cards, etc. They can also cause problems for your coding and printing operations, by interfering with the performance and functionality of your devices and software products.</p>
- <h3>How a keygen can be ineffective and unreliable</h3>
- <p>Another drawback of using a keygen for Colos Create Professional 5.0 is that it can be ineffective and unreliable. By using a keygen, you are not guaranteed to get a working activation code or serial number for this software product. A keygen may generate an invalid or expired code or number that can be rejected by the software's security and verification system. A keygen may also generate a code or number that has already been used by someone else, which can cause conflicts and errors. A keygen may also fail to generate a code or number at all, which can leave you with no access to the software product.</p>
- <h2>Alternatives to Using a Keygen for Colos Create Professional 5.0</h2>
- <p>Given the risks and drawbacks of using a keygen for Colos Create Professional 5.0, you may be wondering what are some alternatives to using a keygen for this software product. Here are some of the options that you can consider instead of using a keygen for Colos Create Professional 5.0.</p>
- <h3>How to purchase a legitimate license for Colos Create Professional 5.0</h3>
- <p>The best alternative to using a keygen for Colos Create Professional 5.0 is to purchase a legitimate license for this software product from Markem-Imaje or their authorized distributors or resellers. By purchasing a legitimate license, you can enjoy the full features and benefits of this software product without any restrictions or limitations. You can also avoid the legal and ethical issues that come with using a keygen. You can also ensure the safety and security of your computer and network, as well as the quality and reliability of your coding and printing operations.</p>
- <p>To purchase a legitimate license for Colos Create Professional 5.0, you can visit the official website of Markem-Imaje at https://www.markem-imaje.com/products/software/colos-create-professional, where you can find more information about this software product, such as its features, benefits, specifications, system requirements, etc. You can also contact their sales team or find a local distributor or reseller near you, where you can request a quote, place an order, or get technical support.</p>
- <h3>How to use a free trial version of Colos Create Professional 5.0</h3>
- <p>Another alternative to using a keygen for Colos Create Professional 5.0 is to use a free trial version of this software product from Markem-Imaje. By using a free trial version, you can test and evaluate the features and benefits of this software product before deciding whether to purchase a legitimate license or not. You can also avoid the risks and drawbacks of using a keygen, as you are using a legal and ethical way of accessing the software product.</p>
- <p>To use a free trial version of Colos Create Professional 5.0, you can visit the official website of Markem-Imaje at https://www.markem-imaje.com/products/software/colos-create-professional, where you can find a link to download the trial version of this software product. You can also contact their sales team or find a local distributor or reseller near you, where you can request a trial version of this software product. The trial version of Colos Create Professional 5.0 is valid for 30 days, after which you will need to purchase a legitimate license or uninstall the software product.</p>
- <h3>How to use other similar software products</h3>
- <p>Another alternative to using a keygen for Colos Create Professional 5.0 is to use other similar software products that can help you with message design and management for your coding and printing needs. There are many other software products available in the market that offer similar or different features and benefits as Colos Create Professional 5.0, and that may suit your specific requirements, preferences, or budget better than Colos Create Professional 5.0.</p>
- <p>To use other similar software products, you can do some research online or offline, and compare the features, benefits, prices, reviews, ratings, etc. of different software products that can help you with message design and management for your coding and printing needs. You can also ask for recommendations from your peers, colleagues, friends, or family who have used or are using similar software products. You can also contact the developers or vendors of these software products and request more information, demos, trials, quotes, etc.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Colos Create Professional 5.0 is a software product that allows you to create and manage messages for various coding and printing technologies, such as inkjet printers, laser coders, thermal transfer printers, print and apply systems, etc. It offers many features and benefits for your coding and printing needs, such as message design, message management, coding support, printing support, productivity enhancement, quality improvement, etc.</p>
- <p>However, using a keygen for Colos Create Professional 5.0 is not a good idea, as it involves many risks and drawbacks that can outweigh the benefits. Using a keygen can be illegal and unethical, as it violates the intellectual property rights of Markem-Imaje and deprives them of the revenue they deserve. Using a keygen can also be unsafe and harmful, as it exposes your computer and network to potential threats, such as viruses, malware, spyware, ransomware, etc. Using a keygen can also be ineffective and unreliable, as it may not generate a working activation code or serial number for this software product, or may cause conflicts and errors with the software's security and verification system.</p>
- <p>Therefore, instead of using a keygen for Colos Create Professional 5.0, you should consider some alternatives, such as purchasing a legitimate license for this software product from Markem-Imaje or their authorized distributors or resellers, using a free trial version of this software product from Markem-Imaje, or using other similar software products that can help you with message design and management for your coding and printing needs. These alternatives can help you avoid the risks and drawbacks of using a keygen, and enjoy the features and benefits of this software product without any problems.</p>
- <p>We hope that this article has helped you understand what Colos Create Professional 5.0 is, what a keygen is, what a .rar file is, and why you should avoid using a keygen for this software product. We also hope that this article has helped you find some alternatives to using a keygen for this software product. If you have any questions or comments, please feel free to contact us or leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Colos Create Professional 5.0 and using a keygen for this software product.</p>
- <h3>What are the system requirements for Colos Create Professional 5.0?</h3>
- <p>The system requirements for Colos Create Professional 5.0 are as follows:</p>
- <table>
-<tr>
-<th>Operating System</th>
-<th>Windows 7 SP1 or higher (32-bit or 64-bit)</th>
-</tr>
-<tr>
-<th>Processor</th>
-<th>Intel Core i3 or higher</th>
-</tr>
-<tr>
-<th>Memory</th>
-<th>4 GB RAM or higher</th>
-</tr>
-<tr>
-<th>Hard Disk Space</th>
-<th>1 GB or higher</th>
-</tr>
-<tr>
-<th>Display</th>
-<th>1024 x 768 pixels or higher</th>
-</tr>
-<tr>
-<th>Network</th>
-<th>Ethernet or Wi-Fi</th>
-</tr>
-<tr>
-<th>Other Requirements</th>
-<th>.NET Framework 4.6.1 or higher<br/>Microsoft SQL Server Express 2014 SP3 or higher<br/>Microsoft Office 2010 or higher (optional)</th>
-</tr>
-</table>
- <h3>How to install and activate Colos Create Professional 5.0?</h3>
- <p>To install and activate Colos Create Professional 5.0, you need to follow these steps:</p>
- <ol>
-<li>Download the installation file from the official website of Markem-Imaje or from the link provided by their sales team or local distributor or reseller.</li>
-<li>Run the installation file and follow the instructions on the screen.</li>
-<li>Enter the activation code or serial number that you have received from Markem-Imaje or their authorized distributors or resellers.</li>
-<li>Complete the installation process and launch the software.</li>
-<li>Enjoy using Colos Create Professional 5.0!</li>
-</ol>
- <h3>How to use Colos Create Professional 5.0 to create and manage messages?</h3>
- <p>To use Colos Create Professional 5.0 to create and manage messages, you need to follow these steps:</p>
- <ol>
-<li>Launch the software and log in with your username and password.</li>
-<li>Select the device type that you want to use for coding and printing from the list of supported devices.</li>
-<li>Create a new message or open an existing message from the database.</li>
-<li>Edit the message content using text, graphics, barcodes, logos, etc., and customize the message settings according to your requirements.</li>
-<li>Save the message in the database and distribute it to one or more devices across your network.</li>
-<li>Print the message on your products or packages using your selected device.</li>
-<li>Manage your messages centrally, store them in a database, edit them easily, and distribute them to multiple devices across your network.</li>
-</ol>
- <h3>How to troubleshoot common issues with Colos Create Professional 5.0?</h3>
- <p>If you encounter any issues with Colos Create Professional 5.0, such as installation errors , activation errors, message errors, printing errors, etc., you can try the following steps to troubleshoot them:</p>
- <ol>
-<li>Check the system requirements for Colos Create Professional 5.0 and make sure that your computer and network meet them.</li>
-<li>Check the installation and activation instructions for Colos Create Professional 5.0 and make sure that you have followed them correctly.</li>
-<li>Check the user manual and online help for Colos Create Professional 5.0 and make sure that you have understood how to use the software properly.</li>
-<li>Check the error messages and codes that appear on the screen and look for possible causes and solutions in the user manual, online help, or official website of Markem-Imaje.</li>
-<li>Restart the software, the device, or the computer and see if the issue is resolved.</li>
-<li>Update the software, the device, or the computer to the latest version and see if the issue is resolved.</li>
-<li>Contact customer support for Colos Create Professional 5.0 and report the issue, providing as much details as possible, such as error messages, codes, screenshots, etc.</li>
-</ol>
- <h3>How to contact customer support for Colos Create Professional 5.0?</h3>
- <p>If you need any assistance or support with Colos Create Professional 5.0, you can contact customer support for this software product by using any of the following methods:</p>
- <ul>
-<li>Visit the official website of Markem-Imaje at https://www.markem-imaje.com/contact-us, where you can find their contact details, such as phone numbers, email addresses, postal addresses, etc., for different regions and countries.</li>
-<li>Visit the online support portal of Markem-Imaje at https://support.markem-imaje.com/, where you can find various resources, such as user manuals, online help, FAQs, videos, downloads, etc., for different products and services.</li>
-<li>Fill out an online form at https://www.markem-imaje.com/contact-us/contact-forms/support-request-form, where you can request support for a specific product or service, providing your contact details and a brief description of your issue or query.</li>
-<li>Call the toll-free number 1-800-322-0116 (US and Canada) or +1-603-357-1133 (other countries) to speak to a customer service representative who can assist you with your issue or query.</li>
-</ul></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Crashday Crack.full.rar [BETTER].md b/spaces/stomexserde/gpt4-ui/Examples/Crashday Crack.full.rar [BETTER].md
deleted file mode 100644
index a3345e83a5bb2ebc3878faf9b2dbb8b93a35715b..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Crashday Crack.full.rar [BETTER].md	
+++ /dev/null
@@ -1,98 +0,0 @@
-<br />
-<h1>Crashday Crack.Full.Rar: What Is It and How to Download It Safely</h1>
-<p>Crashday is a racing video game that was released in 2006 by Atari SA. It is a multiplayer arcade racer that features seven game modes, 12 vehicles, 36 tracks, weapons, damage model, track editor, modding support, and more. Crashday is a cult classic that has a loyal fan base who love its action-packed and destructive gameplay.</p>
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-<p>Crashday crack.full.rar is a file that contains a cracked version of Crashday that allows you to play the game for free without buying the original game. It also includes the Redline Edition, which is an official re-release version of Crashday that was launched in 2017 by 2tainment GmbH. The Redline Edition has improved graphics, gameplay tweaks, Steam integration, new tracks, and user-made mods from Steam Workshop.</p>
-<p>Some people want to download crashday crack.full.rar because they want to enjoy Crashday without spending money or time on buying the original game. They also want to experience the Redline Edition with its enhanced features and mods. However, downloading crashday crack.full.rar also comes with some risks and challenges that you should be aware of before you decide to do it.</p>
-<p>In this article, we will explain what Crashday and crashday crack.full.rar are, why some people want to download them, what are the risks and benefits of doing so, and how to download them safely. We hope that this article will help you make an informed decision on whether to download crashday crack.full.rar or not.</p>
-<p></p>
- <h2>What Is Crashday and Why It Is a Popular Racing Game</h2>
-<p>Crashday is a racing video game that was developed by Replay Studios and Moonbyte Games. It was released in 2006 by Atari SA for the Windows platform. It is a multiplayer arcade racer that features seven game modes, 12 vehicles, 36 tracks, weapons, damage model, track editor, modding support, and more.</p>
-<p>The game modes of Crashday are:</p>
-<ul>
-<li>Race: A standard racing mode where you compete against other drivers on a track.</li>
-<li>Wrecking Match: A deathmatch mode where you try to destroy other vehicles with weapons and stunts.</li>
-<li>Stunt Show: A mode where you perform stunts and tricks on a track to earn points.</li>
-<li>Hold The Flag: A capture the flag mode where you try to hold a flag for as long as possible while avoiding other drivers.</li>
-<li>Pass The Bomb: A mode where you try to pass a bomb to another driver before it explodes.</li>
-<li>Bomb Run: A mode where you try to reach checkpoints before a timer runs out and your vehicle explodes.</li>
-<li>Test Drive: A mode where you can drive freely on any track and customize your vehicle.</li>
-</ul>
-<p>The vehicles of Crashday are divided into four classes: Compact, Race, Offroad, and Heavy. Each class has different attributes such as speed, acceleration, handling, armor, and weight. You can also customize your vehicle with different colors, decals, parts, and weapons. The weapons include machine guns, rockets, mines, and more.</p>
-<p>The tracks of Crashday are varied and diverse. They range from urban streets, highways, deserts, forests, mountains, to industrial areas. Some tracks have ramps, loops, tunnels, bridges, and destructible objects. You can also create your own tracks with the track editor and share them with other players.</p>
-<p>Crashday is a popular racing game because it offers a lot of fun and action for racing fans. It has a fast-paced and explosive gameplay that keeps you on the edge of your seat. It has a realistic and detailed damage model that makes every crash satisfying and spectacular. It has a creative and flexible track editor that lets you design your own tracks and challenges. It has a modding support that allows you to add new content and features to the game.</p>
-<p>Crashday received positive reviews from critics and players when it was released. It was praised for its graphics, gameplay, sound, variety, and replay value. It was also compared favorably to other racing games such as Burnout, FlatOut, and Carmageddon. It has a score of 7.5 out of 10 on Metacritic and 8.4 out of 10 on IGN.</p>
- <h2>What Is Crashday Crack.Full.Rar and Why Some People Want to Download It</h2>
-<p>Crashday crack.full.rar is a file that contains a cracked version of Crashday that allows you to play the game for free without buying the original game. It also includes the Redline Edition, which is an official re-release version of Crashday that was launched in 2017 by 2tainment GmbH. The Redline Edition has improved graphics, gameplay tweaks, Steam integration, new tracks, and user-made mods from Steam Workshop.</p>
-<p>A cracked version of a game is a modified version that bypasses the copy protection or digital rights management (DRM) system of the game. This means that you can play the game without having the original CD or DVD or the activation code or the online account. A crack is usually a small file or program that you have to run or copy to the game folder to make it work.</p>
-<p>The Redline Edition of Crashday is an updated version of the game that was released in 2017 by 2tainment GmbH in collaboration with Moonbyte Games. The Redline Edition has enhanced graphics with higher resolution textures, anti-aliasing, anisotropic filtering, and dynamic shadows. It also has gameplay tweaks such as improved physics, AI, balance, and performance. It also has Steam integration with achievements, leaderboards, multiplayer, and Steam Workshop. It also has new tracks such as the Airport, the Construction Site, and the Speedway. It also has user-made mods from Steam Workshop that add new cars, tracks, weapons, and more to the game.</p>
-<p>Some people want to download crashday crack.full.rar because they want to enjoy Crashday without spending money or time on buying the original game. They also want to experience the Redline Edition with its enhanced features and mods. They think that downloading crashday crack.full.rar is a convenient and easy way to get the game for free and have more fun with it.</p>
-<p>Some of the reasons why some people want to download crashday crack.full.rar are:</p>
-<ul>
-<li>To play Crashday for free without buying the original game. Some people may not have enough money or time to buy the original game or they may not have access to the online platforms where the game is sold. They may also think that the game is not worth paying for or that they are entitled to play it for free.</li>
-<li>To access the Redline Edition with enhanced graphics and features. Some people may have the original game but they may not have the Redline Edition or they may not be able to upgrade to it. They may also want to try out the Redline Edition before buying it or they may prefer to have it for free.</li>
-<li>To enjoy user-made mods, cars, and tracks from Steam Workshop. Some people may want to have more variety and customization options for their game. They may want to download and install user-made mods, cars, and tracks from Steam Workshop that add new content and features to the game. They may also want to create and share their own mods with other players.</li>
-</ul> <h2>What Are the Risks and Benefits of Downloading Crashday Crack.Full.Rar</h2>
-<p>Downloading crashday crack.full.rar may seem like a good idea for some people who want to play Crashday for free and enjoy its features and mods. However, it also comes with some risks and challenges that you should be aware of before you decide to do it. Downloading crashday crack.full.rar has both benefits and drawbacks that you should weigh carefully.</p>
-<p>Some of the benefits of downloading crashday crack.full.rar are:</p>
-<ul>
-<li>Saving money and time on buying the original game. Downloading crashday crack.full.rar can save you money and time that you would otherwise spend on buying the original game or the Redline Edition. You can get the game for free and play it whenever you want without any restrictions or limitations.</li>
-<li>Having more fun and variety with the Redline Edition and mods. Downloading crashday crack.full.rar can give you access to the Redline Edition with its improved graphics, gameplay, and features. You can also download and install user-made mods, cars, and tracks from Steam Workshop that add new content and features to the game. You can have more fun and variety with your game and customize it to your liking.</li>
-<li>Supporting the developers of Crashday by increasing its popularity. Downloading crashday crack.full.rar can also help the developers of Crashday by increasing its popularity and exposure. You can spread the word about the game and encourage other people to play it or buy it. You can also give feedback and suggestions to the developers on how to improve the game or fix any issues.</li>
-</ul>
-<p>Some of the risks of downloading crashday crack.full.rar are:</p>
-<ul>
-<li>Getting infected by viruses, malware, or spyware. Downloading crashday crack.full.rar can expose your computer to viruses, malware, or spyware that can harm your system or steal your data. You may download a fake or corrupted file that contains malicious code or programs that can infect your computer or compromise your security. You may also download a file that has unwanted or harmful software bundled with it that can install itself on your computer without your consent.</li>
-<li>Facing legal issues or penalties for piracy or copyright infringement. Downloading crashday crack.full.rar can also put you in legal trouble or risk for piracy or copyright infringement. You may violate the laws or regulations of your country or region that prohibit or punish the unauthorized distribution or use of copyrighted material. You may also infringe on the rights or interests of the owners or creators of Crashday or the Redline Edition. You may face lawsuits, fines, penalties, or even jail time for downloading crashday crack.full.rar.</li>
-<li>Having technical problems or errors with the game or the crack. Downloading crashday crack.full.rar can also cause technical problems or errors with the game or the crack. You may download a file that is incomplete, outdated, incompatible, or faulty that can prevent the game from running properly or at all. You may also encounter bugs, glitches, crashes, freezes, or performance issues with the game or the crack. You may also have difficulties with updating, patching, modding, or multiplayer features of the game or the crack.</li>
-</ul> <h2>How to Download Crashday Crack.Full.Rar Safely</h2>
-<p>If you have decided to download crashday crack.full.rar, you should do it safely and responsibly. You should take some precautions and measures to avoid or minimize the risks and problems that we have mentioned above. You should also respect the rights and interests of the developers and owners of Crashday and the Redline Edition.</p>
-<p>Here is a step-by-step guide on how to download crashday crack.full.rar safely:</p>
-<ol>
-<li>Finding a reliable and trustworthy source or website for the download link. You should do some research and check the reputation and credibility of the source or website that offers the download link for crashday crack.full.rar. You should avoid shady or suspicious websites that may have fake or harmful files or links. You should also read the comments, reviews, or feedback from other users who have downloaded the file or link.</li>
-<li>Checking the file size, name, and extension of the download file. You should verify the file size, name, and extension of the download file before you download it. You should compare it with the original file size, name, and extension of Crashday or the Redline Edition. You should also check if the file has a .rar extension, which means that it is a compressed file that needs to be extracted. You should avoid files that have different or unknown extensions, such as .exe, .bat, .scr, .vbs, or .dll.</li>
-<li>Scanning the download file with an antivirus or anti-malware program. You should scan the download file with an antivirus or anti-malware program before you open or extract it. You should use a reputable and updated program that can detect and remove any viruses, malware, or spyware that may be hidden in the file. You should also scan your computer regularly to ensure that it is clean and safe.</li>
-<li>Extracting the download file with a password or a key if needed. You should extract the download file with a password or a key if it is required. You should get the password or the key from the source or website that provided the download link. You should not enter any personal or sensitive information to get the password or the key. You should also use a trusted and secure program to extract the file, such as WinRAR or 7-Zip.</li>
-<li>Installing the game and applying the crack according to the instructions. You should install the game and apply the crack according to the instructions that are included in the download file or on the source or website. You should follow the steps carefully and correctly to avoid any errors or issues with the game or the crack. You should also backup your original game files before you overwrite them with the cracked files.</li>
-<li>Running the game as an administrator or in compatibility mode if needed. You should run the game as an administrator or in compatibility mode if needed. This can help you solve some technical problems or errors that may occur with the game or the crack. You can do this by right-clicking on the game icon and selecting "Run as administrator" or "Properties" and then "Compatibility".</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In conclusion, crashday crack.full.rar is a file that contains a cracked version of Crashday that allows you to play the game for free without buying the original game. It also includes the Redline Edition, which is an official re-release version of Crashday that was launched in 2017 by 2tainment GmbH. The Redline Edition has improved graphics, gameplay tweaks, Steam integration, new tracks, and user-made mods from Steam Workshop.</p>
-<p>Some people want to download crashday crack.full.rar because they want to enjoy Crashday without spending money or time on buying the original game. They also want to experience the Redline Edition with its enhanced features and mods. However, downloading crashday crack.full.rar also comes with some risks and challenges that you should be aware of before you decide to do it.</p>
-<p>Downloading crashday crack.full.rar has both benefits and drawbacks that you should weigh carefully. Some of the benefits are saving money and time on buying the original game, having more fun and variety with the Redline Edition and mods, and supporting the developers of Crashday by increasing its popularity. Some of the risks are getting infected by viruses, malware, or spyware, facing legal issues or penalties for piracy or copyright infringement, and having technical problems or errors with the game or the crack.</p>
-<p>If you have decided to download crashday crack.full.rar, you should do it safely and responsibly. You should take some precautions and measures to avoid or minimize the risks and problems that we have mentioned above. You should also respect the rights and interests of the developers and owners of Crashday and the Redline Edition.</p>
-<p>We hope that this article has helped you understand what crashday crack.full.rar is, why some people want to download it, what are the risks and benefits of doing so, and how to download it safely. We hope that you will make an informed decision on whether to download crashday crack.full.rar or not. We also hope that you will enjoy playing Crashday and have a lot of fun with it.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about crashday crack.full.rar:</p>
-<ol>
-<li>Q: Where can I find a reliable and trustworthy source or website for the download link for crashday crack.full.rar?<br>
-A: There is no definitive answer to this question, as different sources or websites may have different levels of reliability and trustworthiness. However, some general tips to find a good source or website are: <ul>
-<li>Look for sources or websites that have positive ratings, reviews, or feedback from other users who have downloaded the file or link.</li>
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-A: You can scan the download file with an antivirus or anti-malware program by following these steps: <ul>
-<li>Download and install a reputable and updated antivirus or anti-malware program on your computer.</li>
-<li>Right-click on the download file and select "Scan with [name of your program]" from the menu.</li>
-<li>Wait for the program to scan the file and show you the results.</li>
-<li>If the program detects any viruses, malware, or spyware in the file, delete the file immediately and do not open or extract it.</li>
-<li>If the program does not detect any viruses, malware, or spyware in the file, proceed with opening or extracting it.</li>
-</ul></li>
-<li>Q: How can I run the game as an administrator or in compatibility mode?<br>
-A: You can run the game as an administrator or in compatibility mode by following these steps: <ul>
-<li>Right-click on the game icon and select "Properties" from the menu.</li>
-<li>Select the "Compatibility" tab from the window that opens.</li>
-<li>To run the game as an administrator, check the box next to "Run this program as an administrator" and click "Apply" and "OK".</li>
-<li>To run the game in compatibility mode, check the box next to "Run this program in compatibility mode for:" and select a compatible operating system from the drop-down list and click "Apply" and "OK".</li>
-</ul></li>
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-</ol></p> b2dd77e56b<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/How To Download Obs For Mac 2021.md b/spaces/stomexserde/gpt4-ui/Examples/How To Download Obs For Mac 2021.md
deleted file mode 100644
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--- a/spaces/stomexserde/gpt4-ui/Examples/How To Download Obs For Mac 2021.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-<br />
-<h1>How To Download OBS For Mac: A Step-By-Step Guide</h1>
-<p>OBS Studio is a free and open source software for video recording and live streaming. It allows you to capture your screen, webcam, microphone, and other sources, and mix them together in real time. You can also add transitions, filters, scenes, and more to create professional-looking videos.</p>
-<p>If you want to use OBS Studio on your Mac computer, you need to download and install it first. In this article, we will show you how to do that in a few simple steps.</p>
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-
-<h2>Step 1: Visit the OBS Project website</h2>
-<p>The first thing you need to do is to go to the official website of OBS Project at <a href="https://obsproject.com/download">https://obsproject.com/download</a>. Here you will find the download links for different operating systems, including Windows, Mac, and Linux.</p>
-<p>Click on the Mac logo to see the available download options for macOS. You will see two buttons: one for downloading the installer file (.dmg), and another for downloading via Homebrew (a package manager for macOS).</p>
-
-<h2>Step 2: Download the installer file</h2>
-<p>We recommend downloading the installer file (.dmg) as it is easier and faster than using Homebrew. Click on the "Download Installer" button to start the download process. The file size is about 140 MB, so it may take a few minutes depending on your internet speed.</p>
-<p></p>
-<p>Once the download is complete, locate the file in your Downloads folder or wherever you saved it. It should have a name like "obs-mac-27.1.3.dmg". Double-click on it to open it.</p>
-
-<h2>Step 3: Install OBS Studio</h2>
-<p>After opening the installer file, you will see a window with two icons: one for OBS Studio and another for Applications. Drag and drop the OBS Studio icon onto the Applications icon to copy it to your Applications folder. This will install OBS Studio on your Mac.</p>
-<p>You can also right-click on the OBS Studio icon and select "Open" to launch it directly from the installer window. However, we recommend moving it to your Applications folder first for easier access in the future.</p>
-
-<h2>Step 4: Run OBS Studio</h2>
-<p>Now that you have installed OBS Studio on your Mac, you can run it from your Applications folder or from Launchpad. You may see a warning message saying that OBS Studio is an app downloaded from the internet and asking if you want to open it. Click on "Open" to confirm.</p>
-<p>The first time you run OBS Studio, you will be asked to choose whether you want to use the Auto-Configuration Wizard or not. This wizard will help you optimize your settings based on your system and your streaming goals. You can either click on "Yes" to use it or "No" to skip it.</p>
-<p>If you choose to use the wizard, you will have to answer some questions about your video resolution, framerate, bitrate, streaming service, etc. The wizard will then test your settings and apply them automatically. You can always change them later if you want.</p>
-
-<h2>Step 5: Enjoy OBS Studio</h2>
-<p>Congratulations! You have successfully downloaded and installed OBS Studio on your Mac. You can now start creating and streaming your videos with this powerful software.</p>
-<p>To learn more about how to use OBS Studio, you can check out their <a href="https://obsproject.com/wiki/">Knowledge Base</a>, their <a href="https://obsproject.com/forum/">Forum</a>, or their <a href="https://www.youtube.com/user/obsproject">YouTube Channel</a>. You can also find many tutorials and guides online from other users and experts.</p> 7b8c122e87<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Jean Meeus Book Astronomical Algorithms Download ((LINK)).md b/spaces/stomexserde/gpt4-ui/Examples/Jean Meeus Book Astronomical Algorithms Download ((LINK)).md
deleted file mode 100644
index 4aa73715e0b78c7eda6aa5af38d7fee35811e4e5..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Jean Meeus Book Astronomical Algorithms Download ((LINK)).md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>How to Download Jean Meeus' Book Astronomical Algorithms</h1>
-<p>If you are interested in learning about the mathematics of astronomy, you may want to download Jean Meeus' book Astronomical Algorithms. This book covers topics such as planetary orbits, lunar events, calendrical calculations, and more. It provides formulas, algorithms, and examples for computing various astronomical phenomena. In this article, we will show you how to download this book from different sources.</p>
-<h2>Archive.org</h2>
-<p>One of the easiest ways to download Jean Meeus' book Astronomical Algorithms is from Archive.org. This website offers free access to millions of books, movies, music, and other digital content. You can find the book by searching for "Astronomical Algorithms Jean Meeus" or by following this link: <a href="https://archive.org/details/astronomicalalgorithmsjeanmeeus1991">https://archive.org/details/astronomicalalgorithmsjeanmeeus1991</a>. You can download the book in various formats, such as PDF, EPUB, or MOBI. You can also read it online or borrow it for 14 days.</p>
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-<h2>Google Books</h2>
-<p>Another option to download Jean Meeus' book Astronomical Algorithms is from Google Books. This website allows you to search and preview millions of books from libraries and publishers worldwide. You can find the book by searching for "Astronomical Algorithms Jean Meeus" or by following this link: <a href="https://books.google.com/books/about/Astronomical_Algorithms.html?id=1smAQgAACAAJ">https://books.google.com/books/about/Astronomical_Algorithms.html?id=1smAQgAACAAJ</a>. You can download a preview of the book in PDF format or buy the full version from the publisher or other sellers.</p>
-<h2>ACM Digital Library</h2>
-<p>A third option to download Jean Meeus' book Astronomical Algorithms is from ACM Digital Library. This website provides access to publications and resources from the Association for Computing Machinery (ACM), which is a professional organization for computer science and related fields. You can find the book by searching for "Astronomical Algorithms" or by following this link: <a href="https://dl.acm.org/doi/book/10.5555/532892">https://dl.acm.org/doi/book/10.5555/532892</a>. You can download a citation of the book in various formats, such as BibTeX or EndNote. You can also request a copy from the author or the publisher.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Jean Meeus' book Astronomical Algorithms from different sources. This book is a valuable resource for anyone who wants to learn about the mathematics of astronomy and how to compute various astronomical phenomena. We hope you find this article helpful and enjoy reading this book.</p>
-
-<h2>Who is Jean Meeus?</h2>
-<p>Jean Meeus is a Belgian meteorologist and amateur astronomer who is well-known for his expertise in celestial mechanics, spherical astronomy, and mathematical astronomy. He was born on 12 December 1928 and studied mathematics at the University of Leuven in Belgium, where he received the Degree of Licentiate in 1953. From then until his retirement in 1993, he was a meteorologist at Brussels Airport. He has also been an active member of various astronomical societies and organizations, such as the International Astronomical Union and the Astronomical Society of the Pacific. He has received several awards and honors for his contributions to the field of astronomy, such as the Amateur Achievement Award of the Astronomical Society of the Pacific in 1986 and the naming of the main belt asteroid 2213 Meeus after him in 1981.</p>
-<h2>Why is Astronomical Algorithms important?</h2>
-<p>Astronomical Algorithms is one of the most comprehensive and authoritative books on the mathematics of astronomy. It covers a wide range of topics, such as time systems, coordinate transformations, eclipses, planetary phenomena, lunar phenomena, solar phenomena, calendars, and more. It provides clear explanations, formulas, algorithms, and examples for computing various astronomical phenomena with high accuracy and efficiency. It also includes references to other sources of information and data, such as astronomical tables and ephemerides. The book is intended for anyone who wants to learn about the practical aspects of astronomy and how to apply them to various situations and problems. It is especially useful for amateur astronomers, students, teachers, researchers, programmers, and engineers who are interested in celestial mechanics and spherical astronomy.</p>
-<h2>How to use Astronomical Algorithms?</h2>
-<p>Astronomical Algorithms can be used as a reference book or a textbook for learning about the mathematics of astronomy. The book is divided into 67 chapters that cover different topics and subtopics. Each chapter begins with an introduction that explains the main concepts and objectives of the chapter. Then, it presents the formulas, algorithms, and examples that illustrate how to compute various astronomical quantities and events. The book also provides some hints and tips on how to use calculators or computers for performing the calculations. The book assumes that the reader has some basic knowledge of mathematics, such as algebra, trigonometry, calculus, and numerical methods. However, it does not require any advanced knowledge of astronomy or physics. The book can be used as a self-study guide or as a supplement to other books or courses on astronomy.</p>
-<p></p> 81aa517590<br />
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diff --git a/spaces/stratussox/yolov5_inference/utils/loggers/__init__.py b/spaces/stratussox/yolov5_inference/utils/loggers/__init__.py
deleted file mode 100644
index bc8dd7621579f6372ce60e317c9e031e313e1c37..0000000000000000000000000000000000000000
--- a/spaces/stratussox/yolov5_inference/utils/loggers/__init__.py
+++ /dev/null
@@ -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}'
diff --git a/spaces/studiobrn/SplitTrack/tests/data/__init__.py b/spaces/studiobrn/SplitTrack/tests/data/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/studiobrn/SplitTrack/tests/data/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/sub314xxl/MusicGen/setup.py b/spaces/sub314xxl/MusicGen/setup.py
deleted file mode 100644
index 78a172b7c90003b689bde40b49cc8fe1fb8107d4..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen/setup.py
+++ /dev/null
@@ -1,65 +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.
- 
-"""
-
-from pathlib import Path
-
-from setuptools import setup, find_packages
-
-
-NAME = 'audiocraft'
-DESCRIPTION = 'Audio research library for PyTorch'
-
-URL = 'https://github.com/fairinternal/audiocraft'
-AUTHOR = 'FAIR Speech & Audio'
-EMAIL = 'defossez@meta.com'
-REQUIRES_PYTHON = '>=3.8.0'
-
-for line in open('audiocraft/__init__.py'):
-    line = line.strip()
-    if '__version__' in line:
-        context = {}
-        exec(line, context)
-        VERSION = context['__version__']
-
-HERE = Path(__file__).parent
-
-try:
-    with open(HERE / "README.md", encoding='utf-8') as f:
-        long_description = '\n' + f.read()
-except FileNotFoundError:
-    long_description = DESCRIPTION
-
-REQUIRED = [i.strip() for i in open(HERE / 'requirements.txt') if not i.startswith('#')]
-
-setup(
-    name=NAME,
-    version=VERSION,
-    description=DESCRIPTION,
-    author_email=EMAIL,
-    long_description=long_description,
-    long_description_content_type='text/markdown',
-    author=AUTHOR,
-    url=URL,
-    python_requires=REQUIRES_PYTHON,
-    install_requires=REQUIRED,
-    extras_require={
-        'dev': ['coverage', 'flake8', 'mypy', 'pdoc3', 'pytest'],
-    },
-    packages=find_packages(),
-    package_data={'audiocraft': ['py.typed']},
-    include_package_data=True,
-    license='MIT License',
-    classifiers=[
-        # Trove classifiers
-        # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
-        'License :: OSI Approved :: MIT License',
-        'Topic :: Multimedia :: Sound/Audio',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-    ],
-)
diff --git a/spaces/sunil448832/retrieval-augment-generation/models/llms.py b/spaces/sunil448832/retrieval-augment-generation/models/llms.py
deleted file mode 100644
index 4b71d00666780ffd073a410bf6b1b9a89462256f..0000000000000000000000000000000000000000
--- a/spaces/sunil448832/retrieval-augment-generation/models/llms.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-# Define a Language Model class
-class LLM:
-    def __init__(self, model_name):
-        # Determine the device to use (GPU if available, otherwise CPU)
-        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-        
-        # Load the pre-trained language model with specific settings
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            torch_dtype=torch.float16,  # Set the data type to float16
-            load_in_8bit=True,         # Load in 8-bit format if available
-            device_map='auto'          # Automatically select the device
-        ).bfloat16()  # Convert the model to bfloat16 for lower precision
-        
-        # Initialize the tokenizer for the same model
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        
-        # Set custom padding token and padding side
-        self.tokenizer.pad_token = "[PAD]"
-        self.tokenizer.padding_side = "left"
-
-    def generate_response(self, messages, max_tokens=100, do_sample=True):
-        # Tokenize the input messages and move them to the selected device (GPU or CPU)
-        input_ids = self.tokenizer(
-            messages,
-            max_length=512,
-            padding=True,
-            truncation=True,
-            return_tensors='pt'
-        ).input_ids.cuda()
-        
-        with torch.no_grad():
-            # Generate a response using the loaded model
-            generated_ids = self.model.generate(
-                input_ids,
-                pad_token_id=self.tokenizer.pad_token_id,
-                max_new_tokens=max_tokens,
-                do_sample=do_sample,
-                temperature=0.3  # Adjust the sampling temperature
-            )
-            # Decode the generated tokens into a human-readable response
-            response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]
-        
-        return response
-
-# Main program
-if __name__ == '__main__':
-    # Specify the model name to use
-    model_name = "mistralai/Mistral-7B-Instruct-v0.1"
-    
-    # Create an instance of the Language Model class with the specified model
-    llm = LLM(model_name)
diff --git a/spaces/sunshineatnoon/TextureScraping/models/week0417/model.py b/spaces/sunshineatnoon/TextureScraping/models/week0417/model.py
deleted file mode 100644
index b75765038e8a0d6d591dd311cafa8b3ff598dbdb..0000000000000000000000000000000000000000
--- a/spaces/sunshineatnoon/TextureScraping/models/week0417/model.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-import torchvision.transforms.functional as TF
-
-from .taming_blocks import Encoder
-from .loss import styleLossMaskv3
-from .nnutils import SPADEResnetBlock, get_edges, initWave
-
-from libs.nnutils import poolfeat, upfeat
-from libs.utils import label2one_hot_torch
-from .meanshift_utils import meanshift_cluster, meanshift_assign
-
-from swapae.models.networks.stylegan2_layers import ConvLayer
-from torch_geometric.nn import GCNConv
-from torch_geometric.utils import softmax
-
-class GCN(nn.Module):
-    def __init__(self, n_cluster, temperature = 1, hidden_dim = 256):
-        super().__init__()
-        self.gcnconv1 = GCNConv(hidden_dim, hidden_dim, add_self_loops = True)
-        self.gcnconv2 = GCNConv(hidden_dim, hidden_dim, add_self_loops = True)
-        self.pool1 = nn.Sequential(nn.Conv2d(hidden_dim, n_cluster, 3, 1, 1))
-        self.temperature = temperature
-
-    def compute_edge_score_softmax(self, raw_edge_score, edge_index, num_nodes):
-        return softmax(raw_edge_score, edge_index[1], num_nodes=num_nodes)
-
-    def compute_edge_weight(self, node_feature, edge_index):
-        src_feat = torch.gather(node_feature, 0, edge_index[0].unsqueeze(1).repeat(1, node_feature.shape[1]))
-        tgt_feat = torch.gather(node_feature, 0, edge_index[1].unsqueeze(1).repeat(1, node_feature.shape[1]))
-        raw_edge_weight = nn.CosineSimilarity(dim=1, eps=1e-6)(src_feat, tgt_feat)
-        edge_weight = self.compute_edge_score_softmax(raw_edge_weight, edge_index, node_feature.shape[0])
-        return raw_edge_weight.squeeze(), edge_weight.squeeze()
-
-    def forward(self, sp_code, slic, clustering = False):
-        edges, aff = get_edges(torch.argmax(slic, dim = 1).unsqueeze(1), sp_code.shape[1])
-        prop_code = []
-        sp_assign = []
-        edge_weights = []
-        conv_feats = []
-        for i in range(sp_code.shape[0]):
-            # compute edge weight
-            edge_index = edges[i]
-            raw_edge_weight, edge_weight = self.compute_edge_weight(sp_code[i], edge_index)
-            feat = self.gcnconv1(sp_code[i], edge_index, edge_weight = edge_weight)
-            raw_edge_weight, edge_weight = self.compute_edge_weight(feat, edge_index)
-            edge_weights.append(raw_edge_weight)
-            feat = F.leaky_relu(feat, 0.2)
-            feat = self.gcnconv2(feat, edge_index, edge_weight = edge_weight)
-
-            # maybe clustering
-            conv_feat = upfeat(feat, slic[i:i+1])
-            conv_feats.append(conv_feat)
-            if not clustering:
-                feat = conv_feat
-                pred_mask = slic[i:i+1]
-            else:
-                pred_mask = self.pool1(conv_feat)
-                # enforce pixels belong to the same superpixel to have same grouping label
-                pred_mask = upfeat(poolfeat(pred_mask, slic[i:i+1]), slic[i:i+1])
-                s_ = F.softmax(pred_mask * self.temperature, dim = 1)
-
-                # compute texture code w.r.t grouping
-                pool_feat = poolfeat(conv_feat, s_, avg = True)
-                feat = upfeat(pool_feat, s_)
-
-            prop_code.append(feat)
-            sp_assign.append(pred_mask)
-        prop_code = torch.cat(prop_code)
-        conv_feats = torch.cat(conv_feats)
-        return prop_code, torch.cat(sp_assign), conv_feats
-
-class SPADEGenerator(nn.Module):
-    def __init__(self, in_dim, hidden_dim):
-        super().__init__()
-        nf = hidden_dim // 16
-
-        self.head_0 = SPADEResnetBlock(in_dim, 16 * nf)
-
-        self.G_middle_0 = SPADEResnetBlock(16 * nf, 16 * nf)
-        self.G_middle_1 = SPADEResnetBlock(16 * nf, 16 * nf)
-
-        self.up_0 = SPADEResnetBlock(16 * nf, 8 * nf)
-        self.up_1 = SPADEResnetBlock(8 * nf, 4 * nf)
-        self.up_2 = SPADEResnetBlock(4 * nf, 2 * nf)
-        self.up_3 = SPADEResnetBlock(2 * nf, 1 * nf)
-
-        final_nc = nf
-
-        self.conv_img = nn.Conv2d(final_nc, 3, 3, padding=1)
-
-        self.up = nn.Upsample(scale_factor=2)
-
-    def forward(self, sine_wave, texon):
-
-        x = self.head_0(sine_wave, texon)
-
-        x = self.up(x)
-        x = self.G_middle_0(x, texon)
-        x = self.G_middle_1(x, texon)
-
-        x = self.up(x)
-        x = self.up_0(x, texon)
-        x = self.up(x)
-        x = self.up_1(x, texon)
-        #x = self.up(x)
-        x = self.up_2(x, texon)
-        #x = self.up(x)
-        x = self.up_3(x, texon)
-
-        x = self.conv_img(F.leaky_relu(x, 2e-1))
-        return x
-
-class Waver(nn.Module):
-    def __init__(self, tex_code_dim, zPeriodic):
-        super(Waver, self).__init__()
-        K = tex_code_dim
-        layers =  [nn.Conv2d(tex_code_dim, K, 1)]
-        layers += [nn.ReLU(True)]
-        layers += [nn.Conv2d(K, 2 * zPeriodic, 1)]
-        self.learnedWN =  nn.Sequential(*layers)
-        self.waveNumbers = initWave(zPeriodic)
-
-    def forward(self, GLZ=None):
-        return (self.waveNumbers.to(GLZ.device) + self.learnedWN(GLZ))
-
-class AE(nn.Module):
-    def __init__(self, args, **ignore_kwargs):
-        super(AE, self).__init__()
-
-        # encoder & decoder
-        self.enc = Encoder(ch=64, out_ch=3, ch_mult=[1,2,4,8], num_res_blocks=1, attn_resolutions=[],
-                           in_channels=3, resolution=args.crop_size, z_channels=args.hidden_dim, double_z=False)
-        self.G = SPADEGenerator(args.spatial_code_dim + 32, args.hidden_dim)
-
-        self.add_module(
-            "ToTexCode",
-            nn.Sequential(
-                ConvLayer(args.hidden_dim, args.hidden_dim, kernel_size=3, activate=True, bias=True),
-                ConvLayer(args.hidden_dim, args.tex_code_dim, kernel_size=3, activate=True, bias=True),
-                ConvLayer(args.tex_code_dim, args.hidden_dim, kernel_size=1, activate=False, bias=False)
-            )
-        )
-        self.gcn = GCN(n_cluster = args.n_cluster, temperature = args.temperature, hidden_dim = args.hidden_dim)
-
-        self.add_gcn_epoch = args.add_gcn_epoch
-        self.add_clustering_epoch = args.add_clustering_epoch
-        self.add_texture_epoch = args.add_texture_epoch
-
-        self.patch_size = args.patch_size
-        self.style_loss = styleLossMaskv3(device = args.device)
-        self.sine_wave_dim = args.spatial_code_dim
-        self.noise_dim = 32
-        self.spatial_code_dim = args.spatial_code_dim
-
-        # inpainting network
-        if args.spatial_code_dim > 0:
-            self.learnedWN = Waver(args.hidden_dim, zPeriodic = args.spatial_code_dim)
-
-            self.add_module(
-                "Amplitude",
-                nn.Sequential(
-                    nn.Conv2d(args.hidden_dim, args.hidden_dim//2, 1, 1, 0),
-                    nn.Conv2d(args.hidden_dim//2, args.hidden_dim//4, 1, 1, 0),
-                    nn.Conv2d(args.hidden_dim//4, args.spatial_code_dim, 1, 1, 0)
-                    )
-                )
-
-        self.bandwidth = 3.0
-
-    def sample_patch_from_mask(self, mask, patch_num = 10, patch_size = 64):
-        """
-        - Sample `patch_num` patches of size `patch_size*patch_size` w.r.t given mask
-        """
-        nonzeros = torch.nonzero(mask.view(-1)).squeeze()
-        n = len(nonzeros)
-        xys = []
-        imgH, imgW = mask.shape
-        half_patch = patch_size // 2
-        iter_num = 0
-        while len(xys) < patch_num:
-            id = (torch.ones(n)*1.0/n).multinomial(num_samples=1, replacement=False)
-            rx = nonzeros[id] // imgW
-            ry = nonzeros[id] % imgW
-            top = max(0, rx - half_patch)
-            bot = min(imgH, rx + half_patch)
-            left = max(0, ry - half_patch)
-            right = min(imgW, ry + half_patch)
-            patch_mask = mask[top:bot, left:right]
-            if torch.sum(patch_mask) / (patch_size ** 2) > 0.5 or iter_num > 20:
-                xys.append([top, bot, left, right])
-            iter_num += 1
-        return xys
-
-    def get_sine_wave(self, GL, offset_mode = 'rec'):
-        imgH, imgW = GL.shape[-2]//8, GL.shape[-1] // 8
-        GL = F.interpolate(GL, size = (imgH, imgW), mode = 'nearest')
-        xv, yv = np.meshgrid(np.arange(imgH), np.arange(imgW),indexing='ij')
-        c = torch.FloatTensor(np.concatenate([xv[np.newaxis], yv[np.newaxis]], 0)[np.newaxis])
-        c = c.to(GL.device)
-        # c: 1, 2, 28, 28
-        c = c.repeat(GL.shape[0], self.sine_wave_dim, 1, 1)
-        # c: 1, 64, 28, 28
-        period = self.learnedWN(GL)
-        # period: 1, 64, 28, 28
-        raw = period * c
-
-        # random offset
-        roffset = torch.zeros((GL.shape[0], self.sine_wave_dim, 1, 1)).to(GL.device).uniform_(-1, 1) * 6.28
-        roffset = roffset.repeat(1, 1, imgH, imgW)
-        rwave = torch.sin(raw[:, ::2] + raw[:, 1::2] + roffset)
-
-        # zero offset
-        zwave = torch.sin(raw[:, ::2] + raw[:, 1::2])
-        A = self.Amplitude(GL)
-        A = torch.sigmoid(A)
-        wave = torch.cat((zwave, rwave)) * A.repeat(2, 1, 1, 1)
-        return wave
-
-    def syn_tex(self, tex_code, mask, imgH, imgW, offset_mode = 'rec', tex_idx = None):
-        # synthesize all textures
-        # spatial: B x 256 x 14 x 14
-        # tex_code: B x N x 256
-        B, N, _ = tex_code.shape
-        H = imgH // 8
-        W = imgW // 8
-
-        # randomly sample a texture and synthesize it
-        # throw away small texture segments
-        areas = torch.sum(mask, dim=(2, 3))
-        valid_idxs = torch.nonzero(areas[0] / (imgH * imgW) > 0.01).squeeze(-1)
-        if tex_idx is None or tex_idx >= tex_code.shape[1]:
-            tex_idx = valid_idxs[torch.multinomial(areas[0, valid_idxs], 1).squeeze()]
-        else:
-            sorted_list = torch.argsort(areas, dim = 1, descending = True)
-            tex_idx = sorted_list[0, tex_idx]
-        sampled_code = tex_code[:, tex_idx, :]
-        rec_tex = sampled_code.view(1, -1, 1, 1).repeat(1, 1, imgH, imgW)
-
-        # Decoder: Spatial & Texture code -> Image
-        if self.noise_dim == 0:
-            dec_input = self.get_sine_wave(rec_tex, offset_mode)
-        elif self.spatial_code_dim == 0:
-            dec_input = torch.randn(rec_tex.shape[0], self.noise_dim, H, W).to(tex_code.device)
-        else:
-            sine_wave = self.get_sine_wave(rec_tex, offset_mode)
-            noise = torch.randn(sine_wave.shape[0], self.noise_dim, H, W).to(tex_code.device)
-            dec_input = torch.cat((sine_wave, noise), dim = 1)
-
-        tex_syn = self.G(dec_input, rec_tex.repeat(dec_input.shape[0], 1, 1, 1))
-
-        return tex_syn, tex_idx
-
-    def sample_tex_patches(self, tex_idx, rgb_img, rep_rec, mask, patch_num = 10):
-        patches = []
-        masks = []
-        patch_masks = []
-        # sample patches from input image and reconstruction
-        for i in range(rgb_img.shape[0]):
-            # WARNING: : This only works for batch_size = 1 for now
-            maski = mask[i, tex_idx]
-            masks.append(maski.unsqueeze(0))
-            xys = self.sample_patch_from_mask(maski, patch_num = patch_num, patch_size = self.patch_size)
-            # sample 10 patches from input image & reconstruction w.r.t group mask
-            for k in range(patch_num):
-                top, bot, left, right = xys[k]
-                patch_ = rgb_img[i, :, top:bot, left:right]
-                patch_mask_ = maski[top:bot, left:right]
-
-                # In case the patch is on the boundary and smaller than patch_size
-                # We put the patch at some random place of a black image
-                h, w = patch_.shape[-2:]
-                x = 0; y = 0
-                if h < self.patch_size:
-                    x = np.random.randint(0, self.patch_size - h)
-                if w < self.patch_size:
-                    y = np.random.randint(0, self.patch_size - w)
-                patch = torch.zeros(1, 3, self.patch_size, self.patch_size).to(patch_.device)
-                patch_mask = torch.zeros(1, 1, self.patch_size, self.patch_size).to(patch_.device)
-                patch[:, :, x:x+h, y:y+w] = patch_
-                patch_mask[:, :, x:x+h, y:y+w] = patch_mask_
-                patches.append(patch)
-                patch_masks.append(patch_mask)
-        patches = torch.cat(patches)
-        masks = torch.stack(masks)
-        patch_masks = torch.cat(patch_masks)
-
-        # sample patches from synthesized texture
-        tex_patch_size = self.patch_size
-        rep_patches = []
-        for k in range(patch_num):
-            i, j, h, w = transforms.RandomCrop.get_params(rep_rec, output_size=(tex_patch_size, tex_patch_size))
-            rep_rec_patch = TF.crop(rep_rec, i, j, h, w)
-            rep_patches.append(rep_rec_patch)
-        rep_patches = torch.stack(rep_patches, dim = 1)
-        rep_patches = rep_patches.view(-1, 3, tex_patch_size, tex_patch_size)
-        return masks, patch_masks, patches, rep_patches
-
-    def forward(self, rgb_img, slic, epoch = 0, test_time = False, test = False, tex_idx = None):
-        #self.patch_size = np.random.randint(64, 160)
-        B, _, imgH, imgW = rgb_img.shape
-        outputs = {}
-        rec_feat_list = []
-        seg_map = [torch.argmax(slic.cpu(), dim = 1)]
-
-        # Encoder: img (B, 3, H, W) -> feature (B, C, imgH//8, imgW//8)
-        conv_feat, layer_feats = self.enc(rgb_img)
-        B, C, H, W = conv_feat.shape
-
-        # Texture code for each superpixel
-        tex_code = self.ToTexCode(conv_feat)
-
-        code = F.interpolate(tex_code, size = (imgH, imgW), mode = 'bilinear', align_corners = False)
-        pool_code = poolfeat(code, slic, avg = True)
-
-        if epoch >= self.add_gcn_epoch:
-            prop_code, sp_assign, conv_feats = self.gcn(pool_code, slic, (self.add_clustering_epoch <= epoch))
-            softmax = F.softmax(sp_assign * self.gcn.temperature, dim = 1)
-            rec_feat_list.append(prop_code)
-            seg_map = [torch.argmax(sp_assign.cpu(), dim = 1)]
-        else:
-            rec_code = upfeat(pool_code, slic)
-            rec_feat_list.append(rec_code)
-            softmax = slic
-
-        # Texture synthesis
-        if epoch >= self.add_texture_epoch:
-            sp_feat = poolfeat(conv_feats, slic, avg = True).squeeze(0)
-            pts = meanshift_cluster(sp_feat, self.bandwidth, meanshift_step = 15)[-1]
-            with torch.no_grad():
-                sp_assign, _ = meanshift_assign(pts, self.bandwidth)
-                sp_assign = torch.tensor(sp_assign).unsqueeze(-1).to(slic.device).float()
-                sp_assign = upfeat(sp_assign, slic)
-                seg = label2one_hot_torch(sp_assign, C = sp_assign.max().long() + 1)
-                seg_map = [torch.argmax(seg.cpu(), dim = 1)]
-
-            # texture code for each connected group
-            tex_seg = poolfeat(conv_feats, seg, avg = True)
-            if test:
-                rep_rec, tex_idx = self.syn_tex(tex_seg, seg, 564, 564, tex_idx = tex_idx)
-                #rep_rec, tex_idx = self.syn_tex(tex_seg, seg, 1024, 1024, tex_idx = tex_idx)
-            else:
-                rep_rec, tex_idx = self.syn_tex(tex_seg, seg, imgH, imgW, tex_idx = tex_idx)
-            rep_rec = (rep_rec + 1) / 2.0
-            rgb_img = (rgb_img + 1) / 2.0
-
-            # sample patches from input image, reconstruction & synthesized texture
-            # zero offset
-            zmasks, zpatch_masks, zpatches, zrep_patches = self.sample_tex_patches(tex_idx, rgb_img, rep_rec[:1], seg)
-            # random offset
-            rmasks, rpatch_masks, rpatches, rrep_patches = self.sample_tex_patches(tex_idx, rgb_img, rep_rec[1:], seg)
-            masks = torch.cat((zmasks, rmasks))
-            patch_masks = torch.cat((zpatch_masks, rpatch_masks))
-            patches = torch.cat((zpatches, rpatches))
-            rep_patches = torch.cat((zrep_patches, rrep_patches))
-
-            # Gram matrix matching loss between:
-            # - patches from synthesized texture v.s. patches from input image
-            # - patches from reconstruction v.s. patches from input image
-            outputs['style_loss'] = self.style_loss.forward_patch_img(rep_patches, rgb_img.repeat(2, 1, 1, 1), masks)
-
-            outputs['rep_rec'] = rep_rec
-            outputs['masks'] = masks
-            outputs['patches'] = patches.view(-1, 3, self.patch_size, self.patch_size)
-            outputs['patch_masks'] = patch_masks
-            outputs['rep_patches'] = rep_patches * patch_masks + patches * (1 - patch_masks)
-
-            outputs['gt'] = rgb_img
-            bp_tex = rep_rec[:1, :, :imgH, :imgW] * masks[:1] + rgb_img * (1 - masks[:1])
-            outputs['rec'] = bp_tex
-
-        outputs['HA'] = torch.cat(seg_map)
-        return outputs
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Esser Tools 8000 66 [HOT].md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Esser Tools 8000 66 [HOT].md
deleted file mode 100644
index 157d062ff4911907d4ee69993b8385e8634e8fe2..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Esser Tools 8000 66 [HOT].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Esser tools 8000 66</h2><br /><p><b><b>Download File</b> &#9999; <a href="https://cinurl.com/2uEXNL">https://cinurl.com/2uEXNL</a></b></p><br /><br />
-
-Systems and Equipment Certification“, dated December 1992. For those applicants ... ETSO–2C66b. Distance Measuring ... Using a Kueffel and Esser. Type 5600 or ... percentile male (220 lbs) body weight, at sea level and 8000 feet altitude:. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visual Assist 10.9 Build 2341 Free Download [BEST].md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visual Assist 10.9 Build 2341 Free Download [BEST].md
deleted file mode 100644
index d06f482ec42e0185066d4f264499c92bc3cc9a98..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Visual Assist 10.9 Build 2341 Free Download [BEST].md	
+++ /dev/null
@@ -1,8 +0,0 @@
-
-<p>market analysis concluded that the new warnings would reduce in-store youth purchases by 59% to 76% ( australia ). three years later, after the new warnings were introduced, the prevalence of daily smoking among 14- to 17-year-olds fell by 4.9 percentage points (hajek et al. 2008). a study in norway found that use of cigarettes declined by 4.6 percentage points (hansen et al. 2008), and a study in the united kingdom found that the introduction of graphic warnings resulted in a significant decline in youth smoking rates ( richardson, et al. 2009). in all countries, the declines in youth smoking were larger for girls than for boys.</p>
-<p>as the plain packaging legislation was being developed in australia in the 1990s, it was foreseen that the introduction of such a law would be followed by a series of ever more comprehensive tobacco control measures. indeed, many of these additional policies were enacted in australia beginning in 2004. however, in that year, the australian government announced its intention to introduce plain packaging legislation by july 1, 2007. the legislation was passed on july 1, 2004 and came into force on july 1, 2005.</p>
-<h2>Visual Assist 10.9 Build 2341 Free Download</h2><br /><p><b><b>Download File</b> &#187;&#187;&#187; <a href="https://cinurl.com/2uEXYx">https://cinurl.com/2uEXYx</a></b></p><br /><br />
-<p>you can either choose unlimited tracks per download, which allows unlimited downloads at no additional cost per track, or you can choose the individual download limit. if you get more than the individual download limit, the excess number of tracks will not add to your download limit until you are finished downloading.</p>
-<p>it is easy to load any kontakt instrument from your desktop onto the iphone as long as you are connected to the internet and you have itunes installed. you can choose a folder that is synchronized with your device to save your kontakt instrument and drag and drop it from your computer to your idevice. after loading your kontakt instrument on your iphone, you can freely access it by scrolling up the track list. in addition, you can perform the following idevice functions using your iphone touch screen:</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/models3D/visualization.py b/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/models3D/visualization.py
deleted file mode 100644
index 49bf883d7194039d260ea1dfb70eecd2454c8fd1..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/data/models3D/visualization.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import argparse
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-def main():
-    # Input arguments control
-    pars = argparse.ArgumentParser(description='3D model visualization')
-    pars.add_argument('file', type=str, help='File txt path')
-    args = pars.parse_args()
-    visualize_3Dmodel(args.file)
-
-
-def visualize_3Dmodel(input_file):
-
-    with open(input_file) as f:
-        lines = f.readlines()
-
-    model = []
-    for line in lines:
-        line = line[:-1]    # Remove \n
-        line_split = line.split('|')
-        values = np.array(line_split, dtype=float)
-        model.append(values)
-
-    model = np.array(model)
-    model_xyz = model[:, 1:]
-
-    # Show model
-    fig = plt.figure()
-    ax = fig.add_subplot(111, projection='3d')
-    ax.scatter(model_xyz[:, 0], model_xyz[:, 1], model_xyz[:, 2]+0.8)
-    plt.show()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/models/cnn/coord_conv.py b/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/models/cnn/coord_conv.py
deleted file mode 100644
index 45e970ffd3a5b9b0d94870d4193afe64a1222b35..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/models/cnn/coord_conv.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class AddCoordsTh(nn.Module):
-    def __init__(self, x_dim=64, y_dim=64, with_r=False):
-        super(AddCoordsTh, self).__init__()
-        self.x_dim = x_dim
-        self.y_dim = y_dim
-        self.with_r = with_r
-
-        xx_channel, yy_channel = self._prepare_coords()
-        self.xx_channel = nn.parameter.Parameter(xx_channel, requires_grad=False)
-        self.yy_channel = nn.parameter.Parameter(yy_channel, requires_grad=False)
-
-    def _prepare_coords(self):
-        xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32)
-        xx_ones = xx_ones.unsqueeze(-1)
-
-        xx_range = torch.arange(self.x_dim, dtype=torch.int32).unsqueeze(0)
-        xx_range = xx_range.unsqueeze(1)
-
-        xx_channel = torch.matmul(xx_ones, xx_range)
-        xx_channel = xx_channel.unsqueeze(-1)
-
-        yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32)
-        yy_ones = yy_ones.unsqueeze(1)
-
-        yy_range = torch.arange(self.y_dim, dtype=torch.int32).unsqueeze(0)
-        yy_range = yy_range.unsqueeze(-1)
-
-        yy_channel = torch.matmul(yy_range, yy_ones)
-        yy_channel = yy_channel.unsqueeze(-1)
-
-        xx_channel = xx_channel.permute(0, 3, 2, 1)
-        yy_channel = yy_channel.permute(0, 3, 2, 1)
-
-        xx_channel = xx_channel.float() / (self.x_dim - 1)
-        yy_channel = yy_channel.float() / (self.y_dim - 1)
-
-        xx_channel = xx_channel * 2 - 1
-        yy_channel = yy_channel * 2 - 1
-        return xx_channel, yy_channel
-
-    def forward(self, input_tensor):
-        """
-        input_tensor: (batch, c, x_dim, y_dim)
-        """
-        batch_size_tensor = input_tensor.shape[0]
-        xx_channel = self.xx_channel.repeat(batch_size_tensor, 1, 1, 1)
-        yy_channel = self.yy_channel.repeat(batch_size_tensor, 1, 1, 1)
-        ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1)
-
-        if self.with_r:
-            rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2))
-            ret = torch.cat([ret, rr], dim=1)
-
-        return ret
diff --git a/spaces/taesiri/DeticChatGPT/detic/data/custom_dataset_mapper.py b/spaces/taesiri/DeticChatGPT/detic/data/custom_dataset_mapper.py
deleted file mode 100644
index c7727dded3f93f5eeafdcd72e257197e3fdc817b..0000000000000000000000000000000000000000
--- a/spaces/taesiri/DeticChatGPT/detic/data/custom_dataset_mapper.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import logging
-import numpy as np
-from typing import List, Optional, Union
-import torch
-import pycocotools.mask as mask_util
-
-from detectron2.config import configurable
-
-from detectron2.data import detection_utils as utils
-from detectron2.data.detection_utils import transform_keypoint_annotations
-from detectron2.data import transforms as T
-from detectron2.data.dataset_mapper import DatasetMapper
-from detectron2.structures import Boxes, BoxMode, Instances
-from detectron2.structures import Keypoints, PolygonMasks, BitMasks
-from fvcore.transforms.transform import TransformList
-from .custom_build_augmentation import build_custom_augmentation
-from .tar_dataset import DiskTarDataset
-
-__all__ = ["CustomDatasetMapper"]
-
-class CustomDatasetMapper(DatasetMapper):
-    @configurable
-    def __init__(self, is_train: bool, 
-        with_ann_type=False,
-        dataset_ann=[],
-        use_diff_bs_size=False,
-        dataset_augs=[],
-        is_debug=False,
-        use_tar_dataset=False,
-        tarfile_path='',
-        tar_index_dir='',
-        **kwargs):
-        """
-        add image labels
-        """
-        self.with_ann_type = with_ann_type
-        self.dataset_ann = dataset_ann
-        self.use_diff_bs_size = use_diff_bs_size
-        if self.use_diff_bs_size and is_train:
-            self.dataset_augs = [T.AugmentationList(x) for x in dataset_augs]
-        self.is_debug = is_debug
-        self.use_tar_dataset = use_tar_dataset
-        if self.use_tar_dataset:
-            print('Using tar dataset')
-            self.tar_dataset = DiskTarDataset(tarfile_path, tar_index_dir)
-        super().__init__(is_train, **kwargs)
- 
-
-    @classmethod
-    def from_config(cls, cfg, is_train: bool = True):
-        ret = super().from_config(cfg, is_train)
-        ret.update({
-            'with_ann_type': cfg.WITH_IMAGE_LABELS,
-            'dataset_ann': cfg.DATALOADER.DATASET_ANN,
-            'use_diff_bs_size': cfg.DATALOADER.USE_DIFF_BS_SIZE,
-            'is_debug': cfg.IS_DEBUG,
-            'use_tar_dataset': cfg.DATALOADER.USE_TAR_DATASET,
-            'tarfile_path': cfg.DATALOADER.TARFILE_PATH,
-            'tar_index_dir': cfg.DATALOADER.TAR_INDEX_DIR,
-        })
-        if ret['use_diff_bs_size'] and is_train:
-            if cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop':
-                dataset_scales = cfg.DATALOADER.DATASET_INPUT_SCALE
-                dataset_sizes = cfg.DATALOADER.DATASET_INPUT_SIZE
-                ret['dataset_augs'] = [
-                    build_custom_augmentation(cfg, True, scale, size) \
-                        for scale, size in zip(dataset_scales, dataset_sizes)]
-            else:
-                assert cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge'
-                min_sizes = cfg.DATALOADER.DATASET_MIN_SIZES
-                max_sizes = cfg.DATALOADER.DATASET_MAX_SIZES
-                ret['dataset_augs'] = [
-                    build_custom_augmentation(
-                        cfg, True, min_size=mi, max_size=ma) \
-                        for mi, ma in zip(min_sizes, max_sizes)]
-        else:
-            ret['dataset_augs'] = []
-
-        return ret
-
-    def __call__(self, dataset_dict):
-        """
-        include image labels
-        """
-        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
-        # USER: Write your own image loading if it's not from a file
-        if 'file_name' in dataset_dict:
-            ori_image = utils.read_image(
-                dataset_dict["file_name"], format=self.image_format)
-        else:
-            ori_image, _, _ = self.tar_dataset[dataset_dict["tar_index"]]
-            ori_image = utils._apply_exif_orientation(ori_image)
-            ori_image = utils.convert_PIL_to_numpy(ori_image, self.image_format)
-        utils.check_image_size(dataset_dict, ori_image)
-
-        # USER: Remove if you don't do semantic/panoptic segmentation.
-        if "sem_seg_file_name" in dataset_dict:
-            sem_seg_gt = utils.read_image(
-                dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
-        else:
-            sem_seg_gt = None
-
-        if self.is_debug:
-            dataset_dict['dataset_source'] = 0
-
-        not_full_labeled = 'dataset_source' in dataset_dict and \
-            self.with_ann_type and \
-                self.dataset_ann[dataset_dict['dataset_source']] != 'box'
-
-        aug_input = T.AugInput(copy.deepcopy(ori_image), sem_seg=sem_seg_gt)
-        if self.use_diff_bs_size and self.is_train:
-            transforms = \
-                self.dataset_augs[dataset_dict['dataset_source']](aug_input)
-        else:
-            transforms = self.augmentations(aug_input)
-        image, sem_seg_gt = aug_input.image, aug_input.sem_seg
-
-        image_shape = image.shape[:2]  # h, w
-        dataset_dict["image"] = torch.as_tensor(
-            np.ascontiguousarray(image.transpose(2, 0, 1)))
-        
-        if sem_seg_gt is not None:
-            dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
-
-        # USER: Remove if you don't use pre-computed proposals.
-        # Most users would not need this feature.
-        if self.proposal_topk is not None:
-            utils.transform_proposals(
-                dataset_dict, image_shape, transforms, 
-                proposal_topk=self.proposal_topk
-            )
-
-        if not self.is_train:
-            # USER: Modify this if you want to keep them for some reason.
-            dataset_dict.pop("annotations", None)
-            dataset_dict.pop("sem_seg_file_name", None)
-            return dataset_dict
-
-        if "annotations" in dataset_dict:
-            # USER: Modify this if you want to keep them for some reason.
-            for anno in dataset_dict["annotations"]:
-                if not self.use_instance_mask:
-                    anno.pop("segmentation", None)
-                if not self.use_keypoint:
-                    anno.pop("keypoints", None)
-
-            # USER: Implement additional transformations if you have other types of data
-            all_annos = [
-                (utils.transform_instance_annotations(
-                    obj, transforms, image_shape, 
-                    keypoint_hflip_indices=self.keypoint_hflip_indices,
-                ),  obj.get("iscrowd", 0))
-                for obj in dataset_dict.pop("annotations")
-            ]
-            annos = [ann[0] for ann in all_annos if ann[1] == 0]
-            instances = utils.annotations_to_instances(
-                annos, image_shape, mask_format=self.instance_mask_format
-            )
-            
-            del all_annos
-            if self.recompute_boxes:
-                instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
-            dataset_dict["instances"] = utils.filter_empty_instances(instances)
-        if self.with_ann_type:
-            dataset_dict["pos_category_ids"] = dataset_dict.get(
-                'pos_category_ids', [])
-            dataset_dict["ann_type"] = \
-                self.dataset_ann[dataset_dict['dataset_source']]
-        if self.is_debug and (('pos_category_ids' not in dataset_dict) or \
-            (dataset_dict['pos_category_ids'] == [])):
-            dataset_dict['pos_category_ids'] = [x for x in sorted(set(
-                dataset_dict['instances'].gt_classes.tolist()
-            ))]
-        return dataset_dict
-
-# DETR augmentation
-def build_transform_gen(cfg, is_train):
-    """
-    """
-    if is_train:
-        min_size = cfg.INPUT.MIN_SIZE_TRAIN
-        max_size = cfg.INPUT.MAX_SIZE_TRAIN
-        sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
-    else:
-        min_size = cfg.INPUT.MIN_SIZE_TEST
-        max_size = cfg.INPUT.MAX_SIZE_TEST
-        sample_style = "choice"
-    if sample_style == "range":
-        assert len(min_size) == 2, "more than 2 ({}) min_size(s) are provided for ranges".format(len(min_size))
-
-    logger = logging.getLogger(__name__)
-    tfm_gens = []
-    if is_train:
-        tfm_gens.append(T.RandomFlip())
-    tfm_gens.append(T.ResizeShortestEdge(min_size, max_size, sample_style))
-    if is_train:
-        logger.info("TransformGens used in training: " + str(tfm_gens))
-    return tfm_gens
-
-
-class DetrDatasetMapper:
-    """
-    A callable which takes a dataset dict in Detectron2 Dataset format,
-    and map it into a format used by DETR.
-    The callable currently does the following:
-    1. Read the image from "file_name"
-    2. Applies geometric transforms to the image and annotation
-    3. Find and applies suitable cropping to the image and annotation
-    4. Prepare image and annotation to Tensors
-    """
-
-    def __init__(self, cfg, is_train=True):
-        if cfg.INPUT.CROP.ENABLED and is_train:
-            self.crop_gen = [
-                T.ResizeShortestEdge([400, 500, 600], sample_style="choice"),
-                T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE),
-            ]
-        else:
-            self.crop_gen = None
-
-        self.mask_on = cfg.MODEL.MASK_ON
-        self.tfm_gens = build_transform_gen(cfg, is_train)
-        logging.getLogger(__name__).info(
-            "Full TransformGens used in training: {}, crop: {}".format(str(self.tfm_gens), str(self.crop_gen))
-        )
-
-        self.img_format = cfg.INPUT.FORMAT
-        self.is_train = is_train
-
-    def __call__(self, dataset_dict):
-        """
-        Args:
-            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
-        Returns:
-            dict: a format that builtin models in detectron2 accept
-        """
-        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
-        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
-        utils.check_image_size(dataset_dict, image)
-
-        if self.crop_gen is None:
-            image, transforms = T.apply_transform_gens(self.tfm_gens, image)
-        else:
-            if np.random.rand() > 0.5:
-                image, transforms = T.apply_transform_gens(self.tfm_gens, image)
-            else:
-                image, transforms = T.apply_transform_gens(
-                    self.tfm_gens[:-1] + self.crop_gen + self.tfm_gens[-1:], image
-                )
-
-        image_shape = image.shape[:2]  # h, w
-
-        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
-        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
-        # Therefore it's important to use torch.Tensor.
-        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
-
-        if not self.is_train:
-            # USER: Modify this if you want to keep them for some reason.
-            dataset_dict.pop("annotations", None)
-            return dataset_dict
-
-        if "annotations" in dataset_dict:
-            # USER: Modify this if you want to keep them for some reason.
-            for anno in dataset_dict["annotations"]:
-                if not self.mask_on:
-                    anno.pop("segmentation", None)
-                anno.pop("keypoints", None)
-
-            # USER: Implement additional transformations if you have other types of data
-            annos = [
-                utils.transform_instance_annotations(obj, transforms, image_shape)
-                for obj in dataset_dict.pop("annotations")
-                if obj.get("iscrowd", 0) == 0
-            ]
-            instances = utils.annotations_to_instances(annos, image_shape)
-            dataset_dict["instances"] = utils.filter_empty_instances(instances)
-        return dataset_dict
\ No newline at end of file
diff --git a/spaces/tammm/vits-models/text/symbols.py b/spaces/tammm/vits-models/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/tammm/vits-models/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/team-ai-law-assistant/CUAD/app.py b/spaces/team-ai-law-assistant/CUAD/app.py
deleted file mode 100644
index 97bb69d9430b211ada51d5a99f8e520bb20132df..0000000000000000000000000000000000000000
--- a/spaces/team-ai-law-assistant/CUAD/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from transformers import (
-    AutoConfig,
-    AutoModelForQuestionAnswering,
-    AutoTokenizer,
-    squad_convert_examples_to_features
-)
-
-from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
-from transformers.data.metrics.squad_metrics import compute_predictions_logits
-import streamlit as st
-import gradio as gr
-import json
-import torch
-import time
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
-
-model_checkpoint = "akdeniz27/roberta-base-cuad"
-
-def run_prediction(question_texts, context_text, model_path):
-    max_seq_length = 512
-    doc_stride = 256
-    n_best_size = 1
-    max_query_length = 64
-    max_answer_length = 512
-    do_lower_case = False
-    null_score_diff_threshold = 0.0
-    def to_list(tensor):
-        return tensor.detach().cpu().tolist()
-    config_class, model_class, tokenizer_class = (
-        AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer)
-    config = config_class.from_pretrained(model_path)
-    tokenizer = tokenizer_class.from_pretrained(
-        model_path, do_lower_case=True, use_fast=False)
-    model = model_class.from_pretrained(model_path, config=config)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.to(device)
-    processor = SquadV2Processor()
-    examples = []
-    for i, question_text in enumerate(question_texts):
-        example = SquadExample(
-            qas_id=str(i),
-            question_text=question_text,
-            context_text=context_text,
-            answer_text=None,
-            start_position_character=None,
-            title="Predict",
-            answers=None,
-        )
-        examples.append(example)
-    features, dataset = squad_convert_examples_to_features(
-        examples=examples,
-        tokenizer=tokenizer,
-        max_seq_length=max_seq_length,
-        doc_stride=doc_stride,
-        max_query_length=max_query_length,
-        is_training=False,
-        return_dataset="pt",
-        threads=1,
-    )
-    eval_sampler = SequentialSampler(dataset)
-    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
-    all_results = []
-    for batch in eval_dataloader:
-        model.eval()
-        batch = tuple(t.to(device) for t in batch)
-        with torch.no_grad():
-            inputs = {
-                "input_ids": batch[0],
-                "attention_mask": batch[1],
-                "token_type_ids": batch[2],
-            }
-            example_indices = batch[3]
-            outputs = model(**inputs)
-            for i, example_index in enumerate(example_indices):
-                eval_feature = features[example_index.item()]
-                unique_id = int(eval_feature.unique_id)
-                output = [to_list(output[i]) for output in outputs.to_tuple()]
-                start_logits, end_logits = output
-                result = SquadResult(unique_id, start_logits, end_logits)
-                all_results.append(result)
-    final_predictions = compute_predictions_logits(
-        all_examples=examples,
-        all_features=features,
-        all_results=all_results,
-        n_best_size=n_best_size,
-        max_answer_length=max_answer_length,
-        do_lower_case=do_lower_case,
-        output_prediction_file=None,
-        output_nbest_file=None,
-        output_null_log_odds_file=None,
-        verbose_logging=False,
-        version_2_with_negative=True,
-        null_score_diff_threshold=null_score_diff_threshold,
-        tokenizer=tokenizer
-    )
-    return final_predictions
-
-@st.cache(allow_output_mutation=True)
-def load_model():
-    model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
-    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint , use_fast=False)
-    return model, tokenizer
-    
-@st.cache(allow_output_mutation=True)
-def load_questions():
-	with open('test.json') as json_file:
-		data = json.load(json_file)
-	questions = []
-	for i, q in enumerate(data['data'][0]['paragraphs'][0]['qas']):
-		question = data['data'][0]['paragraphs'][0]['qas'][i]['question']
-		questions.append(question)
-	return questions
-	
-@st.cache(allow_output_mutation=True)
-def load_contracts():
-	with open('test.json') as json_file:
-		data = json.load(json_file)
-	contracts = []
-	for i, q in enumerate(data['data']):
-		contract = ' '.join(data['data'][i]['paragraphs'][0]['context'].split())
-		contracts.append(contract)
-	return contracts
-	
-model, tokenizer = load_model()
-questions = load_questions()
-contracts = load_contracts()
-contract = contracts[0]
-
-st.header("📚 Question Answering in Contract Understanding Atticus Dataset (CUAD)")
-st.image("contract_review.png")
-
-selected_question = st.selectbox('📑 Choose one of the queries from the CUAD dataset or 📝 write a legal contract and see if the model can answer correctly: ', questions)
-
-question_set = [questions[0], selected_question]
-contract_type = st.radio("Select Contract", ("Sample Contract", "New Contract"))
-
-if contract_type == "Sample Contract":
-	sample_contract_num = st.slider("Select Sample Contract #")
-	contract = contracts[sample_contract_num]
-	with st.expander(f"Sample Contract #{sample_contract_num}"):
-		st.write(contract)
-else:
-	contract = st.text_area("Input New Contract", "", height=256)
-Run_Button = st.button("Run", key=None)
-if Run_Button == True and not len(contract)==0 and not len(question_set)==0:
-	predictions = run_prediction(question_set, contract, 'akdeniz27/roberta-base-cuad')
-	
-	for i, p in enumerate(predictions):
-		if i != 0: st.write(f"Question: {question_set[int(p)]}\n\nAnswer: {predictions[p]}\n\n")
-
-
-st.write("🤗")
-st.write("Based on Streamlit code of https://huggingface.co/spaces/akdeniz27/contract-understanding-atticus-dataset-demo")
-st.write("Model: akdeniz27/roberta-base-cuad")
-st.write("Project: https://www.atticusprojectai.org/cuad")
\ No newline at end of file
diff --git a/spaces/techguy1423/ChatABT0.4/README.md b/spaces/techguy1423/ChatABT0.4/README.md
deleted file mode 100644
index 1bdbc6726ad15737ec8065d6b3a84f5c941696f8..0000000000000000000000000000000000000000
--- a/spaces/techguy1423/ChatABT0.4/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ChatABT0.4
-emoji: 😻
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Blackberry Smart Tool V1.0.0.1089 Louisse Edition.27 VERIFIED.md b/spaces/terfces0erbo/CollegeProjectV2/Blackberry Smart Tool V1.0.0.1089 Louisse Edition.27 VERIFIED.md
deleted file mode 100644
index 413063d813414fd501dbbf23e8289dd4984f2ca6..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Blackberry Smart Tool V1.0.0.1089 Louisse Edition.27 VERIFIED.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Bootstrap Studio 4.5.8 Crack License Key Full ((BETTER)) Download 2020.md b/spaces/terfces0erbo/CollegeProjectV2/Bootstrap Studio 4.5.8 Crack License Key Full ((BETTER)) Download 2020.md
deleted file mode 100644
index add46746c57c3748f270d584ad7b39f6d8e83c66..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Bootstrap Studio 4.5.8 Crack License Key Full ((BETTER)) Download 2020.md	
+++ /dev/null
@@ -1,108 +0,0 @@
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/College Physics 10th Edition Serway And Vuille Pdf Download [TOP].md b/spaces/terfces0erbo/CollegeProjectV2/College Physics 10th Edition Serway And Vuille Pdf Download [TOP].md
deleted file mode 100644
index ac8cc5fda0ec6c5e644856e1a5eddbb71fc199fc..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/College Physics 10th Edition Serway And Vuille Pdf Download [TOP].md	
+++ /dev/null
@@ -1,10 +0,0 @@
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Jb V Ray Studiotools V1 2 Rar EXCLUSIVE.md b/spaces/terfces0erbo/CollegeProjectV2/Jb V Ray Studiotools V1 2 Rar EXCLUSIVE.md
deleted file mode 100644
index 7ad4455c022b8bd19726cb5045f01880f5a007ed..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Jb V Ray Studiotools V1 2 Rar EXCLUSIVE.md	
+++ /dev/null
@@ -1,28 +0,0 @@
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diff --git a/spaces/thejagstudio/procom/main/migrations/0005_alter_products_name_alter_products_score.py b/spaces/thejagstudio/procom/main/migrations/0005_alter_products_name_alter_products_score.py
deleted file mode 100644
index c2b4536b37956be3894f9b65d8e33a49f38b664e..0000000000000000000000000000000000000000
--- a/spaces/thejagstudio/procom/main/migrations/0005_alter_products_name_alter_products_score.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Generated by Django 4.1.5 on 2023-01-28 06:12
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    dependencies = [
-        ("main", "0004_alter_products_category"),
-    ]
-
-    operations = [
-        migrations.AlterField(
-            model_name="products",
-            name="name",
-            field=models.CharField(max_length=500, unique=True),
-        ),
-        migrations.AlterField(
-            model_name="products",
-            name="score",
-            field=models.IntegerField(null=True),
-        ),
-    ]
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Free Books Download In Pdf Harlequin Presents - TOP.md b/spaces/tioseFevbu/cartoon-converter/scripts/Free Books Download In Pdf Harlequin Presents - TOP.md
deleted file mode 100644
index 2e9f5dcd96b76bd76e21985a08a4ea383a5a131d..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Free Books Download In Pdf Harlequin Presents - TOP.md	
+++ /dev/null
@@ -1,21 +0,0 @@
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/James Franco Roast 720p Mkvl.md b/spaces/tioseFevbu/cartoon-converter/scripts/James Franco Roast 720p Mkvl.md
deleted file mode 100644
index c7e0916824f5db1c2612b3a507763ad71b2a0def..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/James Franco Roast 720p Mkvl.md	
+++ /dev/null
@@ -1,14 +0,0 @@
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-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/version.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/version.py
deleted file mode 100644
index c7c8bb6ff4f8ed84e466a66cac6b953b901626ea..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/version.py
+++ /dev/null
@@ -1,739 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2012-2017 The Python Software Foundation.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-"""
-Implementation of a flexible versioning scheme providing support for PEP-440,
-setuptools-compatible and semantic versioning.
-"""
-
-import logging
-import re
-
-from .compat import string_types
-from .util import parse_requirement
-
-__all__ = ['NormalizedVersion', 'NormalizedMatcher',
-           'LegacyVersion', 'LegacyMatcher',
-           'SemanticVersion', 'SemanticMatcher',
-           'UnsupportedVersionError', 'get_scheme']
-
-logger = logging.getLogger(__name__)
-
-
-class UnsupportedVersionError(ValueError):
-    """This is an unsupported version."""
-    pass
-
-
-class Version(object):
-    def __init__(self, s):
-        self._string = s = s.strip()
-        self._parts = parts = self.parse(s)
-        assert isinstance(parts, tuple)
-        assert len(parts) > 0
-
-    def parse(self, s):
-        raise NotImplementedError('please implement in a subclass')
-
-    def _check_compatible(self, other):
-        if type(self) != type(other):
-            raise TypeError('cannot compare %r and %r' % (self, other))
-
-    def __eq__(self, other):
-        self._check_compatible(other)
-        return self._parts == other._parts
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __lt__(self, other):
-        self._check_compatible(other)
-        return self._parts < other._parts
-
-    def __gt__(self, other):
-        return not (self.__lt__(other) or self.__eq__(other))
-
-    def __le__(self, other):
-        return self.__lt__(other) or self.__eq__(other)
-
-    def __ge__(self, other):
-        return self.__gt__(other) or self.__eq__(other)
-
-    # See http://docs.python.org/reference/datamodel#object.__hash__
-    def __hash__(self):
-        return hash(self._parts)
-
-    def __repr__(self):
-        return "%s('%s')" % (self.__class__.__name__, self._string)
-
-    def __str__(self):
-        return self._string
-
-    @property
-    def is_prerelease(self):
-        raise NotImplementedError('Please implement in subclasses.')
-
-
-class Matcher(object):
-    version_class = None
-
-    # value is either a callable or the name of a method
-    _operators = {
-        '<': lambda v, c, p: v < c,
-        '>': lambda v, c, p: v > c,
-        '<=': lambda v, c, p: v == c or v < c,
-        '>=': lambda v, c, p: v == c or v > c,
-        '==': lambda v, c, p: v == c,
-        '===': lambda v, c, p: v == c,
-        # by default, compatible => >=.
-        '~=': lambda v, c, p: v == c or v > c,
-        '!=': lambda v, c, p: v != c,
-    }
-
-    # this is a method only to support alternative implementations
-    # via overriding
-    def parse_requirement(self, s):
-        return parse_requirement(s)
-
-    def __init__(self, s):
-        if self.version_class is None:
-            raise ValueError('Please specify a version class')
-        self._string = s = s.strip()
-        r = self.parse_requirement(s)
-        if not r:
-            raise ValueError('Not valid: %r' % s)
-        self.name = r.name
-        self.key = self.name.lower()    # for case-insensitive comparisons
-        clist = []
-        if r.constraints:
-            # import pdb; pdb.set_trace()
-            for op, s in r.constraints:
-                if s.endswith('.*'):
-                    if op not in ('==', '!='):
-                        raise ValueError('\'.*\' not allowed for '
-                                         '%r constraints' % op)
-                    # Could be a partial version (e.g. for '2.*') which
-                    # won't parse as a version, so keep it as a string
-                    vn, prefix = s[:-2], True
-                    # Just to check that vn is a valid version
-                    self.version_class(vn)
-                else:
-                    # Should parse as a version, so we can create an
-                    # instance for the comparison
-                    vn, prefix = self.version_class(s), False
-                clist.append((op, vn, prefix))
-        self._parts = tuple(clist)
-
-    def match(self, version):
-        """
-        Check if the provided version matches the constraints.
-
-        :param version: The version to match against this instance.
-        :type version: String or :class:`Version` instance.
-        """
-        if isinstance(version, string_types):
-            version = self.version_class(version)
-        for operator, constraint, prefix in self._parts:
-            f = self._operators.get(operator)
-            if isinstance(f, string_types):
-                f = getattr(self, f)
-            if not f:
-                msg = ('%r not implemented '
-                       'for %s' % (operator, self.__class__.__name__))
-                raise NotImplementedError(msg)
-            if not f(version, constraint, prefix):
-                return False
-        return True
-
-    @property
-    def exact_version(self):
-        result = None
-        if len(self._parts) == 1 and self._parts[0][0] in ('==', '==='):
-            result = self._parts[0][1]
-        return result
-
-    def _check_compatible(self, other):
-        if type(self) != type(other) or self.name != other.name:
-            raise TypeError('cannot compare %s and %s' % (self, other))
-
-    def __eq__(self, other):
-        self._check_compatible(other)
-        return self.key == other.key and self._parts == other._parts
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    # See http://docs.python.org/reference/datamodel#object.__hash__
-    def __hash__(self):
-        return hash(self.key) + hash(self._parts)
-
-    def __repr__(self):
-        return "%s(%r)" % (self.__class__.__name__, self._string)
-
-    def __str__(self):
-        return self._string
-
-
-PEP440_VERSION_RE = re.compile(r'^v?(\d+!)?(\d+(\.\d+)*)((a|b|c|rc)(\d+))?'
-                               r'(\.(post)(\d+))?(\.(dev)(\d+))?'
-                               r'(\+([a-zA-Z\d]+(\.[a-zA-Z\d]+)?))?$')
-
-
-def _pep_440_key(s):
-    s = s.strip()
-    m = PEP440_VERSION_RE.match(s)
-    if not m:
-        raise UnsupportedVersionError('Not a valid version: %s' % s)
-    groups = m.groups()
-    nums = tuple(int(v) for v in groups[1].split('.'))
-    while len(nums) > 1 and nums[-1] == 0:
-        nums = nums[:-1]
-
-    if not groups[0]:
-        epoch = 0
-    else:
-        epoch = int(groups[0][:-1])
-    pre = groups[4:6]
-    post = groups[7:9]
-    dev = groups[10:12]
-    local = groups[13]
-    if pre == (None, None):
-        pre = ()
-    else:
-        pre = pre[0], int(pre[1])
-    if post == (None, None):
-        post = ()
-    else:
-        post = post[0], int(post[1])
-    if dev == (None, None):
-        dev = ()
-    else:
-        dev = dev[0], int(dev[1])
-    if local is None:
-        local = ()
-    else:
-        parts = []
-        for part in local.split('.'):
-            # to ensure that numeric compares as > lexicographic, avoid
-            # comparing them directly, but encode a tuple which ensures
-            # correct sorting
-            if part.isdigit():
-                part = (1, int(part))
-            else:
-                part = (0, part)
-            parts.append(part)
-        local = tuple(parts)
-    if not pre:
-        # either before pre-release, or final release and after
-        if not post and dev:
-            # before pre-release
-            pre = ('a', -1)     # to sort before a0
-        else:
-            pre = ('z',)        # to sort after all pre-releases
-    # now look at the state of post and dev.
-    if not post:
-        post = ('_',)   # sort before 'a'
-    if not dev:
-        dev = ('final',)
-
-    #print('%s -> %s' % (s, m.groups()))
-    return epoch, nums, pre, post, dev, local
-
-
-_normalized_key = _pep_440_key
-
-
-class NormalizedVersion(Version):
-    """A rational version.
-
-    Good:
-        1.2         # equivalent to "1.2.0"
-        1.2.0
-        1.2a1
-        1.2.3a2
-        1.2.3b1
-        1.2.3c1
-        1.2.3.4
-        TODO: fill this out
-
-    Bad:
-        1           # minimum two numbers
-        1.2a        # release level must have a release serial
-        1.2.3b
-    """
-    def parse(self, s):
-        result = _normalized_key(s)
-        # _normalized_key loses trailing zeroes in the release
-        # clause, since that's needed to ensure that X.Y == X.Y.0 == X.Y.0.0
-        # However, PEP 440 prefix matching needs it: for example,
-        # (~= 1.4.5.0) matches differently to (~= 1.4.5.0.0).
-        m = PEP440_VERSION_RE.match(s)      # must succeed
-        groups = m.groups()
-        self._release_clause = tuple(int(v) for v in groups[1].split('.'))
-        return result
-
-    PREREL_TAGS = set(['a', 'b', 'c', 'rc', 'dev'])
-
-    @property
-    def is_prerelease(self):
-        return any(t[0] in self.PREREL_TAGS for t in self._parts if t)
-
-
-def _match_prefix(x, y):
-    x = str(x)
-    y = str(y)
-    if x == y:
-        return True
-    if not x.startswith(y):
-        return False
-    n = len(y)
-    return x[n] == '.'
-
-
-class NormalizedMatcher(Matcher):
-    version_class = NormalizedVersion
-
-    # value is either a callable or the name of a method
-    _operators = {
-        '~=': '_match_compatible',
-        '<': '_match_lt',
-        '>': '_match_gt',
-        '<=': '_match_le',
-        '>=': '_match_ge',
-        '==': '_match_eq',
-        '===': '_match_arbitrary',
-        '!=': '_match_ne',
-    }
-
-    def _adjust_local(self, version, constraint, prefix):
-        if prefix:
-            strip_local = '+' not in constraint and version._parts[-1]
-        else:
-            # both constraint and version are
-            # NormalizedVersion instances.
-            # If constraint does not have a local component,
-            # ensure the version doesn't, either.
-            strip_local = not constraint._parts[-1] and version._parts[-1]
-        if strip_local:
-            s = version._string.split('+', 1)[0]
-            version = self.version_class(s)
-        return version, constraint
-
-    def _match_lt(self, version, constraint, prefix):
-        version, constraint = self._adjust_local(version, constraint, prefix)
-        if version >= constraint:
-            return False
-        release_clause = constraint._release_clause
-        pfx = '.'.join([str(i) for i in release_clause])
-        return not _match_prefix(version, pfx)
-
-    def _match_gt(self, version, constraint, prefix):
-        version, constraint = self._adjust_local(version, constraint, prefix)
-        if version <= constraint:
-            return False
-        release_clause = constraint._release_clause
-        pfx = '.'.join([str(i) for i in release_clause])
-        return not _match_prefix(version, pfx)
-
-    def _match_le(self, version, constraint, prefix):
-        version, constraint = self._adjust_local(version, constraint, prefix)
-        return version <= constraint
-
-    def _match_ge(self, version, constraint, prefix):
-        version, constraint = self._adjust_local(version, constraint, prefix)
-        return version >= constraint
-
-    def _match_eq(self, version, constraint, prefix):
-        version, constraint = self._adjust_local(version, constraint, prefix)
-        if not prefix:
-            result = (version == constraint)
-        else:
-            result = _match_prefix(version, constraint)
-        return result
-
-    def _match_arbitrary(self, version, constraint, prefix):
-        return str(version) == str(constraint)
-
-    def _match_ne(self, version, constraint, prefix):
-        version, constraint = self._adjust_local(version, constraint, prefix)
-        if not prefix:
-            result = (version != constraint)
-        else:
-            result = not _match_prefix(version, constraint)
-        return result
-
-    def _match_compatible(self, version, constraint, prefix):
-        version, constraint = self._adjust_local(version, constraint, prefix)
-        if version == constraint:
-            return True
-        if version < constraint:
-            return False
-#        if not prefix:
-#            return True
-        release_clause = constraint._release_clause
-        if len(release_clause) > 1:
-            release_clause = release_clause[:-1]
-        pfx = '.'.join([str(i) for i in release_clause])
-        return _match_prefix(version, pfx)
-
-_REPLACEMENTS = (
-    (re.compile('[.+-]$'), ''),                     # remove trailing puncts
-    (re.compile(r'^[.](\d)'), r'0.\1'),             # .N -> 0.N at start
-    (re.compile('^[.-]'), ''),                      # remove leading puncts
-    (re.compile(r'^\((.*)\)$'), r'\1'),             # remove parentheses
-    (re.compile(r'^v(ersion)?\s*(\d+)'), r'\2'),    # remove leading v(ersion)
-    (re.compile(r'^r(ev)?\s*(\d+)'), r'\2'),        # remove leading v(ersion)
-    (re.compile('[.]{2,}'), '.'),                   # multiple runs of '.'
-    (re.compile(r'\b(alfa|apha)\b'), 'alpha'),      # misspelt alpha
-    (re.compile(r'\b(pre-alpha|prealpha)\b'),
-                'pre.alpha'),                       # standardise
-    (re.compile(r'\(beta\)$'), 'beta'),             # remove parentheses
-)
-
-_SUFFIX_REPLACEMENTS = (
-    (re.compile('^[:~._+-]+'), ''),                   # remove leading puncts
-    (re.compile('[,*")([\\]]'), ''),                  # remove unwanted chars
-    (re.compile('[~:+_ -]'), '.'),                    # replace illegal chars
-    (re.compile('[.]{2,}'), '.'),                   # multiple runs of '.'
-    (re.compile(r'\.$'), ''),                       # trailing '.'
-)
-
-_NUMERIC_PREFIX = re.compile(r'(\d+(\.\d+)*)')
-
-
-def _suggest_semantic_version(s):
-    """
-    Try to suggest a semantic form for a version for which
-    _suggest_normalized_version couldn't come up with anything.
-    """
-    result = s.strip().lower()
-    for pat, repl in _REPLACEMENTS:
-        result = pat.sub(repl, result)
-    if not result:
-        result = '0.0.0'
-
-    # Now look for numeric prefix, and separate it out from
-    # the rest.
-    #import pdb; pdb.set_trace()
-    m = _NUMERIC_PREFIX.match(result)
-    if not m:
-        prefix = '0.0.0'
-        suffix = result
-    else:
-        prefix = m.groups()[0].split('.')
-        prefix = [int(i) for i in prefix]
-        while len(prefix) < 3:
-            prefix.append(0)
-        if len(prefix) == 3:
-            suffix = result[m.end():]
-        else:
-            suffix = '.'.join([str(i) for i in prefix[3:]]) + result[m.end():]
-            prefix = prefix[:3]
-        prefix = '.'.join([str(i) for i in prefix])
-        suffix = suffix.strip()
-    if suffix:
-        #import pdb; pdb.set_trace()
-        # massage the suffix.
-        for pat, repl in _SUFFIX_REPLACEMENTS:
-            suffix = pat.sub(repl, suffix)
-
-    if not suffix:
-        result = prefix
-    else:
-        sep = '-' if 'dev' in suffix else '+'
-        result = prefix + sep + suffix
-    if not is_semver(result):
-        result = None
-    return result
-
-
-def _suggest_normalized_version(s):
-    """Suggest a normalized version close to the given version string.
-
-    If you have a version string that isn't rational (i.e. NormalizedVersion
-    doesn't like it) then you might be able to get an equivalent (or close)
-    rational version from this function.
-
-    This does a number of simple normalizations to the given string, based
-    on observation of versions currently in use on PyPI. Given a dump of
-    those version during PyCon 2009, 4287 of them:
-    - 2312 (53.93%) match NormalizedVersion without change
-      with the automatic suggestion
-    - 3474 (81.04%) match when using this suggestion method
-
-    @param s {str} An irrational version string.
-    @returns A rational version string, or None, if couldn't determine one.
-    """
-    try:
-        _normalized_key(s)
-        return s   # already rational
-    except UnsupportedVersionError:
-        pass
-
-    rs = s.lower()
-
-    # part of this could use maketrans
-    for orig, repl in (('-alpha', 'a'), ('-beta', 'b'), ('alpha', 'a'),
-                       ('beta', 'b'), ('rc', 'c'), ('-final', ''),
-                       ('-pre', 'c'),
-                       ('-release', ''), ('.release', ''), ('-stable', ''),
-                       ('+', '.'), ('_', '.'), (' ', ''), ('.final', ''),
-                       ('final', '')):
-        rs = rs.replace(orig, repl)
-
-    # if something ends with dev or pre, we add a 0
-    rs = re.sub(r"pre$", r"pre0", rs)
-    rs = re.sub(r"dev$", r"dev0", rs)
-
-    # if we have something like "b-2" or "a.2" at the end of the
-    # version, that is probably beta, alpha, etc
-    # let's remove the dash or dot
-    rs = re.sub(r"([abc]|rc)[\-\.](\d+)$", r"\1\2", rs)
-
-    # 1.0-dev-r371 -> 1.0.dev371
-    # 0.1-dev-r79 -> 0.1.dev79
-    rs = re.sub(r"[\-\.](dev)[\-\.]?r?(\d+)$", r".\1\2", rs)
-
-    # Clean: 2.0.a.3, 2.0.b1, 0.9.0~c1
-    rs = re.sub(r"[.~]?([abc])\.?", r"\1", rs)
-
-    # Clean: v0.3, v1.0
-    if rs.startswith('v'):
-        rs = rs[1:]
-
-    # Clean leading '0's on numbers.
-    #TODO: unintended side-effect on, e.g., "2003.05.09"
-    # PyPI stats: 77 (~2%) better
-    rs = re.sub(r"\b0+(\d+)(?!\d)", r"\1", rs)
-
-    # Clean a/b/c with no version. E.g. "1.0a" -> "1.0a0". Setuptools infers
-    # zero.
-    # PyPI stats: 245 (7.56%) better
-    rs = re.sub(r"(\d+[abc])$", r"\g<1>0", rs)
-
-    # the 'dev-rNNN' tag is a dev tag
-    rs = re.sub(r"\.?(dev-r|dev\.r)\.?(\d+)$", r".dev\2", rs)
-
-    # clean the - when used as a pre delimiter
-    rs = re.sub(r"-(a|b|c)(\d+)$", r"\1\2", rs)
-
-    # a terminal "dev" or "devel" can be changed into ".dev0"
-    rs = re.sub(r"[\.\-](dev|devel)$", r".dev0", rs)
-
-    # a terminal "dev" can be changed into ".dev0"
-    rs = re.sub(r"(?![\.\-])dev$", r".dev0", rs)
-
-    # a terminal "final" or "stable" can be removed
-    rs = re.sub(r"(final|stable)$", "", rs)
-
-    # The 'r' and the '-' tags are post release tags
-    #   0.4a1.r10       ->  0.4a1.post10
-    #   0.9.33-17222    ->  0.9.33.post17222
-    #   0.9.33-r17222   ->  0.9.33.post17222
-    rs = re.sub(r"\.?(r|-|-r)\.?(\d+)$", r".post\2", rs)
-
-    # Clean 'r' instead of 'dev' usage:
-    #   0.9.33+r17222   ->  0.9.33.dev17222
-    #   1.0dev123       ->  1.0.dev123
-    #   1.0.git123      ->  1.0.dev123
-    #   1.0.bzr123      ->  1.0.dev123
-    #   0.1a0dev.123    ->  0.1a0.dev123
-    # PyPI stats:  ~150 (~4%) better
-    rs = re.sub(r"\.?(dev|git|bzr)\.?(\d+)$", r".dev\2", rs)
-
-    # Clean '.pre' (normalized from '-pre' above) instead of 'c' usage:
-    #   0.2.pre1        ->  0.2c1
-    #   0.2-c1         ->  0.2c1
-    #   1.0preview123   ->  1.0c123
-    # PyPI stats: ~21 (0.62%) better
-    rs = re.sub(r"\.?(pre|preview|-c)(\d+)$", r"c\g<2>", rs)
-
-    # Tcl/Tk uses "px" for their post release markers
-    rs = re.sub(r"p(\d+)$", r".post\1", rs)
-
-    try:
-        _normalized_key(rs)
-    except UnsupportedVersionError:
-        rs = None
-    return rs
-
-#
-#   Legacy version processing (distribute-compatible)
-#
-
-_VERSION_PART = re.compile(r'([a-z]+|\d+|[\.-])', re.I)
-_VERSION_REPLACE = {
-    'pre': 'c',
-    'preview': 'c',
-    '-': 'final-',
-    'rc': 'c',
-    'dev': '@',
-    '': None,
-    '.': None,
-}
-
-
-def _legacy_key(s):
-    def get_parts(s):
-        result = []
-        for p in _VERSION_PART.split(s.lower()):
-            p = _VERSION_REPLACE.get(p, p)
-            if p:
-                if '0' <= p[:1] <= '9':
-                    p = p.zfill(8)
-                else:
-                    p = '*' + p
-                result.append(p)
-        result.append('*final')
-        return result
-
-    result = []
-    for p in get_parts(s):
-        if p.startswith('*'):
-            if p < '*final':
-                while result and result[-1] == '*final-':
-                    result.pop()
-            while result and result[-1] == '00000000':
-                result.pop()
-        result.append(p)
-    return tuple(result)
-
-
-class LegacyVersion(Version):
-    def parse(self, s):
-        return _legacy_key(s)
-
-    @property
-    def is_prerelease(self):
-        result = False
-        for x in self._parts:
-            if (isinstance(x, string_types) and x.startswith('*') and
-                x < '*final'):
-                result = True
-                break
-        return result
-
-
-class LegacyMatcher(Matcher):
-    version_class = LegacyVersion
-
-    _operators = dict(Matcher._operators)
-    _operators['~='] = '_match_compatible'
-
-    numeric_re = re.compile(r'^(\d+(\.\d+)*)')
-
-    def _match_compatible(self, version, constraint, prefix):
-        if version < constraint:
-            return False
-        m = self.numeric_re.match(str(constraint))
-        if not m:
-            logger.warning('Cannot compute compatible match for version %s '
-                           ' and constraint %s', version, constraint)
-            return True
-        s = m.groups()[0]
-        if '.' in s:
-            s = s.rsplit('.', 1)[0]
-        return _match_prefix(version, s)
-
-#
-#   Semantic versioning
-#
-
-_SEMVER_RE = re.compile(r'^(\d+)\.(\d+)\.(\d+)'
-                        r'(-[a-z0-9]+(\.[a-z0-9-]+)*)?'
-                        r'(\+[a-z0-9]+(\.[a-z0-9-]+)*)?$', re.I)
-
-
-def is_semver(s):
-    return _SEMVER_RE.match(s)
-
-
-def _semantic_key(s):
-    def make_tuple(s, absent):
-        if s is None:
-            result = (absent,)
-        else:
-            parts = s[1:].split('.')
-            # We can't compare ints and strings on Python 3, so fudge it
-            # by zero-filling numeric values so simulate a numeric comparison
-            result = tuple([p.zfill(8) if p.isdigit() else p for p in parts])
-        return result
-
-    m = is_semver(s)
-    if not m:
-        raise UnsupportedVersionError(s)
-    groups = m.groups()
-    major, minor, patch = [int(i) for i in groups[:3]]
-    # choose the '|' and '*' so that versions sort correctly
-    pre, build = make_tuple(groups[3], '|'), make_tuple(groups[5], '*')
-    return (major, minor, patch), pre, build
-
-
-class SemanticVersion(Version):
-    def parse(self, s):
-        return _semantic_key(s)
-
-    @property
-    def is_prerelease(self):
-        return self._parts[1][0] != '|'
-
-
-class SemanticMatcher(Matcher):
-    version_class = SemanticVersion
-
-
-class VersionScheme(object):
-    def __init__(self, key, matcher, suggester=None):
-        self.key = key
-        self.matcher = matcher
-        self.suggester = suggester
-
-    def is_valid_version(self, s):
-        try:
-            self.matcher.version_class(s)
-            result = True
-        except UnsupportedVersionError:
-            result = False
-        return result
-
-    def is_valid_matcher(self, s):
-        try:
-            self.matcher(s)
-            result = True
-        except UnsupportedVersionError:
-            result = False
-        return result
-
-    def is_valid_constraint_list(self, s):
-        """
-        Used for processing some metadata fields
-        """
-        # See issue #140. Be tolerant of a single trailing comma.
-        if s.endswith(','):
-            s = s[:-1]
-        return self.is_valid_matcher('dummy_name (%s)' % s)
-
-    def suggest(self, s):
-        if self.suggester is None:
-            result = None
-        else:
-            result = self.suggester(s)
-        return result
-
-_SCHEMES = {
-    'normalized': VersionScheme(_normalized_key, NormalizedMatcher,
-                                _suggest_normalized_version),
-    'legacy': VersionScheme(_legacy_key, LegacyMatcher, lambda self, s: s),
-    'semantic': VersionScheme(_semantic_key, SemanticMatcher,
-                              _suggest_semantic_version),
-}
-
-_SCHEMES['default'] = _SCHEMES['normalized']
-
-
-def get_scheme(name):
-    if name not in _SCHEMES:
-        raise ValueError('unknown scheme name: %r' % name)
-    return _SCHEMES[name]
diff --git a/spaces/tomandandy/MusicGen3/audiocraft/modules/activations.py b/spaces/tomandandy/MusicGen3/audiocraft/modules/activations.py
deleted file mode 100644
index 8bd6f2917a56d72db56555d0ff54b2311bc21778..0000000000000000000000000000000000000000
--- a/spaces/tomandandy/MusicGen3/audiocraft/modules/activations.py
+++ /dev/null
@@ -1,96 +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 torch
-import torch.nn as nn
-from torch import Tensor
-from typing import Union, Callable
-
-
-class CustomGLU(nn.Module):
-    """Custom Gated Linear Unit activation.
-    Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half
-    of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation
-    function (i.e. sigmoid, swish, etc.).
-
-    Args:
-        activation (nn.Module): The custom activation to apply in the Gated Linear Unit
-        dim (int): the dimension on which to split the input. Default: -1
-
-    Shape:
-        - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional
-          dimensions
-        - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2`
-
-    Examples::
-        >>> m = CustomGLU(nn.Sigmoid())
-        >>> input = torch.randn(4, 2)
-        >>> output = m(input)
-    """
-    def __init__(self, activation: nn.Module, dim: int = -1):
-        super(CustomGLU, self).__init__()
-        self.dim = dim
-        self.activation = activation
-
-    def forward(self, x: Tensor):
-        assert x.shape[self.dim] % 2 == 0  # M = N / 2
-        a, b = torch.chunk(x, 2, dim=self.dim)
-        return a * self.activation(b)
-
-
-class SwiGLU(CustomGLU):
-    """SiLU Gated Linear Unit activation.
-    Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(SwiGLU, self).__init__(nn.SiLU(), dim)
-
-
-class GeGLU(CustomGLU):
-    """GeLU Gated Linear Unit activation.
-    Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(GeGLU, self).__init__(nn.GELU(), dim)
-
-
-class ReGLU(CustomGLU):
-    """ReLU Gated Linear Unit activation.
-    Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is
-    the first half of the input matrices, :math:`b` is the second half.
-
-    Args:
-        dim (int): the dimension on which to split the input. Default: -1
-    """
-    def __init__(self, dim: int = -1):
-        super(ReGLU, self).__init__(nn.ReLU(), dim)
-
-
-def get_activation_fn(
-    activation: Union[str, Callable[[Tensor], Tensor]]
-) -> Union[str, Callable[[Tensor], Tensor]]:
-    """Helper function to map an activation string to the activation class.
-    If the supplied activation is not a string that is recognized, the activation is passed back.
-
-    Args:
-        activation (Union[str, Callable[[Tensor], Tensor]]): Activation to check
-    """
-    if isinstance(activation, str):
-        if activation == "reglu":
-            return ReGLU()
-        elif activation == "geglu":
-            return GeGLU()
-        elif activation == "swiglu":
-            return SwiGLU()
-    return activation
diff --git a/spaces/tracinginsights/F1-analysis/pages/Heat_Map.py b/spaces/tracinginsights/F1-analysis/pages/Heat_Map.py
deleted file mode 100644
index f1f22cfd42c677599cf34c6477b8c7fe13425cc5..0000000000000000000000000000000000000000
--- a/spaces/tracinginsights/F1-analysis/pages/Heat_Map.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-from repo_directory import Heat_Map
-from repo_directory import button
-
-# select box for url
-URL = st.text_input(label="Race Result URL from Formula1.com", value="https://www.formula1.com/en/results.html/2022/races/1136/mexico/race-result.html")
-
-race_results_df, drivers = Heat_Map.get_results(URL)
-
-Heat_Map.get_latest_ergast()
-
-drivers_df, laps_df, race_names_df, results_df = Heat_Map.load_data()
-
-# select year
-
-available_years = race_names_df.year.unique().tolist()
-available_years.sort(reverse=True)
-
-YEAR_SELECTED = st.selectbox(
-    'Select year',
-    available_years)
-
-# select race
-available_races = race_names_df[race_names_df.year == YEAR_SELECTED].name.tolist()
-
-RACE_SELECTED = st.selectbox(
-    'Select Race',
-    available_races)
-
-SELECTED_RACEID = race_names_df[
-    (race_names_df.year == YEAR_SELECTED) & (race_names_df.name == RACE_SELECTED)
-].raceId.values[0]
-
-Heat_Map.plot(RACE_SELECTED, SELECTED_RACEID, laps_df, drivers_df, drivers)
\ No newline at end of file
diff --git a/spaces/tsi-org/LLaVA/llava/model/language_model/mpt/configuration_mpt.py b/spaces/tsi-org/LLaVA/llava/model/language_model/mpt/configuration_mpt.py
deleted file mode 100644
index e9eb6fc59b50654ddbe19ed56ad8c0abd1b8efef..0000000000000000000000000000000000000000
--- a/spaces/tsi-org/LLaVA/llava/model/language_model/mpt/configuration_mpt.py
+++ /dev/null
@@ -1,118 +0,0 @@
-"""A HuggingFace-style model configuration."""
-from typing import Dict, Optional, Union
-from transformers import PretrainedConfig
-attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}
-init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0}
-
-class MPTConfig(PretrainedConfig):
-    model_type = 'mpt'
-
-    def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, verbose: int=0, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, **kwargs):
-        """The MPT configuration class.
-
-        Args:
-            d_model (int): The size of the embedding dimension of the model.
-            n_heads (int): The number of attention heads.
-            n_layers (int): The number of layers in the model.
-            expansion_ratio (int): The ratio of the up/down scale in the MLP.
-            max_seq_len (int): The maximum sequence length of the model.
-            vocab_size (int): The size of the vocabulary.
-            resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
-            emb_pdrop (float): The dropout probability for the embedding layer.
-            learned_pos_emb (bool): Whether to use learned positional embeddings
-            attn_config (Dict):  A dictionary used to configure the model's attention module:
-                attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention
-                attn_pdrop (float): The dropout probability for the attention layers.
-                attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
-                qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
-                clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
-                    this value.
-                softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
-                    use the default scale of ``1/sqrt(d_keys)``.
-                prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
-                    extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
-                    can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
-                attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
-                    When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
-                    which sub-sequence each token belongs to.
-                    Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
-                alibi (bool): Whether to use the alibi bias instead of position embeddings.
-                alibi_bias_max (int): The maximum value of the alibi bias.
-            init_device (str): The device to use for parameter initialization.
-            logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
-            no_bias (bool): Whether to use bias in all layers.
-            verbose (int): The verbosity level. 0 is silent.
-            embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
-            norm_type (str): choose type of norm to use
-            multiquery_attention (bool): Whether to use multiquery attention implementation.
-            use_cache (bool): Whether or not the model should return the last key/values attentions
-            init_config (Dict): A dictionary used to configure the model initialization:
-                init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
-                    'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
-                    'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
-                init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
-                emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
-                emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
-                    used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
-                init_std (float): The standard deviation of the normal distribution used to initialize the model,
-                    if using the baseline_ parameter initialization scheme.
-                init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
-                fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
-                init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
-                ---
-                See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
-        """
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.expansion_ratio = expansion_ratio
-        self.max_seq_len = max_seq_len
-        self.vocab_size = vocab_size
-        self.resid_pdrop = resid_pdrop
-        self.emb_pdrop = emb_pdrop
-        self.learned_pos_emb = learned_pos_emb
-        self.attn_config = attn_config
-        self.init_device = init_device
-        self.logit_scale = logit_scale
-        self.no_bias = no_bias
-        self.verbose = verbose
-        self.embedding_fraction = embedding_fraction
-        self.norm_type = norm_type
-        self.use_cache = use_cache
-        self.init_config = init_config
-        if 'name' in kwargs:
-            del kwargs['name']
-        if 'loss_fn' in kwargs:
-            del kwargs['loss_fn']
-        super().__init__(**kwargs)
-        self._validate_config()
-
-    def _set_config_defaults(self, config, config_defaults):
-        for (k, v) in config_defaults.items():
-            if k not in config:
-                config[k] = v
-        return config
-
-    def _validate_config(self):
-        self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
-        self.init_config = self._set_config_defaults(self.init_config, init_config_defaults)
-        if self.d_model % self.n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads')
-        if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])):
-            raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1")
-        if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
-            raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
-        if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
-        if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('alibi only implemented with torch and triton attention.')
-        if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
-        if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
-            raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
-        if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
-            raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
-        if self.init_config.get('name', None) is None:
-            raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
-        if not self.learned_pos_emb and (not self.attn_config['alibi']):
-            raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.')
\ No newline at end of file
diff --git a/spaces/ttt246/brain/Brain/src/model/chat_response_model.py b/spaces/ttt246/brain/Brain/src/model/chat_response_model.py
deleted file mode 100644
index 83ac8626980b2c2e9c0c8cfdd81268e2f90408f5..0000000000000000000000000000000000000000
--- a/spaces/ttt246/brain/Brain/src/model/chat_response_model.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""chat response model"""
-from typing import Any
-
-from Brain.src.model.message_model import MessageModel
-
-
-class ChatResponseModel:
-    def __init__(self, data: Any):
-        self.id = data["id"]
-        self.object = data["object"]
-        self.model = data["model"]
-        self.usage = data["usage"]
-        """'choices': [
-            {
-                'message': {
-                    'role': 'assistant',
-                    'content': 'The 2020 World Series was played in Arlington, Texas at the Globe Life Field, which was the new home stadium for the Texas Rangers.'},
-                'finish_reason': 'stop',
-                'index': 0
-            }
-        ]"""
-        self.choices = []
-        for choice in data["choices"]:
-            self.choices.append(ChoiceModel(choice))
-
-    def get_one_message_item(self) -> MessageModel:
-        if len(self.choices) > 0:
-            return self.choices.__getitem__(0).get_message()
-        else:
-            return MessageModel("", "")
-
-
-class ChoiceModel:
-    def __init__(self, data: Any):
-        tmp_message = data["message"]
-        self.message = MessageModel(tmp_message["role"], tmp_message["content"])
-        self.finish_reason = data["finish_reason"]
-        self.index = data["index"]
-
-    def get_message(self):
-        return self.message
diff --git a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/im_coords_affine_enhance_tool.py b/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/im_coords_affine_enhance_tool.py
deleted file mode 100644
index 8aa8f22a4da05c82d9b507262ffdc9d9e072575d..0000000000000000000000000000000000000000
--- a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/im_coords_affine_enhance_tool.py
+++ /dev/null
@@ -1,242 +0,0 @@
-'''
-图像，坐标仿射增强工具
-'''
-
-
-import cv2
-import numpy as np
-from typing import Union, Tuple, Iterable
-try:
-    from im_tool import pad_picture
-    import coord_tool
-    from affine_matrix_tool import make_move, make_rotate, make_scale, make_shear
-    import point_tool
-except ModuleNotFoundError:
-    from .im_tool import pad_picture
-    from . import coord_tool
-    from .affine_matrix_tool import make_move, make_rotate, make_scale, make_shear
-    from . import point_tool
-
-
-# # 旋转，平移，缩放，切变
-#
-# def make_rotate(angle=180., center_yx=(0.5, 0.5), img_hw: Union[None, Tuple]=(512, 512)):
-#     '''
-#     旋转
-#     :param angle: 顺时针旋转角度
-#     :param center_yx: 如果 img_hw 不为 None，则为百分比坐标，否则为绝对坐标
-#     :param img_hw: 图像大小，单位为像素
-#     :return:
-#     '''
-#     if img_hw is not None:
-#         center_yx = (img_hw[0] * center_yx[0], img_hw[1] * center_yx[1])
-#
-#     R = np.eye(3)
-#     # 这里加个符号使其顺时针转动
-#     R[:2] = cv2.getRotationMatrix2D(angle=-angle, center=center_yx[::-1], scale=1.)
-#     return R
-#
-#
-# def make_move(move_yx=(0.2, 0.2), img_hw: Union[None, Tuple]=(512, 512)):
-#     '''
-#     平移
-#     :param move_yx: 平移距离，当 img_hw 不是None时，单位为图像大小百分比，如果 img_hw 是 None，则为像素单位
-#     :param img_hw: 图像大小，单位为像素
-#     :return:
-#     '''
-#     move_yx = list(move_yx)
-#     if img_hw is not None:
-#         move_yx[1] = move_yx[1] * img_hw[1]
-#         move_yx[0] = move_yx[0] * img_hw[0]
-#
-#     T = np.eye(3)
-#     T[0, 2] = move_yx[1]
-#     T[1, 2] = move_yx[0]
-#     return T
-#
-#
-# def make_scale(scale_yx=(2., 2.), center_yx=(0.5, 0.5), img_hw: Union[None, Tuple]=(512, 512)):
-#     '''
-#     缩放
-#     :param scale_yx: 单位必须为相对图像大小的百分比
-#     :param center_yx: 变换中心位置，当 img_hw 不是None时，单位为图像大小百分比，如果 img_hw 是 None，则为像素单位
-#     :param img_hw: 图像大小，单位为像素
-#     :return:
-#     '''
-#     if img_hw is not None:
-#         center_yx = (img_hw[0] * center_yx[0], img_hw[1] * center_yx[1])
-#
-#     S = np.eye(3)
-#     S[0, 0] = scale_yx[1]
-#     S[1, 1] = scale_yx[0]
-#     S[0, 2] = (1-scale_yx[1]) * center_yx[1]
-#     S[1, 2] = (1-scale_yx[0]) * center_yx[0]
-#     return S
-#
-#
-# def make_shear(shear_yx=(1.2, 1.2)):
-#     '''
-#     切变
-#     :param shear_yx: 单位为角度；变量1，图像x边与窗口x边角度，变量2，图像y边与窗口y边角度
-#     :return:
-#     '''
-#     # Shear
-#     S = np.eye(3)
-#     S[0, 1] = np.tan(shear_yx[1] * np.pi / 180)  # x shear (deg)
-#     S[1, 0] = np.tan(shear_yx[0] * np.pi / 180)  # y shear (deg)
-#     return S
-
-
-def apply_affine_to_img(img, M, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0), out_hw: tuple=None):
-    '''
-    :param img: 图像
-    :param M: 变换矩阵
-    :param interpolation: 平滑方式
-    :param borderMode: 填充方式
-    :param borderValue: 边缘填充颜色
-    :return:
-    '''
-    if out_hw is None:
-        hw = img.shape[:2]
-    else:
-        hw = tuple(out_hw)
-    img = cv2.warpPerspective(img, M, dsize=hw[::-1], flags=interpolation, borderMode=borderMode, borderValue=borderValue)
-    return img
-
-
-# def apply_affine_to_coords(coords_y1x1y2x2, M):
-#     '''
-#     对坐标进行仿射变换
-#     :param coords_y1x1y2x2: 要求输入格式为 [y1x2y2x2, y1x2y2x2, ...]
-#     :param M: 3x3 或 2x3 变换矩阵
-#     :return:
-#     '''
-#     coords_x1y1x2y2 = coord_tool.yxhw2xywh(coords_y1x1y2x2)
-#     # 对矩阵乘法不熟悉，所以使用易于理解的方式处理
-#
-#     # 分解矩阵为旋转矩阵和平移矩阵，因为貌似旋转和平移没法在一次运算中完成
-#     R = M[:2, 0:2]
-#     T = M[:2, 2:3]
-#
-#     # 先得到包围框的四个坐标点，需要变换后的矩形的外接正矩形
-#     coords_x1y1_x2y1_x1y2_x2y2 = coords_x1y1x2y2[:, [0, 1, 2, 1, 0, 3, 2, 3]]
-#     xy = coords_x1y1_x2y1_x1y2_x2y2.reshape([-1, 2])
-#     xy_t = R @ xy.T
-#     xy_t = T + xy_t
-#     xy = xy_t.T
-#
-#     # 变换完了...
-#     # coords_x1y1_x2y1_x1y2_x2y2 = xy.reshape([-1, 8])
-#     coords_groups_xy = xy.reshape([-1, 4, 2])
-#     # 将x坐标分到一组，y坐标分到一组，便于直接求大小
-#     coords_xy_groups = np.transpose(coords_groups_xy, [0, 2, 1])
-#     coord_xy_left_top = np.min(coords_xy_groups, 2)
-#     coord_xy_right_bottom = np.max(coords_xy_groups, 2)
-#     coord_x1y1x2y2 = np.concatenate([coord_xy_left_top, coord_xy_right_bottom], 1)
-#     out = coord_tool.xywh2yxhw(coord_x1y1x2y2)
-#
-#     return out
-
-
-def apply_affine_to_coords(coords_y1x1y2x2, M):
-    '''
-    对坐标进行仿射变换
-    :param coords_y1x1y2x2: 要求输入格式为 [y1x2y2x2, y1x2y2x2, ...]
-    :param M: 3x3 或 2x3 变换矩阵
-    :return:
-    '''
-    # 先得到包围框的四个坐标点，需要变换后的矩形的外接正矩形
-    coords_y1x1_y1x2_y2x1_y2x2 = coords_y1x1y2x2[:, [0, 1, 0, 3, 2, 1, 2, 3]]
-    pts_yx = np.reshape(coords_y1x1_y1x2_y2x1_y2x2, [-1, 2])
-    pts_yx = point_tool.apply_affine_to_points(pts_yx, M)
-    pts_yx = np.reshape(pts_yx, [-1, 8])
-
-    coords_groups_yx = pts_yx.reshape([-1, 4, 2])
-    # 将x坐标分到一组，y坐标分到一组，便于直接求大小
-    coords_yx_groups = np.transpose(coords_groups_yx, [0, 2, 1])
-    coord_yx_left_top = np.min(coords_yx_groups, 2)
-    coord_yx_right_bottom = np.max(coords_yx_groups, 2)
-    coord_y1x1y2x2 = np.concatenate([coord_yx_left_top, coord_yx_right_bottom], 1)
-    # out = coord_tool.xywh2yxhw(coord_y1x1y2x2)
-
-    return coord_y1x1y2x2
-
-
-def coords_clip(coords_y1x1y2x2, img_hw):
-    new_coords = np.clip(coords_y1x1y2x2, [0, 0, 0, 0], [*img_hw, *img_hw])
-    return new_coords
-
-
-def random_affine_matrix(move_low_high=[-0.3, 0.3], angle_low_high=[-180, 180], scale_low_high=[0.7, 1.3], shear_low_high=[-20, 20], img_hw=[512, 512]):
-    move_ratio_h = np.random.uniform(move_low_high[0], move_low_high[1])
-    move_ratio_w = np.random.uniform(move_low_high[0], move_low_high[1])
-    T = make_move([move_ratio_h, move_ratio_w], img_hw)
-
-    angle = np.random.uniform(angle_low_high[0], angle_low_high[1])
-    R = make_rotate(angle, center_yx=[0.5, 0.5], img_hw=img_hw)
-
-    scale_ratio_h = np.random.uniform(scale_low_high[0], scale_low_high[1])
-    scale_ratio_w = np.random.uniform(scale_low_high[0], scale_low_high[1])
-    S = make_scale([scale_ratio_h, scale_ratio_w], center_yx=[0.5, 0.5], img_hw=img_hw)
-
-    shear_ratio_h = np.random.uniform(shear_low_high[0], shear_low_high[1])
-    shear_ratio_w = np.random.uniform(shear_low_high[0], shear_low_high[1])
-    Sh = make_shear([shear_ratio_h, shear_ratio_w])
-
-    # 运算方向注意 T->R->S->Sh
-    M = Sh @ S @ R @ T
-    return M
-
-
-if __name__ == '__main__':
-    import imageio
-    import os
-    import im_tool
-
-    impath = os.path.dirname(__file__) + '/laska.png'
-    im = imageio.imread(impath)
-    coord_y1x1y2x2 = np.array([[75, 50, 150, 175]])
-
-    ori_im = im_tool.draw_boxes_and_labels_to_image(im, [0], coord_tool.y1x1y2x2_to_x1y1x2y2(coord_y1x1y2x2))
-    im_tool.show_image(ori_im)
-
-    # 测试旋转，注意这里得到的是包围框旋转后的外接矩形，所以宽高比不与原来的一样
-    R = make_rotate(60, [0.5, 0.5], im.shape[:2])
-    im_r = apply_affine_to_img(im, R, borderValue=[128, 128, 128])
-    coord_r = apply_affine_to_coords(coord_y1x1y2x2, R)
-    coord_r = coord_tool.y1x1y2x2_to_x1y1x2y2(coord_r)
-    im_r = im_tool.draw_boxes_and_labels_to_image(im_r, [0], coord_r)
-    im_tool.show_image(im_r)
-
-    # 测试平移
-    T = make_move([0.2, 0.5], im.shape[:2])
-    im_t = apply_affine_to_img(im, T, borderValue=[128, 128, 128])
-    coord_t = apply_affine_to_coords(coord_y1x1y2x2, T)
-    coord_t = coord_tool.y1x1y2x2_to_x1y1x2y2(coord_t)
-    im_t = im_tool.draw_boxes_and_labels_to_image(im_t, [0], coord_t)
-    im_tool.show_image(im_t)
-
-    # 测试缩放
-    S = make_scale([1.2, 0.5], [0.5, 0.5], im.shape[:2])
-    im_s = apply_affine_to_img(im, S, borderValue=[128, 128, 128])
-    coord_s = apply_affine_to_coords(coord_y1x1y2x2, S)
-    coord_s = coord_tool.y1x1y2x2_to_x1y1x2y2(coord_s)
-    im_s = im_tool.draw_boxes_and_labels_to_image(im_s, [0], coord_s)
-    im_tool.show_image(im_s)
-
-    # 测试切变
-    SH = make_shear([0, 30])
-    im_sh = apply_affine_to_img(im, SH, borderValue=[128, 128, 128])
-    coord_sh = apply_affine_to_coords(coord_y1x1y2x2, SH)
-    coord_sh = coord_tool.y1x1y2x2_to_x1y1x2y2(coord_sh)
-    im_sh = im_tool.draw_boxes_and_labels_to_image(im_sh, [0], coord_sh)
-    im_tool.show_image(im_sh)
-
-    # 测试组合
-    # 运算方向 T -> R -> S
-    M = S @ R @ T
-    im_m = apply_affine_to_img(im, M, borderValue=[128, 128, 128])
-    coord_m = apply_affine_to_coords(coord_y1x1y2x2, M)
-    coord_m = coord_tool.y1x1y2x2_to_x1y1x2y2(coord_m)
-    im_m = im_tool.draw_boxes_and_labels_to_image(im_m, [0], coord_m)
-    im_tool.show_image(im_m)
diff --git a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/ndarray_over_scan_wrapper.py b/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/ndarray_over_scan_wrapper.py
deleted file mode 100644
index a63094c7d154a46bd550ef95fd63cba6c4d9d4d2..0000000000000000000000000000000000000000
--- a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/ndarray_over_scan_wrapper.py
+++ /dev/null
@@ -1,237 +0,0 @@
-'''
-多维数组超采样工具
-目前支持读取和写入
-
-支持例如以下方式
-
-ori_arr = np.zeros([20, 20])
-arr = NdArrayOverScanWrapper(arr)
-
-读取
-不支持：
-1.省略号
-2.数字列表
-支持：
-1.单数字输入
-2.None
-3.切片
-例如：
-arr[None, :, 1]
-arr[None, -5:5, 1]
-arr[None, 8:12, -3]
-
-写入
-不支持：
-1. 写入超出数组范围的设定值会被丢弃
-2. 省略号
-3. None
-4. 数字列表
-支持：
-1. 单数字输入
-2. 切片
-例如：
-arr[5:10, 3:4] = xxx
-arr[-5:-1, 13] = xxx
-注意：arr[1] 等同于 arr[1:2]
-注意，写入时，填充的数组的维度数量要与原始数组一致。
-注意，写入时，填充的数组的形状要与切片形状一致。
-
-'''
-
-import numpy as np
-from typing import Sized
-
-
-class NdArrayOverScanWrapper:
-    def __init__(self, ori_arr: np.ndarray, pad_mode='constant', **pad_kwargs):
-        self.data = ori_arr
-        self.pad_mode = pad_mode
-        self.pad_kwargs = pad_kwargs
-
-    def __getitem__(self, ss):
-        '''
-        获取指定区域的数组
-        :param ss:
-        :return:
-        '''
-        if not isinstance(ss, Sized):
-            ss = (ss,)
-
-        cur_arr = self.data
-
-        for i, v in enumerate(ss):
-            if v is np.newaxis:
-                cur_arr = np.expand_dims(cur_arr, i)
-
-        assert len(ss) <= cur_arr.ndim
-
-        for i, s in list(enumerate(ss))[::-1]:
-            if isinstance(s, int) or (np.isscalar(s) and np.issubsctype(np.asarray(s), np.integer)):
-                if s < 0:
-                    p_before = abs(s)
-                    pad_p = [[0, 0]] * cur_arr.ndim
-                    pad_p[i] = [p_before, 0]
-                    cur_arr = np.pad(cur_arr, pad_p, mode=self.pad_mode, **self.pad_kwargs)
-                    cur_arr = np.take(cur_arr, s + p_before, axis=i)
-
-                elif s >= cur_arr.shape[i]:
-                    p_after = s - cur_arr.shape[i] + 1
-                    pad_p = [[0, 0]] * cur_arr.ndim
-                    pad_p[i] = [0, p_after]
-                    cur_arr = np.pad(cur_arr, pad_p, mode=self.pad_mode, **self.pad_kwargs)
-                    cur_arr = np.take(cur_arr, s, axis=i)
-
-                else:
-                    cur_arr = np.take(cur_arr, s, axis=i)
-
-            elif isinstance(s, slice):
-                p_before = 0
-                p_after = 0
-
-                if s.start is None:
-                    s.start = 0
-                elif s.start < 0:
-                    p_before = abs(s.start)
-                elif s.start >= cur_arr.shape[i]:
-                    p_after = s.start - cur_arr.shape[i] + 1
-
-                if s.stop is None:
-                    s.stop = cur_arr.shape[i]
-                elif s.stop < 0:
-                    p_before = max(p_before, abs(s.stop))
-                elif s.stop > cur_arr.shape[i]:
-                    p_after = max(p_after, s.stop - cur_arr.shape[i])
-
-                if p_before != 0 or p_after != 0:
-                    pad_p = [[0, 0]] * cur_arr.ndim
-                    pad_p[i] = [p_before, p_after]
-                    cur_arr = np.pad(cur_arr, pad_p, mode=self.pad_mode, **self.pad_kwargs)
-
-                s = np.arange(s.start + p_before, s.stop + p_before, s.step)
-
-                cur_arr = np.take(cur_arr, s, axis=i)
-
-            elif s is None:
-                continue
-
-            else:
-                raise AssertionError('Error! Wrong index.')
-
-        return cur_arr
-
-    def __setitem__(self, ss, value):
-        '''
-        设定值。
-        要加入多个限定条件，不然很难写或写不了
-        1. 超出数组范围的设定值会被丢弃
-        2. 不支持的省略号
-        3. 不支持None
-        4. 不支持数字列表
-
-        :param key:
-        :param value:
-        :return:
-        '''
-
-        if not isinstance(ss, Sized):
-            ss = (ss,)
-
-        assert len(ss) <= self.data.ndim, 'Error! Too many dimensions are specified.'
-        assert self.data.ndim == value.ndim, 'Error! Not support different ndim.'
-
-        # 处理后的切片索引
-        dss = []
-
-        cur_value = value
-
-        for dim_i, s in enumerate(ss):
-            cur_ori_dim = self.data.shape[dim_i]
-            cur_value_dim = cur_value.shape[dim_i]
-
-            if isinstance(s, int) or (np.isscalar(s) and np.issubsctype(np.asarray(s), np.integer)):
-                assert cur_value_dim == 1
-                if s < 0:
-                    dss.append(None)
-                elif s >= cur_ori_dim:
-                    dss.append(None)
-                else:
-                    assert cur_value_dim == 1
-                    dss.append(slice(s, s + 1, 1))
-
-            elif isinstance(s, slice):
-                start = s.start
-                stop = s.stop
-                step = s.step
-
-                if stop is None:
-                    stop = cur_ori_dim
-                if start is None:
-                    start = 0
-
-                # 仅支持1步长
-                assert step is None or step == 1, 'Error! Only support step==1.'
-                assert cur_value_dim == stop - start, 'Error! Value size is not equal slice.'
-
-                if stop < 0:
-                    stop = -1
-                elif stop > cur_ori_dim:
-                    cur_value = np.take(cur_value, np.arange(0, cur_value_dim-(stop-cur_ori_dim)), dim_i)
-                    stop = cur_ori_dim
-
-                cur_value_dim = cur_value.shape[dim_i]
-
-                if start < 0:
-                    if abs(start) > cur_value_dim:
-                        start = -1
-                    else:
-                        cur_value = np.take(cur_value, np.arange(abs(start), cur_value_dim), dim_i)
-                        start = 0
-                elif start > cur_ori_dim:
-                    start = -1
-
-                if start == -1 or stop == -1 or stop - start < 1:
-                    dss.append(None)
-                else:
-                    dss.append(slice(start, stop, step))
-
-            # 因为语法 arr[[1,2,3],[1,2],[1,2,3]] 行为与预期不一致，取消该种方法
-            # elif isinstance(s, Sized):
-            #     new_s = []
-            #     new_vs = []
-            #     for i, iii in enumerate(s):
-            #         if 0 <= iii < cur_ori_dim and i < cur_value_dim:
-            #             new_s.append(iii)
-            #             new_vs.append(i)
-            #
-            #     if len(new_s) == 0:
-            #         dss.append(None)
-            #     else:
-            #         cur_value = np.take(cur_value, new_vs, dim_i)
-            #         dss.append(new_s)
-
-            else:
-                raise AssertionError('Error! Wrong index.')
-
-        for s in dss:
-            if s is None:
-                return
-
-        dss = tuple(dss)
-        self.data.__setitem__(dss, cur_value)
-
-
-if __name__ == '__main__':
-    a = np.random.randint(0, 1000, size=[4, 5, 6, 7])
-    w = NdArrayOverScanWrapper(a, 'reflect')
-    o = w[None, -1, -1:6, -1, -5:10]
-    print(o.shape)
-
-    # w[:, :, :, 1:5] = np.full([1,1,1,4], -1)
-
-    a = np.full([15, 20], 0)
-    w = NdArrayOverScanWrapper(a, 'reflect')
-    w[-5:5, -5:5] = np.full([10, 10], 1)
-    w[10:20, -5:5] = np.full([10, 10], 1)
-    w[-5:5, 15:25] = np.full([10, 10], 1)
-    w[10:20, 15:25] = np.full([10, 10], 1)
-    print(w.data)
diff --git a/spaces/uSerNameDDHL/bingo/src/state/index.ts b/spaces/uSerNameDDHL/bingo/src/state/index.ts
deleted file mode 100644
index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/src/state/index.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atom } from 'jotai'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
-  { author: 'system', text: 'conversation between user and robot', id: '1' },
-  { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?  ', id: '2' },
-  {
-    author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
-  - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
-  - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
-  - 优点：清洁性能强劲，操作方便，适用多种场景；
-  - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
-  - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
-  - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
-  `, id: '3' },
-  { author: 'user', text: '今天的新闻', id: '4' },
-  {
-    author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
-  # 中国新闻
-  - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
-  - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
-  - 中央气象台7月16日18时发布台风橙色预警[^1^]
-  - 贵州石阡：暑期旅游带动乡村振兴[^1^]
-  - 激活大科学装置的“人才红利”[^1^]
-  - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
-  - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
-  - 成都以赛为媒提升城市美誉度[^1^]
-  - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
-  - 浙江建德：新安江上享清凉[^1^]
-  - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
-  - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
-  - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
-  - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
-  - 大运来了丨成都迎大运 全民健身动起来[^1^]
-  - 藏在高校里的宝藏博物馆[^1^]
-  - 中国汽车工业用70年赢得三个“全球第一”[^1^]
-  - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
-  # 国际新闻
-  - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
-  - 国际航运业加快绿色转型[^2^]
-  - 美企反对收紧对华芯片出口限制[^2^]
-  - 欧洲加大气候科技领域投资[^2^]
-  - 中企助力丹麦发展清洁能源[^2^]
-  - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
-  - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
-  以上信息仅供参考，具体详情请点击以下链接查看：
-
-  [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
-  [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
-  { author: 'user', text: '写一个快排', id: '6' },
-  { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n    temp = arr[i]\n    arr[i] = arr[j]\n    arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n    # 选择最右边的元素作为基准元素\n    pivot = arr[high]\n    # 初始化一个指针，指向左边区间的最后一个位置\n    i = low - 1\n    # 遍历左边区间中的每个元素\n    for j in range(low, high):\n        # 如果当前元素小于或等于基准元素\n        if arr[j] <= pivot:\n            # 将指针右移一位\n            i += 1\n            # 将指针所指的元素与当前元素交换\n            swap(arr, i, j)\n    # 将基准元素与指针右边的第一个元素交换\n    swap(arr, i + 1, high)\n    # 返回基准元素的最终位置\n    return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n    # 如果区间长度大于1\n    if low < high:\n        # 对区间进行划分，得到基准元素的位置\n        p = partition(arr, low, high)\n        # 对左边子区间进行快速排序\n        quick_sort(arr, low, p - 1)\n        # 对右边子区间进行快速排序\n        quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
-  {
-    author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)",
-    id: '8'
-  }
-]
-
-export const GreetMessages = [
-  '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
-  '重新开始总是很棒。问我任何问题!',
-  '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
-  '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
-  '很好，让我们来更改主题。你在想什么?',
-  '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
-  '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
-  '感谢刷新！你有新的话题吗？',
-  '明白了，让我们重新开始。接下来应该讨论什么？',
-  '下一步！我可以为你做什么？',
-  '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage<BingConversationStyle>('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const voiceAtom = atomWithStorage<boolean>('enableTTS', false, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
-  return new BingWebBot({
-    cookie: ' ',
-    ua: ' ',
-  })
-}
-
-export const chatFamily = atomFamily(
-  (param: Param) => {
-    return atomWithImmer({
-      botId: param.botId,
-      bot: createBotInstance(),
-      messages: [] as ChatMessageModel[],
-      generatingMessageId: '',
-      abortController: undefined as AbortController | undefined,
-      conversationId: nanoid(),
-    })
-  },
-  (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
-
-export const voiceListenAtom = atom(false)
diff --git a/spaces/ulysses115/Nogizaka46-so/vdecoder/__init__.py b/spaces/ulysses115/Nogizaka46-so/vdecoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/uragankatrrin/MHN-React/mhnreact/inference.py b/spaces/uragankatrrin/MHN-React/mhnreact/inference.py
deleted file mode 100644
index b1bdcd861cb7d66df6691a5741090f1c11e9ab47..0000000000000000000000000000000000000000
--- a/spaces/uragankatrrin/MHN-React/mhnreact/inference.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Author: Philipp Seidl
-        ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning
-        Johannes Kepler University Linz
-Contact: seidl@ml.jku.at
-
-File contains functions that help prepare and download USPTO-related datasets
-"""
-
-# Cell
-from .model import ModelConfig, MHN
-import torch
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Ancient Egypt Knew No Pharaohs Nor Any Israelites - Pyramidion  PDF  The Exodus  Akhenaten[2].md b/spaces/usbethFlerru/sovits-modelsV2/example/Ancient Egypt Knew No Pharaohs Nor Any Israelites - Pyramidion  PDF  The Exodus  Akhenaten[2].md
deleted file mode 100644
index 17986c55dd3d5c6099829f15e7c40cdccb6855c6..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Ancient Egypt Knew No Pharaohs Nor Any Israelites - Pyramidion  PDF  The Exodus  Akhenaten[2].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Egypt Knew No Pharaohs Nor Israelites Downloadl</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt; <a href="https://urlcod.com/2uyWMJ">https://urlcod.com/2uyWMJ</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/logger.py b/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/logger.py
deleted file mode 100644
index 18145f54c927abd59b95f3fa6e6da8002bc2ce97..0000000000000000000000000000000000000000
--- a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/util/logger.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import functools
-import logging
-import os
-import sys
-
-from termcolor import colored
-
-
-class _ColorfulFormatter(logging.Formatter):
-    def __init__(self, *args, **kwargs):
-        self._root_name = kwargs.pop("root_name") + "."
-        self._abbrev_name = kwargs.pop("abbrev_name", "")
-        if len(self._abbrev_name):
-            self._abbrev_name = self._abbrev_name + "."
-        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
-
-    def formatMessage(self, record):
-        record.name = record.name.replace(self._root_name, self._abbrev_name)
-        log = super(_ColorfulFormatter, self).formatMessage(record)
-        if record.levelno == logging.WARNING:
-            prefix = colored("WARNING", "red", attrs=["blink"])
-        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
-            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
-        else:
-            return log
-        return prefix + " " + log
-
-
-# so that calling setup_logger multiple times won't add many handlers
-@functools.lru_cache()
-def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
-    """
-    Initialize the detectron2 logger and set its verbosity level to "INFO".
-
-    Args:
-        output (str): a file name or a directory to save log. If None, will not save log file.
-            If ends with ".txt" or ".log", assumed to be a file name.
-            Otherwise, logs will be saved to `output/log.txt`.
-        name (str): the root module name of this logger
-
-    Returns:
-        logging.Logger: a logger
-    """
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    logger.propagate = False
-
-    if abbrev_name is None:
-        abbrev_name = name
-
-    plain_formatter = logging.Formatter(
-        "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
-    )
-    # stdout logging: master only
-    if distributed_rank == 0:
-        ch = logging.StreamHandler(stream=sys.stdout)
-        ch.setLevel(logging.DEBUG)
-        if color:
-            formatter = _ColorfulFormatter(
-                colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
-                datefmt="%m/%d %H:%M:%S",
-                root_name=name,
-                abbrev_name=str(abbrev_name),
-            )
-        else:
-            formatter = plain_formatter
-        ch.setFormatter(formatter)
-        logger.addHandler(ch)
-
-    # file logging: all workers
-    if output is not None:
-        if output.endswith(".txt") or output.endswith(".log"):
-            filename = output
-        else:
-            filename = os.path.join(output, "log.txt")
-        if distributed_rank > 0:
-            filename = filename + f".rank{distributed_rank}"
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
-
-        fh = logging.StreamHandler(_cached_log_stream(filename))
-        fh.setLevel(logging.DEBUG)
-        fh.setFormatter(plain_formatter)
-        logger.addHandler(fh)
-
-    return logger
-
-
-# cache the opened file object, so that different calls to `setup_logger`
-# with the same file name can safely write to the same file.
-@functools.lru_cache(maxsize=None)
-def _cached_log_stream(filename):
-    return open(filename, "a")
diff --git a/spaces/vinthony/SadTalker/Dockerfile b/spaces/vinthony/SadTalker/Dockerfile
deleted file mode 100644
index 5ddc6e3d8b246534a58f9612a88b309fa7e10795..0000000000000000000000000000000000000000
--- a/spaces/vinthony/SadTalker/Dockerfile
+++ /dev/null
@@ -1,59 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
-    apt-get upgrade -y && \
-    apt-get install -y --no-install-recommends \
-    git \
-    zip \
-    unzip \
-    git-lfs \
-    wget \
-    curl \
-    # ffmpeg \
-    ffmpeg \
-    x264 \
-    # python build dependencies \
-    build-essential \
-    libssl-dev \
-    zlib1g-dev \
-    libbz2-dev \
-    libreadline-dev \
-    libsqlite3-dev \
-    libncursesw5-dev \
-    xz-utils \
-    tk-dev \
-    libxml2-dev \
-    libxmlsec1-dev \
-    libffi-dev \
-    liblzma-dev && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
-    pyenv global ${PYTHON_VERSION} && \
-    pyenv rehash && \
-    pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN ls -a
-ENV PYTHONPATH=${HOME}/app \
-    PYTHONUNBUFFERED=1 \
-    GRADIO_ALLOW_FLAGGING=never \
-    GRADIO_NUM_PORTS=1 \
-    GRADIO_SERVER_NAME=0.0.0.0 \
-    GRADIO_THEME=huggingface \
-    SYSTEM=spaces
-CMD ["python", "app.py"]
\ No newline at end of file
diff --git a/spaces/vpsrikanth/FaceSimilarity/app/__init__.py b/spaces/vpsrikanth/FaceSimilarity/app/__init__.py
deleted file mode 100644
index f102a9cadfa89ce554b3b26d2b90bfba2e05273c..0000000000000000000000000000000000000000
--- a/spaces/vpsrikanth/FaceSimilarity/app/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.0.1"
diff --git a/spaces/vumichien/Generate_human_motion/pyrender/pyrender/sampler.py b/spaces/vumichien/Generate_human_motion/pyrender/pyrender/sampler.py
deleted file mode 100644
index e4784d068f808a40a56c8e748d83175f7f4e6233..0000000000000000000000000000000000000000
--- a/spaces/vumichien/Generate_human_motion/pyrender/pyrender/sampler.py
+++ /dev/null
@@ -1,102 +0,0 @@
-"""Samplers, conforming to the glTF 2.0 standards as specified in
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-sampler
-
-Author: Matthew Matl
-"""
-from .constants import GLTF
-
-
-class Sampler(object):
-    """Texture sampler properties for filtering and wrapping modes.
-
-    Parameters
-    ----------
-    name : str, optional
-        The user-defined name of this object.
-    magFilter : int, optional
-        Magnification filter. Valid values:
-            - :attr:`.GLTF.NEAREST`
-            - :attr:`.GLTF.LINEAR`
-    minFilter : int, optional
-        Minification filter. Valid values:
-            - :attr:`.GLTF.NEAREST`
-            - :attr:`.GLTF.LINEAR`
-            - :attr:`.GLTF.NEAREST_MIPMAP_NEAREST`
-            - :attr:`.GLTF.LINEAR_MIPMAP_NEAREST`
-            - :attr:`.GLTF.NEAREST_MIPMAP_LINEAR`
-            - :attr:`.GLTF.LINEAR_MIPMAP_LINEAR`
-    wrapS : int, optional
-        S (U) wrapping mode. Valid values:
-            - :attr:`.GLTF.CLAMP_TO_EDGE`
-            - :attr:`.GLTF.MIRRORED_REPEAT`
-            - :attr:`.GLTF.REPEAT`
-    wrapT : int, optional
-        T (V) wrapping mode. Valid values:
-            - :attr:`.GLTF.CLAMP_TO_EDGE`
-            - :attr:`.GLTF.MIRRORED_REPEAT`
-            - :attr:`.GLTF.REPEAT`
-    """
-
-    def __init__(self,
-                 name=None,
-                 magFilter=None,
-                 minFilter=None,
-                 wrapS=GLTF.REPEAT,
-                 wrapT=GLTF.REPEAT):
-        self.name = name
-        self.magFilter = magFilter
-        self.minFilter = minFilter
-        self.wrapS = wrapS
-        self.wrapT = wrapT
-
-    @property
-    def name(self):
-        """str : The user-defined name of this object.
-        """
-        return self._name
-
-    @name.setter
-    def name(self, value):
-        if value is not None:
-            value = str(value)
-        self._name = value
-
-    @property
-    def magFilter(self):
-        """int : Magnification filter type.
-        """
-        return self._magFilter
-
-    @magFilter.setter
-    def magFilter(self, value):
-        self._magFilter = value
-
-    @property
-    def minFilter(self):
-        """int : Minification filter type.
-        """
-        return self._minFilter
-
-    @minFilter.setter
-    def minFilter(self, value):
-        self._minFilter = value
-
-    @property
-    def wrapS(self):
-        """int : S (U) wrapping mode.
-        """
-        return self._wrapS
-
-    @wrapS.setter
-    def wrapS(self, value):
-        self._wrapS = value
-
-    @property
-    def wrapT(self):
-        """int : T (V) wrapping mode.
-        """
-        return self._wrapT
-
-    @wrapT.setter
-    def wrapT(self, value):
-        self._wrapT = value
diff --git a/spaces/vumichien/canvas_controlnet/ldm/modules/diffusionmodules/upscaling.py b/spaces/vumichien/canvas_controlnet/ldm/modules/diffusionmodules/upscaling.py
deleted file mode 100644
index 03816662098ce1ffac79bd939b892e867ab91988..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/ldm/modules/diffusionmodules/upscaling.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
-from ldm.util import default
-
-
-class AbstractLowScaleModel(nn.Module):
-    # for concatenating a downsampled image to the latent representation
-    def __init__(self, noise_schedule_config=None):
-        super(AbstractLowScaleModel, self).__init__()
-        if noise_schedule_config is not None:
-            self.register_schedule(**noise_schedule_config)
-
-    def register_schedule(self, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                   cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def forward(self, x):
-        return x, None
-
-    def decode(self, x):
-        return x
-
-
-class SimpleImageConcat(AbstractLowScaleModel):
-    # no noise level conditioning
-    def __init__(self):
-        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
-        self.max_noise_level = 0
-
-    def forward(self, x):
-        # fix to constant noise level
-        return x, torch.zeros(x.shape[0], device=x.device).long()
-
-
-class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
-    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
-        super().__init__(noise_schedule_config=noise_schedule_config)
-        self.max_noise_level = max_noise_level
-
-    def forward(self, x, noise_level=None):
-        if noise_level is None:
-            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
-        else:
-            assert isinstance(noise_level, torch.Tensor)
-        z = self.q_sample(x, noise_level)
-        return z, noise_level
-
-
-
diff --git a/spaces/wangguanlin/vits_Kazari/modules.py b/spaces/wangguanlin/vits_Kazari/modules.py
deleted file mode 100644
index f5af1fd9a20dc03707889f360a39bb4b784a6df3..0000000000000000000000000000000000000000
--- a/spaces/wangguanlin/vits_Kazari/modules.py
+++ /dev/null
@@ -1,387 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/whitphx/gradio-static-test/dist/assets/Blocks-99723874.js b/spaces/whitphx/gradio-static-test/dist/assets/Blocks-99723874.js
deleted file mode 100644
index a128c6fbd4d9cebc6aeb6083508d67dcd553feeb..0000000000000000000000000000000000000000
--- a/spaces/whitphx/gradio-static-test/dist/assets/Blocks-99723874.js
+++ /dev/null
@@ -1,57 +0,0 @@
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i=this.parseTagName();if(this.bumpSpace(),this.bumpIf("/>"))return{val:{type:Q.literal,value:"<".concat(i,"/>"),location:U(r,this.clonePosition())},err:null};if(this.bumpIf(">")){var o=this.parseMessage(t+1,n,!0);if(o.err)return o;var l=o.val,a=this.clonePosition();if(this.bumpIf("</")){if(this.isEOF()||!st(this.char()))return this.error(D.INVALID_TAG,U(a,this.clonePosition()));var u=this.clonePosition(),s=this.parseTagName();return i!==s?this.error(D.UNMATCHED_CLOSING_TAG,U(u,this.clonePosition())):(this.bumpSpace(),this.bumpIf(">")?{val:{type:Q.tag,value:i,children:l,location:U(r,this.clonePosition())},err:null}:this.error(D.INVALID_TAG,U(a,this.clonePosition())))}else return this.error(D.UNCLOSED_TAG,U(r,this.clonePosition()))}else return this.error(D.INVALID_TAG,U(r,this.clonePosition()))},e.prototype.parseTagName=function(){var t=this.offset();for(this.bump();!this.isEOF()&&Pl(this.char());)this.bump();return this.message.slice(t,this.offset())},e.prototype.parseLiteral=function(t,n){for(var r=this.clonePosition(),i="";;){var o=this.tryParseQuote(n);if(o){i+=o;continue}var l=this.tryParseUnquoted(t,n);if(l){i+=l;continue}var a=this.tryParseLeftAngleBracket();if(a){i+=a;continue}break}var u=U(r,this.clonePosition());return{val:{type:Q.literal,value:i,location:u},err:null}},e.prototype.tryParseLeftAngleBracket=function(){return!this.isEOF()&&this.char()===60&&(this.ignoreTag||!Al(this.peek()||0))?(this.bump(),"<"):null},e.prototype.tryParseQuote=function(t){if(this.isEOF()||this.char()!==39)return null;switch(this.peek()){case 39:return this.bump(),this.bump(),"'";case 123:case 60:case 62:case 125:break;case 35:if(t==="plural"||t==="selectordinal")break;return null;default:return null}this.bump();var n=[this.char()];for(this.bump();!this.isEOF();){var r=this.char();if(r===39)if(this.peek()===39)n.push(39),this.bump();else{this.bump();break}else n.push(r);this.bump()}return lt.apply(void 0,n)},e.prototype.tryParseUnquoted=function(t,n){if(this.isEOF())return null;var r=this.char();return r===60||r===123||r===35&&(n==="plural"||n==="selectordinal")||r===125&&t>0?null:(this.bump(),lt(r))},e.prototype.parseArgument=function(t,n){var r=this.clonePosition();if(this.bump(),this.bumpSpace(),this.isEOF())return this.error(D.EXPECT_ARGUMENT_CLOSING_BRACE,U(r,this.clonePosition()));if(this.char()===125)return this.bump(),this.error(D.EMPTY_ARGUMENT,U(r,this.clonePosition()));var i=this.parseIdentifierIfPossible().value;if(!i)return this.error(D.MALFORMED_ARGUMENT,U(r,this.clonePosition()));if(this.bumpSpace(),this.isEOF())return this.error(D.EXPECT_ARGUMENT_CLOSING_BRACE,U(r,this.clonePosition()));switch(this.char()){case 125:return this.bump(),{val:{type:Q.argument,value:i,location:U(r,this.clonePosition())},err:null};case 44:return this.bump(),this.bumpSpace(),this.isEOF()?this.error(D.EXPECT_ARGUMENT_CLOSING_BRACE,U(r,this.clonePosition())):this.parseArgumentOptions(t,n,i,r);default:return this.error(D.MALFORMED_ARGUMENT,U(r,this.clonePosition()))}},e.prototype.parseIdentifierIfPossible=function(){var t=this.clonePosition(),n=this.offset(),r=at(this.message,n),i=n+r.length;this.bumpTo(i);var o=this.clonePosition(),l=U(t,o);return{value:r,location:l}},e.prototype.parseArgumentOptions=function(t,n,r,i){var o,l=this.clonePosition(),a=this.parseIdentifierIfPossible().value,u=this.clonePosition();switch(a){case"":return this.error(D.EXPECT_ARGUMENT_TYPE,U(l,u));case"number":case"date":case"time":{this.bumpSpace();var s=null;if(this.bumpIf(",")){this.bumpSpace();var f=this.clonePosition(),p=this.parseSimpleArgStyleIfPossible();if(p.err)return p;var _=wl(p.val);if(_.length===0)return this.error(D.EXPECT_ARGUMENT_STYLE,U(this.clonePosition(),this.clonePosition()));var h=U(f,this.clonePosition());s={style:_,styleLocation:h}}var g=this.tryParseArgumentClose(i);if(g.err)return g;var m=U(i,this.clonePosition());if(s&&It(s?.style,"::",0)){var b=Tl(s.style.slice(2));if(a==="number"){var p=this.parseNumberSkeletonFromString(b,s.styleLocation);return p.err?p:{val:{type:Q.number,value:r,location:m,style:p.val},err:null}}else{if(b.length===0)return this.error(D.EXPECT_DATE_TIME_SKELETON,m);var H=b;this.locale&&(H=fl(b,this.locale));var _={type:Se.dateTime,pattern:H,location:s.styleLocation,parsedOptions:this.shouldParseSkeletons?rl(H):{}},A=a==="date"?Q.date:Q.time;return{val:{type:A,value:r,location:m,style:_},err:null}}}return{val:{type:a==="number"?Q.number:a==="date"?Q.date:Q.time,value:r,location:m,style:(o=s?.style)!==null&&o!==void 0?o:null},err:null}}case"plural":case"selectordinal":case"select":{var y=this.clonePosition();if(this.bumpSpace(),!this.bumpIf(","))return this.error(D.EXPECT_SELECT_ARGUMENT_OPTIONS,U(y,X({},y)));this.bumpSpace();var c=this.parseIdentifierIfPossible(),O=0;if(a!=="select"&&c.value==="offset"){if(!this.bumpIf(":"))return this.error(D.EXPECT_PLURAL_ARGUMENT_OFFSET_VALUE,U(this.clonePosition(),this.clonePosition()));this.bumpSpace();var p=this.tryParseDecimalInteger(D.EXPECT_PLURAL_ARGUMENT_OFFSET_VALUE,D.INVALID_PLURAL_ARGUMENT_OFFSET_VALUE);if(p.err)return p;this.bumpSpace(),c=this.parseIdentifierIfPossible(),O=p.val}var k=this.tryParsePluralOrSelectOptions(t,a,n,c);if(k.err)return k;var g=this.tryParseArgumentClose(i);if(g.err)return g;var P=U(i,this.clonePosition());return a==="select"?{val:{type:Q.select,value:r,options:At(k.val),location:P},err:null}:{val:{type:Q.plural,value:r,options:At(k.val),offset:O,pluralType:a==="plural"?"cardinal":"ordinal",location:P},err:null}}default:return this.error(D.INVALID_ARGUMENT_TYPE,U(l,u))}},e.prototype.tryParseArgumentClose=function(t){return this.isEOF()||this.char()!==125?this.error(D.EXPECT_ARGUMENT_CLOSING_BRACE,U(t,this.clonePosition())):(this.bump(),{val:!0,err:null})},e.prototype.parseSimpleArgStyleIfPossible=function(){for(var t=0,n=this.clonePosition();!this.isEOF();){var r=this.char();switch(r){case 39:{this.bump();var i=this.clonePosition();if(!this.bumpUntil("'"))return this.error(D.UNCLOSED_QUOTE_IN_ARGUMENT_STYLE,U(i,this.clonePosition()));this.bump();break}case 123:{t+=1,this.bump();break}case 125:{if(t>0)t-=1;else return{val:this.message.slice(n.offset,this.offset()),err:null};break}default:this.bump();break}}return{val:this.message.slice(n.offset,this.offset()),err:null}},e.prototype.parseNumberSkeletonFromString=function(t,n){var r=[];try{r=ol(t)}catch{return this.error(D.INVALID_NUMBER_SKELETON,n)}return{val:{type:Se.number,tokens:r,location:n,parsedOptions:this.shouldParseSkeletons?ul(r):{}},err:null}},e.prototype.tryParsePluralOrSelectOptions=function(t,n,r,i){for(var o,l=!1,a=[],u=new Set,s=i.value,f=i.location;;){if(s.length===0){var p=this.clonePosition();if(n!=="select"&&this.bumpIf("=")){var _=this.tryParseDecimalInteger(D.EXPECT_PLURAL_ARGUMENT_SELECTOR,D.INVALID_PLURAL_ARGUMENT_SELECTOR);if(_.err)return _;f=U(p,this.clonePosition()),s=this.message.slice(p.offset,this.offset())}else break}if(u.has(s))return this.error(n==="select"?D.DUPLICATE_SELECT_ARGUMENT_SELECTOR:D.DUPLICATE_PLURAL_ARGUMENT_SELECTOR,f);s==="other"&&(l=!0),this.bumpSpace();var h=this.clonePosition();if(!this.bumpIf("{"))return this.error(n==="select"?D.EXPECT_SELECT_ARGUMENT_SELECTOR_FRAGMENT:D.EXPECT_PLURAL_ARGUMENT_SELECTOR_FRAGMENT,U(this.clonePosition(),this.clonePosition()));var g=this.parseMessage(t+1,n,r);if(g.err)return g;var m=this.tryParseArgumentClose(h);if(m.err)return m;a.push([s,{value:g.val,location:U(h,this.clonePosition())}]),u.add(s),this.bumpSpace(),o=this.parseIdentifierIfPossible(),s=o.value,f=o.location}return a.length===0?this.error(n==="select"?D.EXPECT_SELECT_ARGUMENT_SELECTOR:D.EXPECT_PLURAL_ARGUMENT_SELECTOR,U(this.clonePosition(),this.clonePosition())):this.requiresOtherClause&&!l?this.error(D.MISSING_OTHER_CLAUSE,U(this.clonePosition(),this.clonePosition())):{val:a,err:null}},e.prototype.tryParseDecimalInteger=function(t,n){var r=1,i=this.clonePosition();this.bumpIf("+")||this.bumpIf("-")&&(r=-1);for(var o=!1,l=0;!this.isEOF();){var a=this.char();if(a>=48&&a<=57)o=!0,l=l*10+(a-48),this.bump();else break}var u=U(i,this.clonePosition());return o?(l*=r,El(l)?{val:l,err:null}:this.error(n,u)):this.error(t,u)},e.prototype.offset=function(){return this.position.offset},e.prototype.isEOF=function(){return this.offset()===this.message.length},e.prototype.clonePosition=function(){return{offset:this.position.offset,line:this.position.line,column:this.position.column}},e.prototype.char=function(){var t=this.position.offset;if(t>=this.message.length)throw Error("out of bound");var n=On(this.message,t);if(n===void 0)throw Error("Offset ".concat(t," is at invalid UTF-16 code unit boundary"));return n},e.prototype.error=function(t,n){return{val:null,err:{kind:t,message:this.message,location:n}}},e.prototype.bump=function(){if(!this.isEOF()){var t=this.char();t===10?(this.position.line+=1,this.position.column=1,this.position.offset+=1):(this.position.column+=1,this.position.offset+=t<65536?1:2)}},e.prototype.bumpIf=function(t){if(It(this.message,t,this.offset())){for(var n=0;n<t.length;n++)this.bump();return!0}return!1},e.prototype.bumpUntil=function(t){var n=this.offset(),r=this.message.indexOf(t,n);return r>=0?(this.bumpTo(r),!0):(this.bumpTo(this.message.length),!1)},e.prototype.bumpTo=function(t){if(this.offset()>t)throw Error("targetOffset ".concat(t," must be greater than or equal to the current offset ").concat(this.offset()));for(t=Math.min(t,this.message.length);;){var n=this.offset();if(n===t)break;if(n>t)throw Error("targetOffset ".concat(t," is at 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diff --git a/spaces/windmaple/stable-diffusion-2/style.css b/spaces/windmaple/stable-diffusion-2/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/windmaple/stable-diffusion-2/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/wyysf/GenMM/utils/base.py b/spaces/wyysf/GenMM/utils/base.py
deleted file mode 100644
index f8b3a9808470d6a1746bbfe9872471c5494f7e62..0000000000000000000000000000000000000000
--- a/spaces/wyysf/GenMM/utils/base.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import os
-import os.path as osp
-import sys
-import time
-import yaml
-import imageio
-import random
-import shutil
-import random
-import numpy as np
-import torch
-from tqdm import tqdm
-import matplotlib.pyplot as plt
-
-class ConfigParser():
-    def __init__(self, args):
-        """
-        class to parse configuration.
-        """
-        args = args.parse_args()
-        self.cfg = self.merge_config_file(args)
-
-        # set random seed
-        self.set_seed()
-
-    def __str__(self):
-        return str(self.cfg.__dict__)
-
-    def __getattr__(self, name):
-        """
-        Access items use dot.notation.
-        """
-        return self.cfg.__dict__[name]
-
-    def __getitem__(self, name):
-        """
-        Access items like ordinary dict.
-        """
-        return self.cfg.__dict__[name]
-
-    def merge_config_file(self, args, allow_invalid=True):
-        """
-        Load json config file and merge the arguments
-        """
-        assert args.config is not None
-        with open(args.config, 'r') as f:
-            cfg = yaml.safe_load(f)
-            if 'config' in cfg.keys():
-                del cfg['config']
-        f.close()
-        invalid_args = list(set(cfg.keys()) - set(dir(args)))
-        if invalid_args and not allow_invalid:
-            raise ValueError(f"Invalid args {invalid_args} in {args.config}.")
-        
-        for k in list(cfg.keys()):
-            if k in args.__dict__.keys() and args.__dict__[k] is not None:
-                print('=========>  overwrite config: {} = {}'.format(k, args.__dict__[k]))
-                del cfg[k]
-
-        args.__dict__.update(cfg)
-
-        return args
-
-    def set_seed(self):
-        ''' set random seed for random, numpy and torch. '''
-        if 'seed' not in self.cfg.__dict__.keys():
-            return
-        if self.cfg.seed is None:
-            self.cfg.seed = int(time.time()) % 1000000
-        print('=========>  set random seed: {}'.format(self.cfg.seed))
-        # fix random seeds for reproducibility
-        random.seed(self.cfg.seed)
-        np.random.seed(self.cfg.seed)
-        torch.manual_seed(self.cfg.seed)
-        torch.cuda.manual_seed(self.cfg.seed)
-
-    def save_codes_and_config(self, save_path):
-        """
-        save codes and config to $save_path.
-        """
-        cur_codes_path = osp.dirname(osp.dirname(os.path.abspath(__file__)))
-        if os.path.exists(save_path):
-            shutil.rmtree(save_path)
-        shutil.copytree(cur_codes_path, osp.join(save_path, 'codes'), \
-            ignore=shutil.ignore_patterns('*debug*', '*data*', '*output*', '*exps*', '*.txt', '*.json', '*.mp4', '*.png', '*.jpg', '*.bvh', '*.csv', '*.pth', '*.tar', '*.npz'))
-
-        with open(osp.join(save_path, 'config.yaml'), 'w') as f:
-            f.write(yaml.dump(self.cfg.__dict__))
-        f.close()
-
-
-# other utils
-class logger:
-    """Keeps track of the levels and steps of optimization. Logs it via TQDM"""
-    def __init__(self, n_steps, n_lvls):
-        self.n_steps = n_steps
-        self.n_lvls = n_lvls
-        self.lvl = -1
-        self.lvl_step = 0
-        self.steps = 0
-        self.pbar = tqdm(total=self.n_lvls * self.n_steps, desc='Starting')
-
-    def step(self):
-        self.pbar.update(1)
-        self.steps += 1
-        self.lvl_step += 1
-
-    def new_lvl(self):
-        self.lvl += 1
-        self.lvl_step = 0
-
-    def print(self):
-        self.pbar.set_description(f'Lvl {self.lvl}/{self.n_lvls-1}, step {self.lvl_step}/{self.n_steps}')
-
-
-def set_seed(seed):
-    if seed is not None:
-        random.seed(seed)
-        np.random.seed(seed)
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
-
-
-# debug utils
-def draw_trajectory(trajectory, save_path=None, anim=True):
-    r = max(abs(trajectory.min()), trajectory.max())
-    if anim:
-        imgs = []
-        for i in tqdm(range(1, trajectory.shape[0])):
-            plt.plot(trajectory[:i, 0], trajectory[:i, 2], color='red')
-            plt.xlim(-r-1, r+1)
-            plt.ylim(-r-1, r+1)
-            plt.savefig(save_path + '.png')
-            imgs += [imageio.imread(save_path + '.png')]
-        imageio.mimwrite(save_path + '.mp4', imgs)
-        plt.close()
-    else:
-        # plt.scatter(trajectory[:, 0], trajectory[:, 1], trajectory[:, 2])
-        plt.plot(trajectory[:, 0], trajectory[:, 2], color='red')
-        plt.xlim(-r*1.5, r*1.5)
-        plt.ylim(-r*1.5, r*1.5)
-        if save_path is not None:
-            plt.savefig(save_path + '.png')
-            plt.close()
-
-    # velo = self.raw_motion[0, self.mask, :].numpy()
-    # print(velo.shape)
-    # imgs = []
diff --git a/spaces/xdecoder/Demo/tasks/ref_cap.py b/spaces/xdecoder/Demo/tasks/ref_cap.py
deleted file mode 100644
index 76cd1fd34a038db0fd7a8818ff7a7c764bfb040d..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Demo/tasks/ref_cap.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# --------------------------------------------------------
-# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
-# Copyright (c) 2022 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Xueyan Zou (xueyan@cs.wisc.edu)
-# --------------------------------------------------------
-
-import torch
-import torch.nn.functional as F
-import numpy as np
-from PIL import Image
-from torchvision import transforms
-from utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog
-
-t = []
-t.append(transforms.Resize(224, interpolation=Image.BICUBIC))
-transform_ret = transforms.Compose(t)
-t = []
-t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
-transform_grd = transforms.Compose(t)
-
-metedata = MetadataCatalog.get('coco_2017_train_panoptic')
-
-def referring_captioning(model, image, texts, inpainting_text, *args, **kwargs):
-    model_last, model_cap = model
-    with torch.no_grad():
-        image_ori = image
-        image = transform_grd(image)
-        width = image.size[0]
-        height = image.size[1]
-        image = np.asarray(image)
-        image_ori_ = image
-        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
-        texts_input = [[texts.strip() if texts.endswith('.') else (texts + '.')]]
-
-        batch_inputs = [{'image': images, 'groundings': {'texts':texts_input}, 'height': height, 'width': width}]
-        outputs = model_last.model.evaluate_grounding(batch_inputs, None)
-
-        grd_mask = (outputs[-1]['grounding_mask'] > 0).float()
-        grd_mask_ = (1 - F.interpolate(grd_mask[None,], (224, 224), mode='nearest')[0]).bool()
-
-        color = [252/255, 91/255, 129/255]
-        visual = Visualizer(image_ori_, metadata=metedata)
-        demo = visual.draw_binary_mask(grd_mask.cpu().numpy()[0], color=color, text=texts)
-        res = demo.get_image()
-
-        if (1 - grd_mask_.float()).sum() < 5:
-            torch.cuda.empty_cache()
-            return Image.fromarray(res), 'n/a', None
-
-        grd_mask_ = grd_mask_ * 0
-        image = transform_ret(image_ori)
-        image_ori = np.asarray(image_ori)
-        image = np.asarray(image)
-        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
-        batch_inputs = [{'image': images, 'image_id': 0, 'captioning_mask': grd_mask_}]
-
-        token_text = texts.replace('.','') if texts.endswith('.') else texts
-        token = model_cap.model.sem_seg_head.predictor.lang_encoder.tokenizer.encode(token_text)
-        token = torch.tensor(token)[None,:-1]
-
-        outputs = model_cap.model.evaluate_captioning(batch_inputs, extra={'token': token})
-        # outputs = model_cap.model.evaluate_captioning(batch_inputs, extra={})
-        text = outputs[-1]['captioning_text']
-
-    torch.cuda.empty_cache()
-    return Image.fromarray(res), text, None 
\ No newline at end of file
diff --git a/spaces/xiang-wuu/yolov5/utils/autoanchor.py b/spaces/xiang-wuu/yolov5/utils/autoanchor.py
deleted file mode 100644
index 1a4c52141bc68d9cb390a033eda90eddc2f235f7..0000000000000000000000000000000000000000
--- a/spaces/xiang-wuu/yolov5/utils/autoanchor.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-AutoAnchor utils
-"""
-
-import random
-
-import numpy as np
-import torch
-import yaml
-from tqdm import tqdm
-
-from utils.general import LOGGER, colorstr, emojis
-
-PREFIX = colorstr('AutoAnchor: ')
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = m.anchors.prod(-1).mean(-1).view(-1)  # mean anchor area per output layer
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da and (da.sign() != ds.sign()):  # same order
-        LOGGER.info(f'{PREFIX}Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1 / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1 / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    stride = m.stride.to(m.anchors.device).view(-1, 1, 1)  # model strides
-    anchors = m.anchors.clone() * stride  # current anchors
-    bpr, aat = metric(anchors.cpu().view(-1, 2))
-    s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
-    if bpr > 0.98:  # threshold to recompute
-        LOGGER.info(emojis(f'{s}Current anchors are a good fit to dataset ✅'))
-    else:
-        LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...'))
-        na = m.anchors.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            LOGGER.info(f'{PREFIX}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchors[:] = anchors.clone().view_as(m.anchors)
-            check_anchor_order(m)  # must be in pixel-space (not grid-space)
-            m.anchors /= stride
-            s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)'
-        else:
-            s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)'
-        LOGGER.info(emojis(s))
-
-
-def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            dataset: path to data.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    from scipy.cluster.vq import kmeans
-
-    npr = np.random
-    thr = 1 / thr
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k, verbose=True):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
-            f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
-            f'past_thr={x[x > thr].mean():.3f}-mean: '
-        for x in k:
-            s += '%i,%i, ' % (round(x[0]), round(x[1]))
-        if verbose:
-            LOGGER.info(s[:-2])
-        return k
-
-    if isinstance(dataset, str):  # *.yaml file
-        with open(dataset, errors='ignore') as f:
-            data_dict = yaml.safe_load(f)  # model dict
-        from utils.dataloaders import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found: {i} of {len(wh0)} labels are < 3 pixels in size')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans init
-    try:
-        LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
-        assert n <= len(wh)  # apply overdetermined constraint
-        s = wh.std(0)  # sigmas for whitening
-        k = kmeans(wh / s, n, iter=30)[0] * s  # points
-        assert n == len(k)  # kmeans may return fewer points than requested if wh is insufficient or too similar
-    except Exception:
-        LOGGER.warning(f'{PREFIX}WARNING: switching strategies from kmeans to random init')
-        k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size  # random init
-    wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
-    k = print_results(k, verbose=False)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k, verbose)
-
-    return print_results(k)
diff --git a/spaces/xiaofenglingreal/Remove-Animation-Figures-Background/README.md b/spaces/xiaofenglingreal/Remove-Animation-Figures-Background/README.md
deleted file mode 100644
index 9c89e7099fe5d26c331ff4e343b54fb85b150870..0000000000000000000000000000000000000000
--- a/spaces/xiaofenglingreal/Remove-Animation-Figures-Background/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Remove Animation Figures Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
deleted file mode 100644
index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : __init__.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# 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 set_sbn_eps_mode
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .batchnorm import patch_sync_batchnorm, convert_model
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/xp3857/Image_Restoration_Colorization/README.md b/spaces/xp3857/Image_Restoration_Colorization/README.md
deleted file mode 100644
index e464c759c7b0f5be457d654514b4a880ebdb0927..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Image Restoration and Colorization
-emoji: 🌟
-colorFrom: blue
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
-duplicated_from: manhkhanhUIT/Image_Restoration_Colorization
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
\ No newline at end of file
diff --git a/spaces/xuqinyang/Baichuan-13B-Chat-Int4-Cpp/app.py b/spaces/xuqinyang/Baichuan-13B-Chat-Int4-Cpp/app.py
deleted file mode 100644
index d1c520f2cd78cb4ff3e97e040f39d8963d707ab3..0000000000000000000000000000000000000000
--- a/spaces/xuqinyang/Baichuan-13B-Chat-Int4-Cpp/app.py
+++ /dev/null
@@ -1,250 +0,0 @@
-from typing import Iterator
-
-import gradio as gr
-
-
-from model import run
-
-DEFAULT_SYSTEM_PROMPT = ""
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = 4000
-
-DESCRIPTION = """
-# Baichuan-13B-Chat
-"""
-LICENSE=""
-
-
-
-def clear_and_save_textbox(message: str) -> tuple[str, str]:
-    return '', message
-
-
-def display_input(message: str,
-                  history: list[tuple[str, str]]) -> list[tuple[str, str]]:
-    history.append((message, ''))
-    return history
-
-
-def delete_prev_fn(
-        history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
-    try:
-        message, _ = history.pop()
-    except IndexError:
-        message = ''
-    return history, message or ''
-
-
-def generate(
-    message: str,
-    history_with_input: list[tuple[str, str]],
-    system_prompt: str,
-    max_new_tokens: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-) -> Iterator[list[tuple[str, str]]]:
-
-    history = history_with_input[:-1]
-    generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
-    for response in generator:
-        yield history + [(message, response)]
-
-
-def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
-    generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 8192, 1, 0.95, 50)
-    for x in generator:
-        pass
-    return '', x
-
-
-def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None:
-    a = 1
-
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value='Duplicate Space for private use',
-                       elem_id='duplicate-button')
-
-    with gr.Group():
-        chatbot = gr.Chatbot(label='Chatbot')
-        with gr.Row():
-            textbox = gr.Textbox(
-                container=False,
-                show_label=False,
-                placeholder='Type a message...',
-                scale=10,
-            )
-            submit_button = gr.Button('Submit',
-                                      variant='primary',
-                                      scale=1,
-                                      min_width=0)
-    with gr.Row():
-        retry_button = gr.Button('🔄  Retry', variant='secondary')
-        undo_button = gr.Button('↩️ Undo', variant='secondary')
-        clear_button = gr.Button('🗑️  Clear', variant='secondary')
-
-    saved_input = gr.State()
-
-    with gr.Accordion(label='Advanced options', open=False):
-        system_prompt = gr.Textbox(label='System prompt',
-                                   value=DEFAULT_SYSTEM_PROMPT,
-                                   lines=6)
-        max_new_tokens = gr.Slider(
-            label='Max new tokens',
-            minimum=1,
-            maximum=MAX_MAX_NEW_TOKENS,
-            step=1,
-            value=DEFAULT_MAX_NEW_TOKENS,
-        )
-        temperature = gr.Slider(
-            label='Temperature',
-            minimum=0.1,
-            maximum=4.0,
-            step=0.1,
-            value=0.3,
-        )
-        top_p = gr.Slider(
-            label='Top-p (nucleus sampling)',
-            minimum=0.05,
-            maximum=1.0,
-            step=0.05,
-            value=0.85,
-        )
-        top_k = gr.Slider(
-            label='Top-k',
-            minimum=1,
-            maximum=1000,
-            step=1,
-            value=5,
-        )
-
-    gr.Examples(
-        examples=[
-            '世界上第二高的山峰是哪座',
-
-        ],
-        inputs=textbox,
-        outputs=[textbox, chatbot],
-        fn=process_example,
-        cache_examples=True,
-    )
-
-    gr.Markdown(LICENSE)
-
-    textbox.submit(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    button_event_preprocess = submit_button.click(
-        fn=clear_and_save_textbox,
-        inputs=textbox,
-        outputs=[textbox, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=check_input_token_length,
-        inputs=[saved_input, chatbot, system_prompt],
-        api_name=False,
-        queue=False,
-    ).success(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    retry_button.click(
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=display_input,
-        inputs=[saved_input, chatbot],
-        outputs=chatbot,
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=generate,
-        inputs=[
-            saved_input,
-            chatbot,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-        ],
-        outputs=chatbot,
-        api_name=False,
-    )
-
-    undo_button.click(
-
-        fn=delete_prev_fn,
-        inputs=chatbot,
-        outputs=[chatbot, saved_input],
-        api_name=False,
-        queue=False,
-    ).then(
-        fn=lambda x: x,
-        inputs=[saved_input],
-        outputs=textbox,
-        api_name=False,
-        queue=False,
-    )
-
-    clear_button.click(
-        fn=lambda: ([], ''),
-        outputs=[chatbot, saved_input],
-        queue=False,
-        api_name=False,
-    )
-
-demo.queue(max_size=20).launch()
diff --git a/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/train.py b/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/train.py
deleted file mode 100644
index 5097ff26a590abd8fb47b4875c8ea71db79f7a02..0000000000000000000000000000000000000000
--- a/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/train.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# flake8: noqa
-import os.path as osp
-from basicsr.train import train_pipeline
-
-import realesrgan.archs
-import realesrgan.data
-import realesrgan.models
-
-if __name__ == "__main__":
-    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
-    train_pipeline(root_path)
diff --git a/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/generate_meta_info_pairdata.py b/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 58c4c1f07566a95622d6e350b0a552496e3a179e..0000000000000000000000000000000000000000
--- a/spaces/yangheng/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
-    txt_file = open(args.meta_info, "w")
-    # sca images
-    img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], "*")))
-    img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], "*")))
-
-    assert len(img_paths_gt) == len(img_paths_lq), (
-        "GT folder and LQ folder should have the same length, but got "
-        f"{len(img_paths_gt)} and {len(img_paths_lq)}."
-    )
-
-    for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
-        # get the relative paths
-        img_name_gt = os.path.relpath(img_path_gt, args.root[0])
-        img_name_lq = os.path.relpath(img_path_lq, args.root[1])
-        print(f"{img_name_gt}, {img_name_lq}")
-        txt_file.write(f"{img_name_gt}, {img_name_lq}\n")
-
-
-if __name__ == "__main__":
-    """This script is used to generate meta info (txt file) for paired images."""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--input",
-        nargs="+",
-        default=[
-            "datasets/DF2K/DIV2K_train_HR_sub",
-            "datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub",
-        ],
-        help="Input folder, should be [gt_folder, lq_folder]",
-    )
-    parser.add_argument(
-        "--root", nargs="+", default=[None, None], help="Folder root, will use the "
-    )
-    parser.add_argument(
-        "--meta_info",
-        type=str,
-        default="datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt",
-        help="txt path for meta info",
-    )
-    args = parser.parse_args()
-
-    assert (
-        len(args.input) == 2
-    ), "Input folder should have two elements: gt folder and lq folder"
-    assert (
-        len(args.root) == 2
-    ), "Root path should have two elements: root for gt folder and lq folder"
-    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
-    for i in range(2):
-        if args.input[i].endswith("/"):
-            args.input[i] = args.input[i][:-1]
-        if args.root[i] is None:
-            args.root[i] = os.path.dirname(args.input[i])
-
-    main(args)
diff --git a/spaces/ybelkada/interfacegan_pp/torch_utils/ops/grid_sample_gradfix.py b/spaces/ybelkada/interfacegan_pp/torch_utils/ops/grid_sample_gradfix.py
deleted file mode 100644
index 979ee831b232c68b8c271be9e376c70c57a31b02..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/interfacegan_pp/torch_utils/ops/grid_sample_gradfix.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False  # Enable the custom op by setting this to true.
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
-    if _should_use_custom_op():
-        return _GridSample2dForward.apply(input, grid)
-    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
-    return enabled
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dForward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, grid):
-        assert input.ndim == 4
-        assert grid.ndim == 4
-        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-        ctx.save_for_backward(input, grid)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, grid = ctx.saved_tensors
-        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
-        return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dBackward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, grad_output, input, grid):
-        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
-        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
-        ctx.save_for_backward(grid)
-        return grad_input, grad_grid
-
-    @staticmethod
-    def backward(ctx, grad2_grad_input, grad2_grad_grid):
-        _ = grad2_grad_grid # unused
-        grid, = ctx.saved_tensors
-        grad2_grad_output = None
-        grad2_input = None
-        grad2_grid = None
-
-        if ctx.needs_input_grad[0]:
-            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
-
-        assert not ctx.needs_input_grad[2]
-        return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/PianoRollToolbar/VolumeSlider.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/PianoRollToolbar/VolumeSlider.tsx
deleted file mode 100644
index 0d7a4b83bc021c2a196702f2ef6d2e46175081e8..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/PianoRollToolbar/VolumeSlider.tsx
+++ /dev/null
@@ -1,51 +0,0 @@
-import styled from "@emotion/styled"
-import VolumeUp from "mdi-react/VolumeHighIcon"
-import { observer } from "mobx-react-lite"
-import React, { FC, useCallback } from "react"
-import { Slider } from "../../../components/Slider"
-import { setTrackVolume } from "../../actions"
-import { useStores } from "../../hooks/useStores"
-
-const Container = styled.div`
-  display: flex;
-  width: 8rem;
-  margin-left: 1rem;
-  margin-right: 1rem;
-  align-items: center;
-`
-
-const VolumeIcon = styled(VolumeUp)`
-  width: 2rem;
-  height: 2rem;
-  color: ${({ theme }) => theme.secondaryTextColor};
-  margin-right: 0.5rem;
-`
-
-export interface VolumeSliderProps {
-  trackId: number
-}
-
-const _VolumeSlider: FC<VolumeSliderProps> = observer(({ trackId }) => {
-  const rootStore = useStores()
-  const {
-    pianoRollStore: { currentVolume },
-  } = rootStore
-  const volume = currentVolume ?? 100
-  const onChange = useCallback(
-    (value: number) => setTrackVolume(rootStore)(trackId, value),
-    [rootStore, trackId],
-  )
-  return (
-    <Container>
-      <VolumeIcon />
-      <Slider
-        value={volume}
-        onChange={(value) => onChange(value)}
-        max={127}
-        minStepsBetweenThumbs={1}
-      />
-    </Container>
-  )
-})
-
-export const VolumeSlider = React.memo(_VolumeSlider)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/blenderbot_small/modeling_blenderbot_small.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/blenderbot_small/modeling_blenderbot_small.py
deleted file mode 100644
index a1e888aec90807b4af85626bfa6c8d7dc65313cf..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/blenderbot_small/modeling_blenderbot_small.py
+++ /dev/null
@@ -1,1608 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch BlenderbotSmall model."""
-
-
-import copy
-import math
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from ...activations import ACT2FN
-from ...modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPastAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    Seq2SeqLMOutput,
-    Seq2SeqModelOutput,
-)
-from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    add_end_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_blenderbot_small import BlenderbotSmallConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "BlenderbotSmallConfig"
-
-
-BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "facebook/blenderbot_small-90M",
-    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
-]
-
-
-# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
-def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
-    """
-    Shift input ids one token to the right.
-    """
-    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
-    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
-    shifted_input_ids[:, 0] = decoder_start_token_id
-
-    if pad_token_id is None:
-        raise ValueError("self.model.config.pad_token_id has to be defined.")
-    # replace possible -100 values in labels by `pad_token_id`
-    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
-    return shifted_input_ids
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-# Copied from transformers.models.blenderbot.modeling_blenderbot.BlenderbotLearnedPositionalEmbedding with Blenderbot->BlenderbotSmall
-class BlenderbotSmallLearnedPositionalEmbedding(nn.Embedding):
-    """
-    This module learns positional embeddings up to a fixed maximum size.
-    """
-
-    def __init__(self, num_embeddings: int, embedding_dim: int):
-        super().__init__(num_embeddings, embedding_dim)
-
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
-        """`input_ids_shape` is expected to be [bsz x seqlen]."""
-        bsz, seq_len = input_ids_shape[:2]
-        positions = torch.arange(
-            past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
-        )
-        return super().forward(positions)
-
-
-# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BlenderbotSmall
-class BlenderbotSmallAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        is_decoder: bool = False,
-        bias: bool = True,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-
-        if (self.head_dim * num_heads) != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
-                f" and `num_heads`: {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-        self.is_decoder = is_decoder
-
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scaling
-        # get key, value proj
-        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
-        # is checking that the `sequence_length` of the `past_key_value` is the same as
-        # the provided `key_value_states` to support prefix tuning
-        if (
-            is_cross_attention
-            and past_key_value is not None
-            and past_key_value[0].shape[2] == key_value_states.shape[1]
-        ):
-            # reuse k,v, cross_attentions
-            key_states = past_key_value[0]
-            value_states = past_key_value[1]
-        elif is_cross_attention:
-            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
-            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
-        elif past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-        else:
-            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_states, value_states)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.reshape(*proj_shape)
-        value_states = value_states.reshape(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            if layer_head_mask.size() != (self.num_heads,):
-                raise ValueError(
-                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
-                    f" {layer_head_mask.size()}"
-                )
-            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to be reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned across GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped, past_key_value
-
-
-# Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->BlenderbotSmall
-class BlenderbotSmallEncoderLayer(nn.Module):
-    def __init__(self, config: BlenderbotSmallConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-        self.self_attn = BlenderbotSmallAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.encoder_attention_heads,
-            dropout=config.attention_dropout,
-        )
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: torch.FloatTensor,
-        layer_head_mask: torch.FloatTensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                `(encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-        hidden_states, attn_weights, _ = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        if hidden_states.dtype == torch.float16 and (
-            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
-        ):
-            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-# Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->BlenderbotSmall
-class BlenderbotSmallDecoderLayer(nn.Module):
-    def __init__(self, config: BlenderbotSmallConfig):
-        super().__init__()
-        self.embed_dim = config.d_model
-
-        self.self_attn = BlenderbotSmallAttention(
-            embed_dim=self.embed_dim,
-            num_heads=config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-            is_decoder=True,
-        )
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.encoder_attn = BlenderbotSmallAttention(
-            self.embed_dim,
-            config.decoder_attention_heads,
-            dropout=config.attention_dropout,
-            is_decoder=True,
-        )
-        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
-        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = True,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            encoder_hidden_states (`torch.FloatTensor`):
-                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
-            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                `(encoder_attention_heads,)`.
-            cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
-                size `(decoder_attention_heads,)`.
-            past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        # Self Attention
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        # add present self-attn cache to positions 1,2 of present_key_value tuple
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            past_key_value=self_attn_past_key_value,
-            attention_mask=attention_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        # Cross-Attention Block
-        cross_attn_present_key_value = None
-        cross_attn_weights = None
-        if encoder_hidden_states is not None:
-            residual = hidden_states
-
-            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
-            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
-            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
-                hidden_states=hidden_states,
-                key_value_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-                layer_head_mask=cross_attn_layer_head_mask,
-                past_key_value=cross_attn_past_key_value,
-                output_attentions=output_attentions,
-            )
-            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-            hidden_states = residual + hidden_states
-            hidden_states = self.encoder_attn_layer_norm(hidden_states)
-
-            # add cross-attn to positions 3,4 of present_key_value tuple
-            present_key_value = present_key_value + cross_attn_present_key_value
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights, cross_attn_weights)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-class BlenderbotSmallPreTrainedModel(PreTrainedModel):
-    config_class = BlenderbotSmallConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        std = self.config.init_std
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (BlenderbotSmallDecoder, BlenderbotSmallEncoder)):
-            module.gradient_checkpointing = value
-
-    @property
-    def dummy_inputs(self):
-        pad_token = self.config.pad_token_id
-        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
-        dummy_inputs = {
-            "attention_mask": input_ids.ne(pad_token),
-            "input_ids": input_ids,
-            "decoder_input_ids": input_ids,
-        }
-        return dummy_inputs
-
-
-BLENDERBOT_SMALL_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`BlenderbotSmallConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-BLENDERBOT_SMALL_GENERATION_EXAMPLE = r"""
-    Conversation example:
-
-    ```python
-    >>> from transformers import AutoTokenizer, BlenderbotSmallForConditionalGeneration
-
-    >>> mname = "facebook/blenderbot_small-90M"
-    >>> model = BlenderbotSmallForConditionalGeneration.from_pretrained(mname)
-    >>> tokenizer = AutoTokenizer.from_pretrained(mname)
-    >>> UTTERANCE = "My friends are cool but they eat too many carbs."
-    >>> print("Human: ", UTTERANCE)
-    Human:  My friends are cool but they eat too many carbs.
-
-    >>> inputs = tokenizer([UTTERANCE], return_tensors="pt")
-    >>> reply_ids = model.generate(**inputs)
-    >>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0])
-    Bot:  what kind of carbs do they eat? i don't know much about carbs.
-
-    >>> REPLY = "I'm not sure"
-    >>> print("Human: ", REPLY)
-    Human: I'm not sure
-
-    >>> NEXT_UTTERANCE = (
-    ...     "My friends are cool but they eat too many carbs.__end__ __start__what kind of carbs do they eat? "
-    ...     "i don't know much about carbs__end__ "
-    ...     "__start__ I'm not sure."
-    ... )
-    >>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt")
-    >>> next_reply_ids = model.generate(**inputs)
-    >>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])
-    Bot:  they eat a lot of carbs. carbs are high in fat, protein, and fats.
-    ```
-"""
-
-BLENDERBOT_SMALL_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Indices of decoder input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are decoder input IDs?](../glossary#decoder-input-ids)
-
-            BlenderbotSmall uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If
-            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
-            be used by default.
-        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-            Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
-            1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape
-            `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you
-            can choose to directly pass an embedded representation. This is useful if you want more control over how to
-            convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
-            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
-            input (see `past_key_values`). This is useful if you want more control over how to convert
-            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
-
-            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
-            of `inputs_embeds`.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-class BlenderbotSmallEncoder(BlenderbotSmallPreTrainedModel):
-    """
-    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
-    [`BlenderbotSmallEncoderLayer`].
-
-    Args:
-        config: BlenderbotSmallConfig
-        embed_tokens (nn.Embedding): output embedding
-    """
-
-    def __init__(self, config: BlenderbotSmallConfig, embed_tokens: Optional[nn.Embedding] = None):
-        super().__init__(config)
-
-        self.dropout = config.dropout
-        self.layerdrop = config.encoder_layerdrop
-
-        embed_dim = config.d_model
-        self.padding_idx = config.pad_token_id
-        self.max_source_positions = config.max_position_embeddings
-        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
-
-        if embed_tokens is not None:
-            self.embed_tokens = embed_tokens
-        else:
-            self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
-
-        self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
-            config.max_position_embeddings,
-            embed_dim,
-        )
-        self.layers = nn.ModuleList([BlenderbotSmallEncoderLayer(config) for _ in range(config.encoder_layers)])
-        self.layernorm_embedding = nn.LayerNorm(embed_dim)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-
-                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-
-        embed_pos = self.embed_positions(input_shape)
-
-        hidden_states = inputs_embeds + embed_pos
-        hidden_states = self.layernorm_embedding(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        # expand attention_mask
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        # check if head_mask has a correct number of layers specified if desired
-        if head_mask is not None:
-            if head_mask.size()[0] != len(self.layers):
-                raise ValueError(
-                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
-                    f" {head_mask.size()[0]}."
-                )
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            to_drop = False
-            if self.training:
-                dropout_probability = torch.rand([])
-                if dropout_probability < self.layerdrop:  # skip the layer
-                    to_drop = True
-
-            if to_drop:
-                layer_outputs = (None, None)
-            else:
-                if self.gradient_checkpointing and self.training:
-
-                    def create_custom_forward(module):
-                        def custom_forward(*inputs):
-                            return module(*inputs, output_attentions)
-
-                        return custom_forward
-
-                    layer_outputs = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(encoder_layer),
-                        hidden_states,
-                        attention_mask,
-                        (head_mask[idx] if head_mask is not None else None),
-                    )
-                else:
-                    layer_outputs = encoder_layer(
-                        hidden_states,
-                        attention_mask,
-                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                        output_attentions=output_attentions,
-                    )
-
-                hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotSmallDecoderLayer`]
-
-    Args:
-        config: BlenderbotSmallConfig
-        embed_tokens (nn.Embedding): output embedding
-    """
-
-    def __init__(self, config: BlenderbotSmallConfig, embed_tokens: Optional[nn.Embedding] = None):
-        super().__init__(config)
-        self.dropout = config.dropout
-        self.layerdrop = config.decoder_layerdrop
-        self.padding_idx = config.pad_token_id
-        self.max_target_positions = config.max_position_embeddings
-        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
-
-        if embed_tokens is not None:
-            self.embed_tokens = embed_tokens
-        else:
-            self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
-
-        self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
-            config.max_position_embeddings,
-            config.d_model,
-        )
-        self.layers = nn.ModuleList([BlenderbotSmallDecoderLayer(config) for _ in range(config.decoder_layers)])
-        self.layernorm_embedding = nn.LayerNorm(config.d_model)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-                inputs_embeds.device
-            )
-            combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-            )
-
-        return combined_attention_mask
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        head_mask=None,
-        cross_attn_head_mask=None,
-        past_key_values=None,
-        inputs_embeds=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-
-                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
-                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
-                of the decoder.
-            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
-                Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
-                selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
-                cross-attention on hidden heads. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
-                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
-                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
-                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
-                all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
-                shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
-                `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
-                control over how to convert `input_ids` indices into associated vectors than the model's internal
-                embedding lookup matrix.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            input_ids = input_ids.view(-1, input_shape[-1])
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
-
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask, input_shape, inputs_embeds, past_key_values_length
-        )
-
-        # expand encoder attention mask
-        if encoder_hidden_states is not None and encoder_attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
-
-        # embed positions
-        positions = self.embed_positions(input_shape, past_key_values_length)
-
-        # BlenderbotSmall applies layer norm on hidden_states
-        inputs_embeds = self.layernorm_embedding(inputs_embeds)
-        hidden_states = inputs_embeds + positions
-
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
-        next_decoder_cache = () if use_cache else None
-
-        # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
-        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
-            if attn_mask is not None:
-                if attn_mask.size()[0] != len(self.layers):
-                    raise ValueError(
-                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
-                        f" {head_mask.size()[0]}."
-                    )
-        for idx, decoder_layer in enumerate(self.layers):
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-            if self.training:
-                dropout_probability = torch.rand([])
-                if dropout_probability < self.layerdrop:
-                    continue
-
-            past_key_value = past_key_values[idx] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, output_attentions, use_cache)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    head_mask[idx] if head_mask is not None else None,
-                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_attention_mask=encoder_attention_mask,
-                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
-                    cross_attn_layer_head_mask=(
-                        cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
-                    ),
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-                if encoder_hidden_states is not None:
-                    all_cross_attentions += (layer_outputs[2],)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    "The bare BlenderbotSmall Model outputting raw hidden-states without any specific head on top.",
-    BLENDERBOT_SMALL_START_DOCSTRING,
-)
-class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
-    _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
-
-    def __init__(self, config: BlenderbotSmallConfig):
-        super().__init__(config)
-
-        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
-        self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
-
-        self.encoder = BlenderbotSmallEncoder(config, self.shared)
-        self.decoder = BlenderbotSmallDecoder(config, self.shared)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.shared
-
-    def set_input_embeddings(self, value):
-        self.shared = value
-        self.encoder.embed_tokens = self.shared
-        self.decoder.embed_tokens = self.shared
-
-    def get_encoder(self):
-        return self.encoder
-
-    def get_decoder(self):
-        return self.decoder
-
-    @add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        decoder_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
-        r"""
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, BlenderbotSmallModel
-
-        >>> model = BlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")
-        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
-
-        >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
-        >>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt")  # Batch size 1
-        >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)
-
-        >>> last_hidden_states = outputs.last_hidden_state
-        >>> list(last_hidden_states.shape)
-        [1, 3, 512]
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if encoder_outputs is None:
-            encoder_outputs = self.encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                head_mask=head_mask,
-                inputs_embeds=inputs_embeds,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-            )
-        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
-        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
-            encoder_outputs = BaseModelOutput(
-                last_hidden_state=encoder_outputs[0],
-                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
-                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
-            )
-
-        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            encoder_hidden_states=encoder_outputs[0],
-            encoder_attention_mask=attention_mask,
-            head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=decoder_inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if not return_dict:
-            return decoder_outputs + encoder_outputs
-
-        return Seq2SeqModelOutput(
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    "The BlenderbotSmall Model with a language modeling head. Can be used for summarization.",
-    BLENDERBOT_SMALL_START_DOCSTRING,
-)
-class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
-    base_model_prefix = "model"
-    _keys_to_ignore_on_load_missing = ["final_logits_bias"]
-    _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
-
-    def __init__(self, config: BlenderbotSmallConfig):
-        super().__init__(config)
-        self.model = BlenderbotSmallModel(config)
-        self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
-        self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_encoder(self):
-        return self.model.get_encoder()
-
-    def get_decoder(self):
-        return self.model.get_decoder()
-
-    def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
-        new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
-        self._resize_final_logits_bias(new_embeddings.weight.shape[0])
-        return new_embeddings
-
-    def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
-        old_num_tokens = self.final_logits_bias.shape[-1]
-        if new_num_tokens <= old_num_tokens:
-            new_bias = self.final_logits_bias[:, :new_num_tokens]
-        else:
-            extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
-            new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
-        self.register_buffer("final_logits_bias", new_bias)
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    @add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
-    @add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        decoder_input_ids: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        decoder_head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Returns:
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if labels is not None:
-            if use_cache:
-                logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
-            use_cache = False
-            if decoder_input_ids is None and decoder_inputs_embeds is None:
-                decoder_input_ids = shift_tokens_right(
-                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
-                )
-
-        outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            decoder_input_ids=decoder_input_ids,
-            encoder_outputs=encoder_outputs,
-            decoder_attention_mask=decoder_attention_mask,
-            head_mask=head_mask,
-            decoder_head_mask=decoder_head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            decoder_inputs_embeds=decoder_inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (lm_logits,) + outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return Seq2SeqLMOutput(
-            loss=masked_lm_loss,
-            logits=lm_logits,
-            past_key_values=outputs.past_key_values,
-            decoder_hidden_states=outputs.decoder_hidden_states,
-            decoder_attentions=outputs.decoder_attentions,
-            cross_attentions=outputs.cross_attentions,
-            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
-            encoder_hidden_states=outputs.encoder_hidden_states,
-            encoder_attentions=outputs.encoder_attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        decoder_input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        head_mask=None,
-        decoder_head_mask=None,
-        cross_attn_head_mask=None,
-        use_cache=None,
-        encoder_outputs=None,
-        **kwargs,
-    ):
-        # cut decoder_input_ids if past is used
-        if past_key_values is not None:
-            decoder_input_ids = decoder_input_ids[:, -1:]
-
-        return {
-            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
-            "encoder_outputs": encoder_outputs,
-            "past_key_values": past_key_values,
-            "decoder_input_ids": decoder_input_ids,
-            "attention_mask": attention_mask,
-            "head_mask": head_mask,
-            "decoder_head_mask": decoder_head_mask,
-            "cross_attn_head_mask": cross_attn_head_mask,
-            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
-        }
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            # cached cross_attention states don't have to be reordered -> they are always the same
-            reordered_past += (
-                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
-                + layer_past[2:],
-            )
-        return reordered_past
-
-
-# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->BlenderbotSmall
-class BlenderbotSmallDecoderWrapper(BlenderbotSmallPreTrainedModel):
-    """
-    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
-    used in combination with the [`EncoderDecoderModel`] framework.
-    """
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.decoder = BlenderbotSmallDecoder(config)
-
-    def forward(self, *args, **kwargs):
-        return self.decoder(*args, **kwargs)
-
-
-# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->BlenderbotSmall, facebook/bart-base->facebook/blenderbot_small-90M
-class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        config = copy.deepcopy(config)
-        config.is_decoder = True
-        config.is_encoder_decoder = False
-        super().__init__(config)
-        self.model = BlenderbotSmallDecoderWrapper(config)
-
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.decoder.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.decoder.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model.decoder = decoder
-
-    def get_decoder(self):
-        return self.model.decoder
-
-    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        cross_attn_head_mask: Optional[torch.Tensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
-        r"""
-        Args:
-            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-
-                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-                [`PreTrainedTokenizer.__call__`] for details.
-
-                [What are input IDs?](../glossary#input-ids)
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
-                if the model is configured as a decoder.
-            encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
-                in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
-            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
-                Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
-
-                - 1 indicates the head is **not masked**,
-                - 0 indicates the head is **masked**.
-
-            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
-                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
-                tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
-
-                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
-                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
-                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
-                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, BlenderbotSmallForCausalLM
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
-        >>> model = BlenderbotSmallForCausalLM.from_pretrained(
-        ...     "facebook/blenderbot_small-90M", add_cross_attention=False
-        ... )
-        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
-        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-        >>> outputs = model(**inputs)
-
-        >>> logits = outputs.logits
-        >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
-        >>> list(logits.shape) == expected_shape
-        True
-        ```"""
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model.decoder(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            head_mask=head_mask,
-            cross_attn_head_mask=cross_attn_head_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        logits = self.lm_head(outputs[0])
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
-    ):
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_ids.shape)
-
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-        # first step, decoder_cached_states are empty
-        return {
-            "input_ids": input_ids,  # encoder_outputs is defined. input_ids not needed
-            "attention_mask": attention_mask,
-            "past_key_values": past_key_values,
-            "use_cache": use_cache,
-        }
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
-            )
-        return reordered_past
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/llama/convert_llama_weights_to_hf.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/llama/convert_llama_weights_to_hf.py
deleted file mode 100644
index acc49884ebdb290622e0df239d4c00c1b59de708..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/llama/convert_llama_weights_to_hf.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import argparse
-import gc
-import json
-import os
-import shutil
-import warnings
-
-import torch
-
-from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
-
-
-try:
-    from transformers import LlamaTokenizerFast
-except ImportError as e:
-    warnings.warn(e)
-    warnings.warn(
-        "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
-    )
-    LlamaTokenizerFast = None
-
-"""
-Sample usage:
-
-```
-python src/transformers/models/llama/convert_llama_weights_to_hf.py \
-    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path
-```
-
-Thereafter, models can be loaded via:
-
-```py
-from transformers import LlamaForCausalLM, LlamaTokenizer
-
-model = LlamaForCausalLM.from_pretrained("/output/path")
-tokenizer = LlamaTokenizer.from_pretrained("/output/path")
-```
-
-Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
-come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
-"""
-
-NUM_SHARDS = {
-    "7B": 1,
-    "7Bf": 1,
-    "13B": 2,
-    "13Bf": 2,
-    "34B": 4,
-    "30B": 4,
-    "65B": 8,
-    "70B": 8,
-    "70Bf": 8,
-}
-
-
-def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
-    return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
-
-
-def read_json(path):
-    with open(path, "r") as f:
-        return json.load(f)
-
-
-def write_json(text, path):
-    with open(path, "w") as f:
-        json.dump(text, f)
-
-
-def write_model(model_path, input_base_path, model_size, tokenizer_path=None, safe_serialization=True):
-    # for backward compatibility, before you needed the repo to be called `my_repo/model_size`
-    if not os.path.isfile(os.path.join(input_base_path, "params.json")):
-        input_base_path = os.path.join(input_base_path, model_size)
-
-    os.makedirs(model_path, exist_ok=True)
-    tmp_model_path = os.path.join(model_path, "tmp")
-    os.makedirs(tmp_model_path, exist_ok=True)
-
-    params = read_json(os.path.join(input_base_path, "params.json"))
-    num_shards = NUM_SHARDS[model_size]
-    n_layers = params["n_layers"]
-    n_heads = params["n_heads"]
-    n_heads_per_shard = n_heads // num_shards
-    dim = params["dim"]
-    dims_per_head = dim // n_heads
-    base = params.get("rope_theta", 10000.0)
-    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
-    if base > 10000.0:
-        max_position_embeddings = 16384
-    else:
-        max_position_embeddings = 2048
-
-    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
-    if tokenizer_path is not None:
-        tokenizer = tokenizer_class(tokenizer_path)
-        tokenizer.save_pretrained(model_path)
-    vocab_size = tokenizer.vocab_size if tokenizer_path is not None else 32000
-
-    if "n_kv_heads" in params:
-        num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
-        num_local_key_value_heads = n_heads_per_shard // num_key_value_heads
-        key_value_dim = dim // num_key_value_heads
-    else:  # compatibility with other checkpoints
-        num_key_value_heads = n_heads
-        num_local_key_value_heads = n_heads_per_shard
-        key_value_dim = dim
-
-    # permute for sliced rotary
-    def permute(w, n_heads=n_heads, dim1=dim, dim2=dim):
-        return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
-
-    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
-    # Load weights
-    if model_size == "7B":
-        # Not sharded
-        # (The sharded implementation would also work, but this is simpler.)
-        loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
-    else:
-        # Sharded
-        loaded = [
-            torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
-            for i in range(num_shards)
-        ]
-    param_count = 0
-    index_dict = {"weight_map": {}}
-    for layer_i in range(n_layers):
-        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
-        if model_size == "7B":
-            # Unsharded
-            state_dict = {
-                f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
-                    loaded[f"layers.{layer_i}.attention.wq.weight"]
-                ),
-                f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
-                    loaded[f"layers.{layer_i}.attention.wk.weight"]
-                ),
-                f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
-                f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
-                f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
-                f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
-                f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
-                f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"],
-                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"],
-            }
-        else:
-            # Sharded
-            # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
-            # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
-            # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
-
-            state_dict = {
-                f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
-                    f"layers.{layer_i}.attention_norm.weight"
-                ].clone(),
-                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
-                    f"layers.{layer_i}.ffn_norm.weight"
-                ].clone(),
-            }
-            state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
-                torch.cat(
-                    [
-                        loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
-                        for i in range(num_shards)
-                    ],
-                    dim=0,
-                ).reshape(dim, dim)
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
-                torch.cat(
-                    [
-                        loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
-                            num_local_key_value_heads, dims_per_head, dim
-                        )
-                        for i in range(num_shards)
-                    ],
-                    dim=0,
-                ).reshape(key_value_dim, dim),
-                num_key_value_heads,
-                key_value_dim,
-                dim,
-            )
-            state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
-                [
-                    loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
-                        num_local_key_value_heads, dims_per_head, dim
-                    )
-                    for i in range(num_shards)
-                ],
-                dim=0,
-            ).reshape(key_value_dim, dim)
-
-            state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
-            )
-            state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
-                [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
-            )
-
-        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
-        for k, v in state_dict.items():
-            index_dict["weight_map"][k] = filename
-            param_count += v.numel()
-        torch.save(state_dict, os.path.join(tmp_model_path, filename))
-
-    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
-    if model_size == "7B":
-        # Unsharded
-        state_dict = {
-            "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
-            "model.norm.weight": loaded["norm.weight"],
-            "lm_head.weight": loaded["output.weight"],
-        }
-    else:
-        state_dict = {
-            "model.norm.weight": loaded[0]["norm.weight"],
-            "model.embed_tokens.weight": torch.cat(
-                [loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1
-            ),
-            "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
-        }
-
-    for k, v in state_dict.items():
-        index_dict["weight_map"][k] = filename
-        param_count += v.numel()
-    torch.save(state_dict, os.path.join(tmp_model_path, filename))
-
-    # Write configs
-    index_dict["metadata"] = {"total_size": param_count * 2}
-    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
-    ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
-    multiple_of = params["multiple_of"] if "multiple_of" in params else 256
-    config = LlamaConfig(
-        hidden_size=dim,
-        intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
-        num_attention_heads=params["n_heads"],
-        num_hidden_layers=params["n_layers"],
-        rms_norm_eps=params["norm_eps"],
-        num_key_value_heads=num_key_value_heads,
-        vocab_size=vocab_size,
-        rope_theta=base,
-        max_position_embeddings=max_position_embeddings,
-    )
-    config.save_pretrained(tmp_model_path)
-
-    # Make space so we can load the model properly now.
-    del state_dict
-    del loaded
-    gc.collect()
-
-    print("Loading the checkpoint in a Llama model.")
-    model = LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
-    # Avoid saving this as part of the config.
-    del model.config._name_or_path
-    model.config.torch_dtype = torch.float16
-    print("Saving in the Transformers format.")
-    model.save_pretrained(model_path, safe_serialization=safe_serialization)
-    shutil.rmtree(tmp_model_path)
-
-
-def write_tokenizer(tokenizer_path, input_tokenizer_path):
-    # Initialize the tokenizer based on the `spm` model
-    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
-    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
-    tokenizer = tokenizer_class(input_tokenizer_path)
-    tokenizer.save_pretrained(tokenizer_path)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--input_dir",
-        help="Location of LLaMA weights, which contains tokenizer.model and model folders",
-    )
-    parser.add_argument(
-        "--model_size",
-        choices=["7B", "7Bf", "13B", "13Bf", "30B", "34B", "65B", "70B", "70Bf", "tokenizer_only"],
-        help="'f' models correspond to the finetuned versions, and are specific to the Llama2 official release. For more details on Llama2, checkout the original repo: https://huggingface.co/meta-llama",
-    )
-    parser.add_argument(
-        "--output_dir",
-        help="Location to write HF model and tokenizer",
-    )
-    parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
-    args = parser.parse_args()
-    spm_path = os.path.join(args.input_dir, "tokenizer.model")
-    if args.model_size != "tokenizer_only":
-        write_model(
-            model_path=args.output_dir,
-            input_base_path=args.input_dir,
-            model_size=args.model_size,
-            safe_serialization=args.safe_serialization,
-            tokenizer_path=spm_path,
-        )
-    else:
-        write_tokenizer(args.output_dir, spm_path)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/export/flatten.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/export/flatten.py
deleted file mode 100644
index f5ba4297567d650f147eebeed361e9d62fab899d..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/export/flatten.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import collections
-from dataclasses import dataclass
-from typing import Callable, List, Optional, Tuple
-import torch
-from torch import nn
-
-from detectron2.structures import Boxes, Instances, ROIMasks
-from detectron2.utils.registry import _convert_target_to_string, locate
-
-from .torchscript_patch import patch_builtin_len
-
-
-@dataclass
-class Schema:
-    """
-    A Schema defines how to flatten a possibly hierarchical object into tuple of
-    primitive objects, so it can be used as inputs/outputs of PyTorch's tracing.
-
-    PyTorch does not support tracing a function that produces rich output
-    structures (e.g. dict, Instances, Boxes). To trace such a function, we
-    flatten the rich object into tuple of tensors, and return this tuple of tensors
-    instead. Meanwhile, we also need to know how to "rebuild" the original object
-    from the flattened results, so we can evaluate the flattened results.
-    A Schema defines how to flatten an object, and while flattening it, it records
-    necessary schemas so that the object can be rebuilt using the flattened outputs.
-
-    The flattened object and the schema object is returned by ``.flatten`` classmethod.
-    Then the original object can be rebuilt with the ``__call__`` method of schema.
-
-    A Schema is a dataclass that can be serialized easily.
-    """
-
-    # inspired by FetchMapper in tensorflow/python/client/session.py
-
-    @classmethod
-    def flatten(cls, obj):
-        raise NotImplementedError
-
-    def __call__(self, values):
-        raise NotImplementedError
-
-    @staticmethod
-    def _concat(values):
-        ret = ()
-        sizes = []
-        for v in values:
-            assert isinstance(v, tuple), "Flattened results must be a tuple"
-            ret = ret + v
-            sizes.append(len(v))
-        return ret, sizes
-
-    @staticmethod
-    def _split(values, sizes):
-        if len(sizes):
-            expected_len = sum(sizes)
-            assert (
-                len(values) == expected_len
-            ), f"Values has length {len(values)} but expect length {expected_len}."
-        ret = []
-        for k in range(len(sizes)):
-            begin, end = sum(sizes[:k]), sum(sizes[: k + 1])
-            ret.append(values[begin:end])
-        return ret
-
-
-@dataclass
-class ListSchema(Schema):
-    schemas: List[Schema]  # the schemas that define how to flatten each element in the list
-    sizes: List[int]  # the flattened length of each element
-
-    def __call__(self, values):
-        values = self._split(values, self.sizes)
-        if len(values) != len(self.schemas):
-            raise ValueError(
-                f"Values has length {len(values)} but schemas " f"has length {len(self.schemas)}!"
-            )
-        values = [m(v) for m, v in zip(self.schemas, values)]
-        return list(values)
-
-    @classmethod
-    def flatten(cls, obj):
-        res = [flatten_to_tuple(k) for k in obj]
-        values, sizes = cls._concat([k[0] for k in res])
-        return values, cls([k[1] for k in res], sizes)
-
-
-@dataclass
-class TupleSchema(ListSchema):
-    def __call__(self, values):
-        return tuple(super().__call__(values))
-
-
-@dataclass
-class IdentitySchema(Schema):
-    def __call__(self, values):
-        return values[0]
-
-    @classmethod
-    def flatten(cls, obj):
-        return (obj,), cls()
-
-
-@dataclass
-class DictSchema(ListSchema):
-    keys: List[str]
-
-    def __call__(self, values):
-        values = super().__call__(values)
-        return dict(zip(self.keys, values))
-
-    @classmethod
-    def flatten(cls, obj):
-        for k in obj.keys():
-            if not isinstance(k, str):
-                raise KeyError("Only support flattening dictionaries if keys are str.")
-        keys = sorted(obj.keys())
-        values = [obj[k] for k in keys]
-        ret, schema = ListSchema.flatten(values)
-        return ret, cls(schema.schemas, schema.sizes, keys)
-
-
-@dataclass
-class InstancesSchema(DictSchema):
-    def __call__(self, values):
-        image_size, fields = values[-1], values[:-1]
-        fields = super().__call__(fields)
-        return Instances(image_size, **fields)
-
-    @classmethod
-    def flatten(cls, obj):
-        ret, schema = super().flatten(obj.get_fields())
-        size = obj.image_size
-        if not isinstance(size, torch.Tensor):
-            size = torch.tensor(size)
-        return ret + (size,), schema
-
-
-@dataclass
-class TensorWrapSchema(Schema):
-    """
-    For classes that are simple wrapper of tensors, e.g.
-    Boxes, RotatedBoxes, BitMasks
-    """
-
-    class_name: str
-
-    def __call__(self, values):
-        return locate(self.class_name)(values[0])
-
-    @classmethod
-    def flatten(cls, obj):
-        return (obj.tensor,), cls(_convert_target_to_string(type(obj)))
-
-
-# if more custom structures needed in the future, can allow
-# passing in extra schemas for custom types
-def flatten_to_tuple(obj):
-    """
-    Flatten an object so it can be used for PyTorch tracing.
-    Also returns how to rebuild the original object from the flattened outputs.
-
-    Returns:
-        res (tuple): the flattened results that can be used as tracing outputs
-        schema: an object with a ``__call__`` method such that ``schema(res) == obj``.
-             It is a pure dataclass that can be serialized.
-    """
-    schemas = [
-        ((str, bytes), IdentitySchema),
-        (list, ListSchema),
-        (tuple, TupleSchema),
-        (collections.abc.Mapping, DictSchema),
-        (Instances, InstancesSchema),
-        ((Boxes, ROIMasks), TensorWrapSchema),
-    ]
-    for klass, schema in schemas:
-        if isinstance(obj, klass):
-            F = schema
-            break
-    else:
-        F = IdentitySchema
-
-    return F.flatten(obj)
-
-
-class TracingAdapter(nn.Module):
-    """
-    A model may take rich input/output format (e.g. dict or custom classes),
-    but `torch.jit.trace` requires tuple of tensors as input/output.
-    This adapter flattens input/output format of a model so it becomes traceable.
-
-    It also records the necessary schema to rebuild model's inputs/outputs from flattened
-    inputs/outputs.
-
-    Example:
-    ::
-        outputs = model(inputs)   # inputs/outputs may be rich structure
-        adapter = TracingAdapter(model, inputs)
-
-        # can now trace the model, with adapter.flattened_inputs, or another
-        # tuple of tensors with the same length and meaning
-        traced = torch.jit.trace(adapter, adapter.flattened_inputs)
-
-        # traced model can only produce flattened outputs (tuple of tensors)
-        flattened_outputs = traced(*adapter.flattened_inputs)
-        # adapter knows the schema to convert it back (new_outputs == outputs)
-        new_outputs = adapter.outputs_schema(flattened_outputs)
-    """
-
-    flattened_inputs: Tuple[torch.Tensor] = None
-    """
-    Flattened version of inputs given to this class's constructor.
-    """
-
-    inputs_schema: Schema = None
-    """
-    Schema of the inputs given to this class's constructor.
-    """
-
-    outputs_schema: Schema = None
-    """
-    Schema of the output produced by calling the given model with inputs.
-    """
-
-    def __init__(
-        self,
-        model: nn.Module,
-        inputs,
-        inference_func: Optional[Callable] = None,
-        allow_non_tensor: bool = False,
-    ):
-        """
-        Args:
-            model: an nn.Module
-            inputs: An input argument or a tuple of input arguments used to call model.
-                After flattening, it has to only consist of tensors.
-            inference_func: a callable that takes (model, *inputs), calls the
-                model with inputs, and return outputs. By default it
-                is ``lambda model, *inputs: model(*inputs)``. Can be override
-                if you need to call the model differently.
-            allow_non_tensor: allow inputs/outputs to contain non-tensor objects.
-                This option will filter out non-tensor objects to make the
-                model traceable, but ``inputs_schema``/``outputs_schema`` cannot be
-                used anymore because inputs/outputs cannot be rebuilt from pure tensors.
-                This is useful when you're only interested in the single trace of
-                execution (e.g. for flop count), but not interested in
-                generalizing the traced graph to new inputs.
-        """
-        super().__init__()
-        if isinstance(model, (nn.parallel.distributed.DistributedDataParallel, nn.DataParallel)):
-            model = model.module
-        self.model = model
-        if not isinstance(inputs, tuple):
-            inputs = (inputs,)
-        self.inputs = inputs
-        self.allow_non_tensor = allow_non_tensor
-
-        if inference_func is None:
-            inference_func = lambda model, *inputs: model(*inputs)  # noqa
-        self.inference_func = inference_func
-
-        self.flattened_inputs, self.inputs_schema = flatten_to_tuple(inputs)
-
-        if all(isinstance(x, torch.Tensor) for x in self.flattened_inputs):
-            return
-        if self.allow_non_tensor:
-            self.flattened_inputs = tuple(
-                [x for x in self.flattened_inputs if isinstance(x, torch.Tensor)]
-            )
-            self.inputs_schema = None
-        else:
-            for input in self.flattened_inputs:
-                if not isinstance(input, torch.Tensor):
-                    raise ValueError(
-                        "Inputs for tracing must only contain tensors. "
-                        f"Got a {type(input)} instead."
-                    )
-
-    def forward(self, *args: torch.Tensor):
-        with torch.no_grad(), patch_builtin_len():
-            if self.inputs_schema is not None:
-                inputs_orig_format = self.inputs_schema(args)
-            else:
-                if len(args) != len(self.flattened_inputs) or any(
-                    x is not y for x, y in zip(args, self.flattened_inputs)
-                ):
-                    raise ValueError(
-                        "TracingAdapter does not contain valid inputs_schema."
-                        " So it cannot generalize to other inputs and must be"
-                        " traced with `.flattened_inputs`."
-                    )
-                inputs_orig_format = self.inputs
-
-            outputs = self.inference_func(self.model, *inputs_orig_format)
-            flattened_outputs, schema = flatten_to_tuple(outputs)
-
-            flattened_output_tensors = tuple(
-                [x for x in flattened_outputs if isinstance(x, torch.Tensor)]
-            )
-            if len(flattened_output_tensors) < len(flattened_outputs):
-                if self.allow_non_tensor:
-                    flattened_outputs = flattened_output_tensors
-                    self.outputs_schema = None
-                else:
-                    raise ValueError(
-                        "Model cannot be traced because some model outputs "
-                        "cannot flatten to tensors."
-                    )
-            else:  # schema is valid
-                if self.outputs_schema is None:
-                    self.outputs_schema = schema
-                else:
-                    assert self.outputs_schema == schema, (
-                        "Model should always return outputs with the same "
-                        "structure so it can be traced!"
-                    )
-            return flattened_outputs
-
-    def _create_wrapper(self, traced_model):
-        """
-        Return a function that has an input/output interface the same as the
-        original model, but it calls the given traced model under the hood.
-        """
-
-        def forward(*args):
-            flattened_inputs, _ = flatten_to_tuple(args)
-            flattened_outputs = traced_model(*flattened_inputs)
-            return self.outputs_schema(flattened_outputs)
-
-        return forward
diff --git a/spaces/yuhanbo/chat-gpt/app/components/settings.tsx b/spaces/yuhanbo/chat-gpt/app/components/settings.tsx
deleted file mode 100644
index efc1a56a8d7d768721564bc1491f601e4118ea9c..0000000000000000000000000000000000000000
--- a/spaces/yuhanbo/chat-gpt/app/components/settings.tsx
+++ /dev/null
@@ -1,461 +0,0 @@
-import { useState, useEffect, useRef, useMemo } from "react";
-
-import EmojiPicker, { Theme as EmojiTheme } from "emoji-picker-react";
-
-import styles from "./settings.module.scss";
-
-import ResetIcon from "../icons/reload.svg";
-import CloseIcon from "../icons/close.svg";
-import ClearIcon from "../icons/clear.svg";
-import EditIcon from "../icons/edit.svg";
-
-import { List, ListItem, Popover, showToast } from "./ui-lib";
-
-import { IconButton } from "./button";
-import {
-  SubmitKey,
-  useChatStore,
-  Theme,
-  ALL_MODELS,
-  useUpdateStore,
-  useAccessStore,
-  ALL_MODEL,
-} from "../store";
-import { Avatar, PromptHints } from "./home";
-
-import Locale, { AllLangs, changeLang, getLang } from "../locales";
-import { getCurrentCommitId, getEmojiUrl } from "../utils";
-import Link from "next/link";
-import { UPDATE_URL } from "../constant";
-import { SearchService, usePromptStore } from "../store/prompt";
-
-function SettingItem(props: {
-  title: string;
-  subTitle?: string;
-  children: JSX.Element;
-}) {
-  return (
-    <ListItem>
-      <div className={styles["settings-title"]}>
-        <div>{props.title}</div>
-        {props.subTitle && (
-          <div className={styles["settings-sub-title"]}>{props.subTitle}</div>
-        )}
-      </div>
-      {props.children}
-    </ListItem>
-  );
-}
-
-export function Settings(props: { closeSettings: () => void }) {
-  const [showEmojiPicker, setShowEmojiPicker] = useState(false);
-  const [config, updateConfig, resetConfig, clearAllData, clearAll] =
-    useChatStore((state) => [
-      state.config,
-      state.updateConfig,
-      state.resetConfig,
-      state.clearAllData,
-      state.clearAll,
-    ]);
-
-  const updateStore = useUpdateStore();
-  const [checkingUpdate, setCheckingUpdate] = useState(false);
-  const currentId = getCurrentCommitId();
-  const remoteId = updateStore.remoteId;
-  const hasNewVersion = currentId !== remoteId;
-
-  function checkUpdate(force = false) {
-    setCheckingUpdate(true);
-    updateStore.getLatestCommitId(force).then(() => {
-      setCheckingUpdate(false);
-    });
-  }
-
-  useEffect(() => {
-    checkUpdate();
-  }, []);
-
-  const accessStore = useAccessStore();
-  const enabledAccessControl = useMemo(
-    () => accessStore.enabledAccessControl(),
-    [],
-  );
-
-  const promptStore = usePromptStore();
-  const builtinCount = SearchService.count.builtin;
-  const customCount = promptStore.prompts.size ?? 0;
-
-  return (
-    <>
-      <div className={styles["window-header"]}>
-        <div className={styles["window-header-title"]}>
-          <div className={styles["window-header-main-title"]}>
-            {Locale.Settings.Title}
-          </div>
-          <div className={styles["window-header-sub-title"]}>
-            {Locale.Settings.SubTitle}
-          </div>
-        </div>
-        <div className={styles["window-actions"]}>
-          <div className={styles["window-action-button"]}>
-            <IconButton
-              icon={<ClearIcon />}
-              onClick={clearAllData}
-              bordered
-              title={Locale.Settings.Actions.ClearAll}
-            />
-          </div>
-          <div className={styles["window-action-button"]}>
-            <IconButton
-              icon={<ResetIcon />}
-              onClick={resetConfig}
-              bordered
-              title={Locale.Settings.Actions.ResetAll}
-            />
-          </div>
-          <div className={styles["window-action-button"]}>
-            <IconButton
-              icon={<CloseIcon />}
-              onClick={props.closeSettings}
-              bordered
-              title={Locale.Settings.Actions.Close}
-            />
-          </div>
-        </div>
-      </div>
-      <div className={styles["settings"]}>
-        <List>
-          <SettingItem title={Locale.Settings.Avatar}>
-            <Popover
-              onClose={() => setShowEmojiPicker(false)}
-              content={
-                <EmojiPicker
-                  lazyLoadEmojis
-                  theme={EmojiTheme.AUTO}
-                  getEmojiUrl={getEmojiUrl}
-                  onEmojiClick={(e) => {
-                    updateConfig((config) => (config.avatar = e.unified));
-                    setShowEmojiPicker(false);
-                  }}
-                />
-              }
-              open={showEmojiPicker}
-            >
-              <div
-                className={styles.avatar}
-                onClick={() => setShowEmojiPicker(true)}
-              >
-                <Avatar role="user" />
-              </div>
-            </Popover>
-          </SettingItem>
-
-          <SettingItem
-            title={Locale.Settings.Update.Version(currentId)}
-            subTitle={
-              checkingUpdate
-                ? Locale.Settings.Update.IsChecking
-                : hasNewVersion
-                ? Locale.Settings.Update.FoundUpdate(remoteId ?? "ERROR")
-                : Locale.Settings.Update.IsLatest
-            }
-          >
-            {checkingUpdate ? (
-              <div />
-            ) : (
-              <IconButton
-                icon={<ResetIcon></ResetIcon>}
-                text={Locale.Settings.Update.CheckUpdate}
-                onClick={() => checkUpdate(true)}
-              />
-            )}
-          </SettingItem>
-
-          <SettingItem title={Locale.Settings.SendKey}>
-            <select
-              value={config.submitKey}
-              onChange={(e) => {
-                updateConfig(
-                  (config) =>
-                    (config.submitKey = e.target.value as any as SubmitKey),
-                );
-              }}
-            >
-              {Object.values(SubmitKey).map((v) => (
-                <option value={v} key={v}>
-                  {v}
-                </option>
-              ))}
-            </select>
-          </SettingItem>
-
-          <ListItem>
-            <div className={styles["settings-title"]}>
-              {Locale.Settings.Theme}
-            </div>
-            <select
-              value={config.theme}
-              onChange={(e) => {
-                updateConfig(
-                  (config) => (config.theme = e.target.value as any as Theme),
-                );
-              }}
-            >
-              {Object.values(Theme).map((v) => (
-                <option value={v} key={v}>
-                  {v}
-                </option>
-              ))}
-            </select>
-          </ListItem>
-
-          <SettingItem title={Locale.Settings.Lang.Name}>
-            <select
-              value={getLang()}
-              onChange={(e) => {
-                changeLang(e.target.value as any);
-              }}
-            >
-              {AllLangs.map((lang) => (
-                <option value={lang} key={lang}>
-                  {Locale.Settings.Lang.Options[lang]}
-                </option>
-              ))}
-            </select>
-          </SettingItem>
-
-          <SettingItem
-            title={Locale.Settings.FontSize.Title}
-            subTitle={Locale.Settings.FontSize.SubTitle}
-          >
-            <input
-              type="range"
-              title={`${config.fontSize ?? 14}px`}
-              value={config.fontSize}
-              min="12"
-              max="18"
-              step="1"
-              onChange={(e) =>
-                updateConfig(
-                  (config) =>
-                    (config.fontSize = Number.parseInt(e.currentTarget.value)),
-                )
-              }
-            ></input>
-          </SettingItem>
-
-          <div className="no-mobile">
-            <SettingItem title={Locale.Settings.TightBorder}>
-              <input
-                type="checkbox"
-                checked={config.tightBorder}
-                onChange={(e) =>
-                  updateConfig(
-                    (config) => (config.tightBorder = e.currentTarget.checked),
-                  )
-                }
-              ></input>
-            </SettingItem>
-          </div>
-        </List>
-        <List>
-          <SettingItem
-            title={Locale.Settings.Prompt.Disable.Title}
-            subTitle={Locale.Settings.Prompt.Disable.SubTitle}
-          >
-            <input
-              type="checkbox"
-              checked={config.disablePromptHint}
-              onChange={(e) =>
-                updateConfig(
-                  (config) =>
-                    (config.disablePromptHint = e.currentTarget.checked),
-                )
-              }
-            ></input>
-          </SettingItem>
-
-          <SettingItem
-            title={Locale.Settings.Prompt.List}
-            subTitle={Locale.Settings.Prompt.ListCount(
-              builtinCount,
-              customCount,
-            )}
-          >
-            <IconButton
-              icon={<EditIcon />}
-              text={Locale.Settings.Prompt.Edit}
-              onClick={() => showToast(Locale.WIP)}
-            />
-          </SettingItem>
-        </List>
-        <List>
-          {enabledAccessControl ? (
-            <SettingItem
-              title={Locale.Settings.AccessCode.Title}
-              subTitle={Locale.Settings.AccessCode.SubTitle}
-            >
-              <input
-                value={accessStore.accessCode}
-                type="text"
-                placeholder={Locale.Settings.AccessCode.Placeholder}
-                onChange={(e) => {
-                  accessStore.updateCode(e.currentTarget.value);
-                }}
-              ></input>
-            </SettingItem>
-          ) : (
-            <></>
-          )}
-
-          <SettingItem
-            title={Locale.Settings.Token.Title}
-            subTitle={Locale.Settings.Token.SubTitle}
-          >
-            <input
-              value={accessStore.token}
-              type="text"
-              placeholder={Locale.Settings.Token.Placeholder}
-              onChange={(e) => {
-                accessStore.updateToken(e.currentTarget.value);
-              }}
-            ></input>
-          </SettingItem>
-
-          <SettingItem
-            title={Locale.Settings.HistoryCount.Title}
-            subTitle={Locale.Settings.HistoryCount.SubTitle}
-          >
-            <input
-              type="range"
-              title={config.historyMessageCount.toString()}
-              value={config.historyMessageCount}
-              min="2"
-              max="25"
-              step="2"
-              onChange={(e) =>
-                updateConfig(
-                  (config) =>
-                    (config.historyMessageCount = e.target.valueAsNumber),
-                )
-              }
-            ></input>
-          </SettingItem>
-
-          <SettingItem
-            title={Locale.Settings.CompressThreshold.Title}
-            subTitle={Locale.Settings.CompressThreshold.SubTitle}
-          >
-            <input
-              type="number"
-              min={500}
-              max={4000}
-              value={config.compressMessageLengthThreshold}
-              onChange={(e) =>
-                updateConfig(
-                  (config) =>
-                    (config.compressMessageLengthThreshold =
-                      e.currentTarget.valueAsNumber),
-                )
-              }
-            ></input>
-          </SettingItem>
-        </List>
-
-        <List>
-          <SettingItem title="会话类型">
-            <select
-              value={config.model}
-              onChange={(e) => {
-                updateConfig(
-                  (config) => (config.model = e.currentTarget.value),
-                );
-                clearAll();
-              }}
-            >
-              {ALL_MODEL.map((v) => (
-                <option value={v.name} key={v.name} disabled={!v.available}>
-                  {v.name}
-                </option>
-              ))}
-            </select>
-          </SettingItem>
-          <SettingItem title={Locale.Settings.Model}>
-            <select
-              value={config.modelConfig.model}
-              onChange={(e) => {
-                updateConfig(
-                  (config) =>
-                    (config.modelConfig.model = e.currentTarget.value),
-                );
-              }}
-              disabled={config.model != "聊天"}
-            >
-              {ALL_MODELS.map((v) => (
-                <option value={v.name} key={v.name} disabled={!v.available}>
-                  {v.name}
-                </option>
-              ))}
-            </select>
-          </SettingItem>
-          <SettingItem
-            title={Locale.Settings.Temperature.Title}
-            subTitle={Locale.Settings.Temperature.SubTitle}
-          >
-            <input
-              type="range"
-              value={config.modelConfig.temperature.toFixed(1)}
-              min="0"
-              max="1"
-              step="0.1"
-              onChange={(e) => {
-                updateConfig(
-                  (config) =>
-                    (config.modelConfig.temperature =
-                      e.currentTarget.valueAsNumber),
-                );
-              }}
-            ></input>
-          </SettingItem>
-          <SettingItem
-            title={Locale.Settings.MaxTokens.Title}
-            subTitle={Locale.Settings.MaxTokens.SubTitle}
-          >
-            <input
-              type="number"
-              min={100}
-              max={4096}
-              value={config.modelConfig.max_tokens}
-              onChange={(e) =>
-                updateConfig(
-                  (config) =>
-                    (config.modelConfig.max_tokens =
-                      e.currentTarget.valueAsNumber),
-                )
-              }
-            ></input>
-          </SettingItem>
-          <SettingItem
-            title={Locale.Settings.PresencePenlty.Title}
-            subTitle={Locale.Settings.PresencePenlty.SubTitle}
-          >
-            <input
-              type="range"
-              value={config.modelConfig.presence_penalty.toFixed(1)}
-              min="-2"
-              max="2"
-              step="0.5"
-              onChange={(e) => {
-                updateConfig(
-                  (config) =>
-                    (config.modelConfig.presence_penalty =
-                      e.currentTarget.valueAsNumber),
-                );
-              }}
-            ></input>
-          </SettingItem>
-        </List>
-      </div>
-    </>
-  );
-}
diff --git a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/onnx_export.py b/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/onnx_export.py
deleted file mode 100644
index e392523c54d8a2f264924fe9db1b15c72f2222a3..0000000000000000000000000000000000000000
--- a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/onnx_export.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import torch
-from onnxexport.model_onnx import SynthesizerTrn
-import utils
-
-def main(NetExport):
-    path = "SoVits4.0"
-    if NetExport:
-        device = torch.device("cpu")
-        hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
-        SVCVITS = SynthesizerTrn(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            **hps.model)
-        _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
-        _ = SVCVITS.eval().to(device)
-        for i in SVCVITS.parameters():
-            i.requires_grad = False
-        
-        n_frame = 10
-        hidden_channels = 256  #(Hubert's shape[2])
-        
-        test_hidden_unit = torch.rand(1, n_frame, hidden_channels)
-        test_pitch = torch.rand(1, n_frame)
-        test_mel2ph = torch.arange(0, n_frame, dtype=torch.int64)[None] # torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0)
-        test_uv = torch.ones(1, n_frame, dtype=torch.float32)
-        test_noise = torch.randn(1, 192, n_frame)
-        test_sid = torch.LongTensor([0])
-        input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
-        output_names = ["audio", ]
-        
-        torch.onnx.export(SVCVITS,
-                          (
-                              test_hidden_unit.to(device),
-                              test_pitch.to(device),
-                              test_mel2ph.to(device),
-                              test_uv.to(device),
-                              test_noise.to(device),
-                              test_sid.to(device)
-                          ),
-                          f"checkpoints/{path}/model.onnx",
-                          dynamic_axes={
-                              "c": [0, 1],
-                              "f0": [1],
-                              "mel2ph": [1],
-                              "uv": [1],
-                              "noise": [2],
-                          },
-                          do_constant_folding=False,
-                          opset_version=16,
-                          verbose=False,
-                          input_names=input_names,
-                          output_names=output_names)
-
-
-if __name__ == '__main__':
-    main(True)
diff --git a/spaces/zeno-ml/translation-report/gpt-MT/README.md b/spaces/zeno-ml/translation-report/gpt-MT/README.md
deleted file mode 100644
index 739d11e8a5ccef7404a7d722b5485cc51b26288c..0000000000000000000000000000000000000000
--- a/spaces/zeno-ml/translation-report/gpt-MT/README.md
+++ /dev/null
@@ -1,170 +0,0 @@
-<p align="center">
-  <img width="600" height="150" src="assets/MT-GPT.png">
-</p>
-<hr />
-
-## How Good Are GPT Models at Machine Translation? 
-## A Comprehensive Evaluation
-
-Paper: https://arxiv.org/abs/2302.09210
-
-## Introduction
-In this work, we present a comprehensive evaluation of GPT models for machine translation, covering various aspects such as quality of different GPT models in comparison with state-of-the-art research and commercial systems, effect of prompting strategies, robustness towards domain shifts and document-level translation, all accompanied with an extensive analysis of the differential aspects of translations produced by GPT. We experiment with 18 different translation directions involving high and low resource languages, as well as non English-centric translations, and evaluate the performance of three GPT models: ChatGPT, GPT3.5 (text-davinci-003), and text-davinci-002. We also show that hybrid approaches, which combine GPT models with other translation systems, can further enhance the translation quality.
-
-## Quick Installation
-```bash
-$ git clone https://github.com/microsoft/gpt-MT.git
-$ cd tools
-$ conda create -n gpt-mt-eval python=3.10
-$ conda activate gpt-mt-eval
-$ pip install --upgrade pip
-$ pip install -r requirements.txt
-$ git clone https://github.com/Unbabel/COMET.git
-$ cd COMET
-$ git checkout fc2f2b3 
-$ poetry install
-```
-
-## Data Shots and System Outputs
-We have released all selected shots in our experiments including the sentence-level shots (RR, QR and QS) and the document-level shots (DR and DF). These shots have been organized under [data-shots](./data-shots/).
-
-Moreover, To make reproducing all results an easy task, all system outputs have been released under [system-output](./evaluation/system-outputs/) in addition the WMT official test sets along with document-separated and domain-separated files.
-
-## Prompt Template Examples
-- zero-shot
->Translate this into 1. [target language]:
->
->[input]
->
->1.
-
-- 1-shot
->Translate this into 1. [target language]:
-> 
->[shot 1 source input]
-> 
->1. [shot 1 reference]
-> 
->Translate this into 1. [target language]:
->
->[input]
->
->1.
-
-- 5-shot
->Translate this into 1. [target language]:
-> 
->[shot 1 source input]
-> 
->1. [shot 1 reference]
-> 
->Translate this into 1. [target language]:
-> 
->[shot 2 source input]
-> 
->1. [shot 2 reference]
-> 
->....
-> 
->Translate this into 1. [target language]:
-> 
->[shot 5 source input]
-> 
->1. [shot 5 reference]
-> 
->Translate this into 1. [target language]:
->
->[input]
->
->1.
-
-
-## Reproducing Results
-To reproduce the reported results in the paper, you need to run the evaluation script [evaluate.py](./tools/evaluate.py).
-### CLI Usage:
-```bash
-$ python evaluate.py -h
-usage: evaluate.py [-h] --testset TESTSET [--docids DOCIDS] --hypotheses HYPOTHESES [HYPOTHESES ...] --directions DIRECTIONS [DIRECTIONS ...]
-                   [--comet-models COMET_MODELS [COMET_MODELS ...]] [--gpus GPUS] --metrics METRICS [METRICS ...] [--save-name SAVE_NAME]
-                   [--sliding-window SLIDING_WINDOW] [--context-length CONTEXT_LENGTH]
-
-options:
-  -h, --help            show this help message and exit
-  --testset TESTSET     A path to the test set directory containing references and sources for each language pair. Must contain
-                        {src_lang}{tgt_lang}/test.{src_lang}-{tgt_lang}.{tgt_lang} and {src_lang}{tgt_lang}/test.{src_lang}-{tgt_lang}.{src_lang}
-  --docids DOCIDS       A path to the directory containing doc-ids corresponding to testset for each language pair. Must contain
-                        {src_lang}{tgt_lang}/test.{src_lang}-{tgt_lang}.docids
-  --hypotheses HYPOTHESES [HYPOTHESES ...]
-                        A path to the model output files. must contain {src_lang}{tgt_lang}/test.{src_lang}-{tgt_lang}.{tgt_lang}
-  --directions DIRECTIONS [DIRECTIONS ...]
-                        Language directions to evaluate on e.g. "en-de de-en"
-  --comet-models COMET_MODELS [COMET_MODELS ...]
-                        A list of COMET models to use for evaluation
-  --gpus GPUS           Number of GPUs to use with COMET
-  --metrics METRICS [METRICS ...]
-                        A list of metrics to use for evaluation, options ["bleu", "comet", "doc-comet", "chrf", "doc-bleu", "doc-chrf"]
-  --save-name SAVE_NAME
-                        name of the output files/folders
-  --sliding-window SLIDING_WINDOW
-                        The stride step over document
-  --context-length CONTEXT_LENGTH
-                        The number of sentences in a single context
-```
-For example:
-- To reproduce `GPT 5-Shot QR` results in `Table3`:
-```bash
-$ cd ./tools
-$ python evaluate.py \
-    --testset ../evaluation/testset/wmt-testset \
-    --directions de-en en-de cs-en en-cs ja-en en-ja zh-en en-zh ru-en en-ru uk-en en-uk is-en en-is ha-en en-ha fr-de de-fr \
-    --metrics comet chrf bleu \
-    --comet-models wmt22-comet-da wmt22-cometkiwi-da \
-    --hypotheses ../evaluation/system-outputs/text-davinci-003/QR/5-shot
-``` 
-- To reproduce `GPT Doc ZS w=16` results in `Table5`: 
-```bash
-$ cd ./tools
-$ python evaluate.py \
-    --testset ../evaluation/testset/wmt-testset \
-    --docids ../evaluation/testset/wmt-testset-docids \
-    --directions de-en en-de \
-    --metrics comet doc-comet chrf bleu doc-bleu \
-    --comet-models wmt22-comet-da wmt22-cometkiwi-da \
-    --hypotheses ../evaluation/system-outputs/text-davinci-003-doc-level/Doc-W16/zeroshot
-``` 
-
-## Contributing
-
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
-
-When you submit a pull request, a CLA bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
-
-## Trademarks
-
-This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 
-trademarks or logos is subject to and must follow 
-[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
-Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
-Any use of third-party trademarks or logos are subject to those third-party's policies.
-
-## Citation
-
-If this work is helpful for your research, please consider citing the following BibTeX entry.
-
-```
-@article{gpt-mt-2023,
-      title={How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation}, 
-      author={Amr Hendy and Mohamed Abdelrehim and Amr Sharaf and Vikas Raunak and Mohamed Gabr and Hitokazu Matsushita and Young Jin Kim and Mohamed Afify and Hany Hassan Awadalla},
-      journal={arXiv preprint arXiv:2302.09210},
-      year={2023}
-}
-```    
-
diff --git a/spaces/zhang-wei-jian/docker/node_modules/encodeurl/README.md b/spaces/zhang-wei-jian/docker/node_modules/encodeurl/README.md
deleted file mode 100644
index 127c5a0d491b284eab066ee42ef8a1e136160101..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/encodeurl/README.md
+++ /dev/null
@@ -1,128 +0,0 @@
-# encodeurl
-
-[![NPM Version][npm-image]][npm-url]
-[![NPM Downloads][downloads-image]][downloads-url]
-[![Node.js Version][node-version-image]][node-version-url]
-[![Build Status][travis-image]][travis-url]
-[![Test Coverage][coveralls-image]][coveralls-url]
-
-Encode a URL to a percent-encoded form, excluding already-encoded sequences
-
-## Installation
-
-This is a [Node.js](https://nodejs.org/en/) module available through the
-[npm registry](https://www.npmjs.com/). Installation is done using the
-[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally):
-
-```sh
-$ npm install encodeurl
-```
-
-## API
-
-```js
-var encodeUrl = require('encodeurl')
-```
-
-### encodeUrl(url)
-
-Encode a URL to a percent-encoded form, excluding already-encoded sequences.
-
-This function will take an already-encoded URL and encode all the non-URL
-code points (as UTF-8 byte sequences). This function will not encode the
-"%" character unless it is not part of a valid sequence (`%20` will be
-left as-is, but `%foo` will be encoded as `%25foo`).
-
-This encode is meant to be "safe" and does not throw errors. It will try as
-hard as it can to properly encode the given URL, including replacing any raw,
-unpaired surrogate pairs with the Unicode replacement character prior to
-encoding.
-
-This function is _similar_ to the intrinsic function `encodeURI`, except it
-will not encode the `%` character if that is part of a valid sequence, will
-not encode `[` and `]` (for IPv6 hostnames) and will replace raw, unpaired
-surrogate pairs with the Unicode replacement character (instead of throwing).
-
-## Examples
-
-### Encode a URL containing user-controled data
-
-```js
-var encodeUrl = require('encodeurl')
-var escapeHtml = require('escape-html')
-
-http.createServer(function onRequest (req, res) {
-  // get encoded form of inbound url
-  var url = encodeUrl(req.url)
-
-  // create html message
-  var body = '<p>Location ' + escapeHtml(url) + ' not found</p>'
-
-  // send a 404
-  res.statusCode = 404
-  res.setHeader('Content-Type', 'text/html; charset=UTF-8')
-  res.setHeader('Content-Length', String(Buffer.byteLength(body, 'utf-8')))
-  res.end(body, 'utf-8')
-})
-```
-
-### Encode a URL for use in a header field
-
-```js
-var encodeUrl = require('encodeurl')
-var escapeHtml = require('escape-html')
-var url = require('url')
-
-http.createServer(function onRequest (req, res) {
-  // parse inbound url
-  var href = url.parse(req)
-
-  // set new host for redirect
-  href.host = 'localhost'
-  href.protocol = 'https:'
-  href.slashes = true
-
-  // create location header
-  var location = encodeUrl(url.format(href))
-
-  // create html message
-  var body = '<p>Redirecting to new site: ' + escapeHtml(location) + '</p>'
-
-  // send a 301
-  res.statusCode = 301
-  res.setHeader('Content-Type', 'text/html; charset=UTF-8')
-  res.setHeader('Content-Length', String(Buffer.byteLength(body, 'utf-8')))
-  res.setHeader('Location', location)
-  res.end(body, 'utf-8')
-})
-```
-
-## Testing
-
-```sh
-$ npm test
-$ npm run lint
-```
-
-## References
-
-- [RFC 3986: Uniform Resource Identifier (URI): Generic Syntax][rfc-3986]
-- [WHATWG URL Living Standard][whatwg-url]
-
-[rfc-3986]: https://tools.ietf.org/html/rfc3986
-[whatwg-url]: https://url.spec.whatwg.org/
-
-## License
-
-[MIT](LICENSE)
-
-[npm-image]: https://img.shields.io/npm/v/encodeurl.svg
-[npm-url]: https://npmjs.org/package/encodeurl
-[node-version-image]: https://img.shields.io/node/v/encodeurl.svg
-[node-version-url]: https://nodejs.org/en/download
-[travis-image]: https://img.shields.io/travis/pillarjs/encodeurl.svg
-[travis-url]: https://travis-ci.org/pillarjs/encodeurl
-[coveralls-image]: https://img.shields.io/coveralls/pillarjs/encodeurl.svg
-[coveralls-url]: https://coveralls.io/r/pillarjs/encodeurl?branch=master
-[downloads-image]: https://img.shields.io/npm/dm/encodeurl.svg
-[downloads-url]: https://npmjs.org/package/encodeurl
diff --git a/spaces/zhanghaohui/szu-gpt-academic/crazy_functions/crazy_functions_test.py b/spaces/zhanghaohui/szu-gpt-academic/crazy_functions/crazy_functions_test.py
deleted file mode 100644
index 0c623b8e027858b2579a021769bb304e34c4e373..0000000000000000000000000000000000000000
--- a/spaces/zhanghaohui/szu-gpt-academic/crazy_functions/crazy_functions_test.py
+++ /dev/null
@@ -1,231 +0,0 @@
-"""
-这是什么？
-    这个文件用于函数插件的单元测试
-    运行方法 python crazy_functions/crazy_functions_test.py
-"""
-    
-# ==============================================================================================================================
-
-def validate_path():
-    import os, sys
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-    os.chdir(root_dir_assume)
-    sys.path.append(root_dir_assume)
-validate_path() # validate path so you can run from base directory
-
-# ==============================================================================================================================
-
-from colorful import *
-from toolbox import get_conf, ChatBotWithCookies
-import contextlib
-import os
-import sys
-from functools import wraps
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
-    get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
-
-llm_kwargs = {
-    'api_key': API_KEY,
-    'llm_model': LLM_MODEL,
-    'top_p':1.0, 
-    'max_length': None,
-    'temperature':1.0,
-}
-plugin_kwargs = { }
-chatbot = ChatBotWithCookies(llm_kwargs)
-history = []
-system_prompt = "Serve me as a writing and programming assistant."
-web_port = 1024
-
-# ==============================================================================================================================
-
-def silence_stdout(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        _original_stdout = sys.stdout
-        sys.stdout = open(os.devnull, 'w')
-        for q in func(*args, **kwargs):
-            sys.stdout = _original_stdout
-            yield q
-            sys.stdout = open(os.devnull, 'w')
-        sys.stdout.close()
-        sys.stdout = _original_stdout
-    return wrapper
-
-class CLI_Printer():
-    def __init__(self) -> None:
-        self.pre_buf = ""
-
-    def print(self, buf):
-        bufp = ""
-        for index, chat in enumerate(buf):
-            a, b = chat
-            bufp += sprint亮靛('[Me]:' + a) + '\n'
-            bufp += '[GPT]:' + b
-            if index < len(buf)-1: 
-                bufp += '\n'
-
-        if self.pre_buf!="" and bufp.startswith(self.pre_buf):
-            print(bufp[len(self.pre_buf):], end='')
-        else:
-            print('\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n'+bufp, end='')
-        self.pre_buf = bufp
-        return
-    
-cli_printer = CLI_Printer()
-# ==============================================================================================================================
-def test_解析一个Python项目():
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    txt = "crazy_functions/test_project/python/dqn"
-    for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_解析一个Cpp项目():
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    txt = "crazy_functions/test_project/cpp/cppipc"
-    for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Latex英文润色():
-    from crazy_functions.Latex全文润色 import Latex英文润色
-    txt = "crazy_functions/test_project/latex/attention"
-    for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_Markdown中译英():
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
-    txt = "README.md"
-    for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_批量翻译PDF文档():
-    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_谷歌检索小助手():
-    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
-    txt = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG="
-    for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_总结word文档():
-    from crazy_functions.总结word文档 import 总结word文档
-    txt = "crazy_functions/test_project/pdf_and_word"
-    for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_下载arxiv论文并翻译摘要():
-    from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-    txt = "1812.10695"
-    for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-def test_联网回答问题():
-    from crazy_functions.联网的ChatGPT import 连接网络回答问题
-    # txt = "谁是应急食品？"
-    # >>        '根据以上搜索结果可以得知，应急食品是“原神”游戏中的角色派蒙的外号。'
-    # txt = "道路千万条，安全第一条。后面两句是？"
-    # >>        '行车不规范，亲人两行泪。'
-    # txt = "You should have gone for the head. What does that mean?"
-    # >>        The phrase "You should have gone for the head" is a quote from the Marvel movies, Avengers: Infinity War and Avengers: Endgame. It was spoken by the character Thanos in Infinity War and by Thor in Endgame.
-    txt = "AutoGPT是什么？"
-    for cookies, cb, hist, msg in 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): 
-        print("当前问答：", cb[-1][-1].replace("\n"," "))
-    for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])
-
-def test_解析ipynb文件():
-    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
-    txt = "crazy_functions/test_samples"
-    for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-
-def test_数学动画生成manim():
-    from crazy_functions.数学动画生成manim import 动画生成
-    txt = "A ball split into 2, and then split into 4, and finally split into 8."
-    for cookies, cb, hist, msg in 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        print(cb)
-
-
-
-def test_Markdown多语言():
-    from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
-    txt = "README.md"
-    history = []
-    for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]:
-        plugin_kwargs = {"advanced_arg": lang}
-        for cookies, cb, hist, msg in Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-            print(cb)
-
-def test_Langchain知识库():
-    from crazy_functions.Langchain知识库 import 知识库问答
-    txt = "./"
-    chatbot = ChatBotWithCookies(llm_kwargs)
-    for cookies, cb, hist, msg in silence_stdout(知识库问答)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        cli_printer.print(cb)   #  print(cb)
-    
-    chatbot = ChatBotWithCookies(cookies)
-    from crazy_functions.Langchain知识库 import 读取知识库作答
-    txt = "What is the installation method？"
-    for cookies, cb, hist, msg in silence_stdout(读取知识库作答)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        cli_printer.print(cb)   #  print(cb)
-
-def test_Langchain知识库读取():
-    from crazy_functions.Langchain知识库 import 读取知识库作答
-    txt = "远程云服务器部署？"
-    for cookies, cb, hist, msg in silence_stdout(读取知识库作答)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        cli_printer.print(cb)   #  print(cb)
-
-def test_Latex():
-    from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比, Latex翻译中文并重新编译PDF
-
-    # txt = r"https://arxiv.org/abs/1706.03762"
-    # txt = r"https://arxiv.org/abs/1902.03185"
-    # txt = r"https://arxiv.org/abs/2305.18290"
-    # txt = r"https://arxiv.org/abs/2305.17608"
-    # txt = r"https://arxiv.org/abs/2211.16068"                     #  ACE
-    # txt = r"C:\Users\x\arxiv_cache\2211.16068\workfolder"  #  ACE
-    # txt = r"https://arxiv.org/abs/2002.09253"
-    # txt = r"https://arxiv.org/abs/2306.07831"
-    # txt = r"https://arxiv.org/abs/2212.10156"
-    # txt = r"https://arxiv.org/abs/2211.11559"
-    # txt = r"https://arxiv.org/abs/2303.08774"
-    txt = r"https://arxiv.org/abs/2303.12712"
-    # txt = r"C:\Users\fuqingxu\arxiv_cache\2303.12712\workfolder"
-    
-
-    for cookies, cb, hist, msg in (Latex翻译中文并重新编译PDF)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-        cli_printer.print(cb)   #  print(cb)
-
-
-
-    # txt = "2302.02948.tar"
-    # print(txt)
-    # main_tex, work_folder = Latex预处理(txt)
-    # print('main tex:', main_tex)
-    # res = 编译Latex(main_tex, work_folder)
-    # # for cookies, cb, hist, msg in silence_stdout(编译Latex)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    #     cli_printer.print(cb)   #  print(cb)
-
-
-
-# test_解析一个Python项目()
-# test_Latex英文润色()
-# test_Markdown中译英()
-# test_批量翻译PDF文档()
-# test_谷歌检索小助手()
-# test_总结word文档()
-# test_下载arxiv论文并翻译摘要()
-# test_解析一个Cpp项目()
-# test_联网回答问题()
-# test_解析ipynb文件()
-# test_数学动画生成manim()
-# test_Langchain知识库()
-# test_Langchain知识库读取()
-if __name__ == "__main__":
-    test_Latex()
-    input("程序完成，回车退出。")
-    print("退出。")
\ No newline at end of file
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/components/user-menu.tsx b/spaces/zhaoys/wfms-kuiwenc/src/components/user-menu.tsx
deleted file mode 100644
index d112c60cc636176c2845fe3ccee47a1c2622411b..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/components/user-menu.tsx
+++ /dev/null
@@ -1,74 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-import { SVG } from './ui/svg'
-
-export function UserMenu() {
-  const [host, setHost] = useState('')
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  useEffect(() => {
-    setHost(location.host)
-  }, [])
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-  return (
-    <div className="flex items-center justify-between">
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <Button className="pl-0" variant="secondary">
-            <div className="flex items-center justify-center text-xs font-medium uppercase rounded-full select-none h-7 w-7 shrink-0 bg-muted/50 text-muted-foreground">
-              <SVG alt="settings" src={SettingIcon} width={20} />
-            </div>
-            <span className="ml-2">设置</span>
-          </Button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent sideOffset={8} align="start" className="w-[180px] bg-background">
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="settings"'
-            }
-            className="cursor-pointer"
-          >
-            设置用户
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="voice"'
-            }
-            className="cursor-pointer"
-          >
-            语音设置
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="prompts"'
-            }
-            className="cursor-pointer"
-          >
-            提示词管理
-          </DropdownMenuItem>
-          
-        </DropdownMenuContent>
-      </DropdownMenu>
-    </div>
-  )
-}
diff --git a/spaces/zwv9/webui-cpu/README.md b/spaces/zwv9/webui-cpu/README.md
deleted file mode 100644
index f91194f7d6b1b5b58905387d8e785f781e3e0049..0000000000000000000000000000000000000000
--- a/spaces/zwv9/webui-cpu/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Webui
-emoji: 🚧
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-duplicated_from: noteh65036/webui
----